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  "fetched_at": "2026-10-19T08:13:13Z",
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      "content": "## Dataset Overview\n\nThis competition provides a large-scale tabular clinical dataset comprising **1,000,000 emergency \ndepartment encounters** from 25 cities across all NUTS-2 statistical regions of Turkiye, spanning \nJanuary 2019 through December 2024. The dataset is designed to support research-grade predictive \nmodeling for two binary clinical outcome targets.\n\n| Property | Value |\n|----------|-------|\n| Total Encounters | 1,000,000 |\n| Training Set | 700,000 encounters |\n| Test Set | 200,000 encounters |\n| Supplemental Set | 100,000 encounters |\n| Feature Columns | 135 (train) / 132 (test) |\n| Time Period | January 2019 through December 2024 |\n| Geographic Coverage | 25 cities, all NUTS-2 regions of Turkiye |\n| Primary Target Prevalence | ~41% |\n| Secondary Target Prevalence | ~14% |\n\n---\n\n## Files\n\n### train.csv\nThe primary training dataset. Contains **700,000 rows** and **135 columns** including both \ntarget variables (`adverse_outcome` and `readmission_30d`). Use this file to build and \nvalidate your models.\n\n### test.csv\nThe competition test dataset. Contains **200,000 rows** and **132 columns**. The two target \ncolumns have been removed. You must predict both targets for all 200,000 encounters and \nsubmit them in the format described on the Evaluation page.\n\n### sample_submission.csv\nA correctly formatted submission file containing all test `encounter_id` values with \nplaceholder predictions of 0 for both targets. Use this as the structural template for \nyour submission file. Do not alter the `encounter_id` column.\n\n### supplemental_data.csv\nAn additional **100,000 labeled encounters** using the same schema as `train.csv`, \nincluding both target variables. These records are not part of the train/test split. \nParticipants may use this file freely for any purpose, including additional model training, \npseudo-labeling, calibration, or external validation.\n\n### hospital_reference.csv\nInfrastructure metadata for the 25 cities represented in the dataset. Join to the main \ndata on the `city` column.\n\n| Column | Description |\n|--------|-------------|\n| city | City name |\n| nuts2_region | NUTS-2 region name |\n| latitude | City latitude (decimal degrees) |\n| longitude | City longitude (decimal degrees) |\n| population_estimate | City population estimate |\n| num_state_hospitals | Number of state hospitals |\n| num_university_hospitals | Number of university hospitals |\n| num_private_hospitals | Number of private hospitals |\n| num_city_hospitals | Number of city hospitals |\n| num_training_research_hospitals | Number of training and research hospitals |\n| total_icu_beds | Total ICU beds across the city |\n| total_ed_beds | Total ED beds across the city |\n| helicopter_ems_available | 1 if helicopter EMS is available |\n| stroke_center | 1 if a certified stroke center is present |\n| trauma_center_level | Level I, Level II, or Level III |\n\n### icd10_reference.csv\nReference table for the primary ICD-10 diagnosis codes used in the dataset. Join to the \nmain data on the `primary_icd10_code` column.\n\n| Column | Description |\n|--------|-------------|\n| icd10_code | ICD-10 code |\n| diagnosis_description | Full text diagnosis name |\n| icd10_chapter | Chapter letter |\n| chapter_name | Chapter description |\n| typical_los_days | Typical inpatient length of stay (days) |\n| typical_mortality_pct | Typical in-hospital mortality rate (%) |\n\n---\n\n## Feature Groups\n\n### Temporal Features\nCovers arrival year, month, day, hour, day of week, season, night shift flag, weekend flag, \npandemic period label, and COVID-19 period binary flag.\n\n### Geographic and Institutional Features\nCity, NUTS-2 region, latitude, longitude, urban/rural classification, hospital type, \nannual ED volume, and bed count category.\n\n### Patient Demographics\nAge, age group, gender, pediatric flag, geriatric flag, height, weight, and BMI.\n\n### Socioeconomic Features\nInsurance type (SGK, Private, Green Card, Uninsured, Emekli Sandigi) and a composite \nsocioeconomic status index scored 1 to 10.\n\n### Triage and Presentation\nArrival mode, Manchester Triage System level (1 to 5), triage label, and chief complaint \n(30 categories).\n\n### Vital Signs\nHeart rate, systolic and diastolic blood pressure, mean arterial pressure, pulse pressure, \nrespiratory rate, temperature, SpO2, Glasgow Coma Scale total, and NRS pain score.\n\n### Laboratory Values\n29 laboratory columns covering complete metabolic panel, complete blood count, coagulation \nstudies, cardiac biomarkers, inflammatory markers, and organ function tests. See the \nmissingness section below for columns with elevated missing rates.\n\n### Comorbidities\n18 binary comorbidity columns (prefix: `comorbidity_`) plus the computed Charlson \nComorbidity Index score.\n\n### Medications\n10 binary medication class columns (prefix: `med_`) plus a polypharmacy indicator for \npatients on 5 or more drug classes.\n\n### ED Procedures and Interventions\n14 binary procedure columns (prefix: `proc_`), IV fluid volume in mL, specialist \nconsultation flag, and consulting specialty.\n\n### Operational Times\nTriage-to-physician time (minutes) and total ED length of stay (minutes).\n\n### COVID-19 Features\nCOVID-19 tested flag, positive result flag, and vaccination status flag.\n\n### Clinical Severity Scores\nComputed approximations of MEWS (Modified Early Warning Score), NEWS2 (National Early \nWarning Score 2), and SOFA (Sequential Organ Failure Assessment).\n\n---\n\n## Missing Data\n\nSeven laboratory columns contain **informative missingness (MNAR: Missing Not At Random)**. \nThese tests are selectively ordered for higher-acuity patients, meaning that the fact a \ntest was not ordered is itself clinically meaningful. Treating these columns as Missing \nCompletely At Random (MCAR) will underperform relative to approaches that model missingness \nexplicitly.\n\n**Recommended approach:** Before imputation, create a binary indicator feature \n`{column}_missing` for each of the following columns.\n\n| Column | Approximate Missing Rate | Clinical Reason for Selective Ordering |\n|--------|--------------------------|----------------------------------------|\n| troponin_i_ngml | ~35% | Ordered for suspected cardiac events |\n| bnp_pgl | ~40% | Ordered for suspected heart failure |\n| d_dimer_mgfl | ~42% | Ordered for suspected thromboembolism |\n| lactate_mmoll | ~25% | Ordered for suspected shock or sepsis |\n| lipase_ul | ~38% | Ordered for suspected pancreatitis |\n| procalcitonin_ngl | ~30% | Ordered for suspected bacterial infection |\n| albumin_gdl | ~28% | Ordered for nutritional or hepatic assessment |\n\n---\n\n## Important Notes for Participants\n\n**On the `disposition` column:** This column is present in `train.csv` and \n`supplemental_data.csv`. It reflects the ED discharge decision (Discharged Home, \nAdmitted to Ward, Admitted to ICU, Transferred Out, Left AMA, Died in ED) and is a \ndirect post-decision proxy for both target variables. **Using this column as a predictor \nconstitutes data leakage and will result in disqualification.** It is provided solely \nfor exploratory data analysis.\n\n**On derived columns:** The columns `map_mmhg`, `pulse_pressure_mmhg`, `hematocrit_pct`, \nand `anion_gap_meql` are mathematically derivable from other features in the dataset. \nParticipants may use them as-is or re-engineer them.\n\n**On the COVID-19 period:** Encounters from 2020 and 2021 exhibit structured distribution \nshift in testing patterns, presentation volumes, and disposition rates relative to \nnon-pandemic years. The `covid_period_flag` column marks these encounters explicitly. \nParticipants may find it valuable to model this period separately or include it as an \ninteraction term.\n\n**On temporal validation:** A time-based train/validation split (e.g., training on \n2019-2022 and validating on 2023-2024) is recommended as a more realistic evaluation \nstrategy than random k-fold cross-validation, as it better simulates prospective \ndeployment conditions.",
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      "content": "## Background\n\nEmergency departments in Turkiye process tens of millions of patient encounters annually across a geographically and demographically diverse national healthcare network. The system spans university hospitals, state hospitals, city hospitals, training and research hospitals, and private institutions distributed across 25 metropolitan and provincial centers covering all NUTS-2 statistical regions, from Istanbul and Izmir in the west to Diyarbakir, Erzurum, and Van in the east.\n\nEarly identification of patients at elevated risk of critical deterioration at the moment of ED presentation is one of the most clinically impactful unsolved problems in applied health informatics. Validated clinical scoring systems such as MEWS, NEWS2, and SOFA provide structured physiological summaries but are designed as threshold tools rather than probabilistic classifiers. Machine learning approaches offer the ability to integrate heterogeneous data types, model nonlinear interactions, and incorporate institutional and temporal context that rule-based scores ignore.\n\n---\n\n## Dataset\n\nThe dataset comprises **1,000,000 emergency department encounters** recorded between January 2019 and December 2024. Each encounter is characterized by **135 features** spanning:\n\n- Patient demographics and anthropometrics\n- Vital signs at initial triage assessment\n- 29 laboratory values with clinically realistic missing data patterns\n- 18 binary comorbidity indicators and computed Charlson Comorbidity Index\n- 10 binary medication class indicators\n- 14 binary ED procedure and intervention indicators\n- Institutional features: hospital type, annual volume, bed category\n- Geographic features: city, NUTS-2 region, urban/rural classification\n- Temporal features: year, hour, season, night shift, weekend, COVID-19 period flag\n- Clinical severity scores: MEWS, NEWS2, SOFA approximations\n- Socioeconomic features: insurance type, SES index\n- COVID-19 specific features: tested, positive, vaccinated status\n\nThe dataset is anchored in the clinical and epidemiological characteristics specific to Turkiye, incorporating SGK (Social Security Institution) insurance coding, nationally standardized Manchester Triage System levels, and regional hospital infrastructure variation across all NUTS-2 regions.\n\n---\n\n## Missing Data\n\nSeven laboratory columns contain informative missingness (Missing Not At Random, MNAR) at rates of 25% to 42%. These tests are selectively ordered for higher-acuity patients, meaning the missingness indicator itself carries predictive signal. Participants are encouraged to engineer binary missingness indicator features prior to imputation.\n\n| Column | Approximate Missing Rate |\n|--------|--------------------------|\n| troponin\\_i\\_ngml | 35% |\n| bnp\\_pgl | 40% |\n| d\\_dimer\\_mgfl | 42% |\n| lactate\\_mmoll | 25% |\n| lipase\\_ul | 38% |\n| procalcitonin\\_ngl | 30% |\n| albumin\\_gdl | 28% |\n\n---\n\n## Target Construction\n\nThe `adverse_outcome` target was generated via a logistic regression model encoding established clinical risk relationships. The strongest predictors include triage level 1 (log-odds +1.2), GCS below 9 (+0.8), lactate above 4 mmol/L (+0.6), SBP below 90 mmHg (+0.5), and age as a continuous predictor (+0.05 per year above 40).\n\n---\n\n## Feature Engineering Directions\n\nThe following engineered features are demonstrated in the starter notebook and are provided as starting points:\n\n$$\\text{Shock Index} = \\frac{\\text{Heart Rate (bpm)}}{\\text{Systolic BP (mmHg)}}$$\n\n$$\\text{Modified Shock Index} = \\frac{\\text{Heart Rate (bpm)}}{\\text{MAP (mmHg)}}$$\n\n$$\\text{Anion Gap} = \\text{Na}^+ - (\\text{Cl}^- + \\text{HCO}_3^-)$$\n\n$$\\text{eGFR} \\approx \\frac{120}{\\text{Creatinine} + 0.1}$$\n\nAdditional high-value interaction terms include `age x charlson_comorbidity_index`, `sofa_score_approx x triage_level`, and `is_night_shift x is_weekend`.\n\n---\n\n## Dataset Construction Methodology\n\nThe dataset was generated using a mechanistic simulation framework (seed = 42) calibrated against published epidemiological parameters. City-level sampling is proportional to population across 25 cities. Hospital type and infrastructure features vary systematically by city size and region. The COVID-19 period (2020 to 2021) introduces structured distribution shift in testing patterns, presentation characteristics, and disposition rates. \n\n---\n\n## Files\n\n| File | Rows | Columns | Description |\n|------|------|---------|-------------|\n| train.csv | 700,000 | 135 | Training encounters with both target variables |\n| test.csv | 200,000 | 132 | Test encounters with targets withheld |\n| sample\\_submission.csv | 200,000 | 3 | Submission format template |\n| supplemental\\_data.csv | 100,000 | 135 | Additional labeled data for training or validation |\n| hospital\\_reference.csv | 25 | 13 | Hospital infrastructure metadata by city |\n| icd10\\_reference.csv | 21 | 7 | ICD-10 diagnostic code reference table |\n| setup\\_solution.ipynb | — | — | Official starter notebook with baseline models |",
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      "content": "## Metric\n\nSubmissions are evaluated using **Macro-Averaged Area Under the Receiver Operating Characteristic Curve (AUC-ROC)**, computed independently for each binary target and averaged with equal weight.\n\n$$\\text{Score} = \\frac{\\text{AUC}(\\hat{y}\\_{\\text{adverse\\_outcome}},\\ y\\_{\\text{adverse\\_outcome}}) + \\text{AUC}(\\hat{y}\\_{\\text{readmission\\_30d}},\\ y\\_{\\text{readmission\\_30d}})}{2}$$\n\nwhere \\(\\hat{y}\\) denotes predicted probabilities in \\([0, 1]\\) and \\(y\\) denotes the binary ground truth labels. AUC is computed using the trapezoidal rule as implemented in `sklearn.metrics.roc_auc_score`.\n\n| Property | Value |\n|----------|-------|\n| Range | 0.0 to 1.0 |\n| Direction | Higher is better |\n| Random classifier baseline | 0.5000 |\n| Perfect classifier | 1.0000 |\n\n---\n\n## Baseline Reference Scores\n\n| Model | AUC adverse\\_outcome | AUC readmission\\_30d | Macro AUC |\n|-------|--------------------|--------------------|-----------|\n| Constant prediction | 0.5000 | 0.5000 | 0.5000 |\n| Logistic Regression (vital signs only) | 0.8251 | 0.6842 | 0.7547 |\n| LightGBM (all features, 5-fold CV) | 0.9123 | 0.7891 | 0.8507 |\n\nScores are computed via 5-fold stratified cross-validation on `train.csv` as reported in `setup_solution.ipynb`.\n\n---\n\n## Submission File\n\nFor each `encounter_id` in the test set, you must predict a probability for `adverse_outcome` and a probability for `readmission_30d`. Both values must be continuous floats in \\([0, 1]\\). The file must contain a header and have the following format:\n```apache\nencounter_id,adverse_outcome,readmission_30d\nTR-ED-00700001,0.34218,0.09841\nTR-ED-00700002,0.72193,0.23407\nTR-ED-00700003,0.08552,0.05123\n```\n\nThe submission file must contain exactly 200,000 rows. All `encounter_id` values must match the test set exactly with no duplicates or omissions. NaN, Inf, or out-of-range values will cause submission rejection.\n\n---\n\n## Leaderboard\n\nThe public leaderboard is evaluated on a randomly selected **50% of the test encounters** and is updated after each submission. The private leaderboard is evaluated on the remaining **50%** and is revealed after the Final Submission Deadline. Participants must select up to **2 Final Submissions** for private leaderboard scoring. If no selection is made, the system automatically selects the 2 submissions with the highest public leaderboard scores.\n\n---\n\n## Evaluation Code\n```python\nfrom sklearn.metrics import roc_auc_score\nimport numpy as np\n\ndef competition_score(y_true_df, y_pred_df):\n    targets = [\"adverse_outcome\", \"readmission_30d\"]\n    merged = y_true_df.merge(y_pred_df, on=\"encounter_id\", suffixes=(\"_true\", \"_pred\"))\n    auc_scores = []\n    for col in targets:\n        auc = roc_auc_score(merged[f\"{col}_true\"], merged[f\"{col}_pred\"])\n        auc_scores.append(auc)\n        print(f\"AUC {col}: {auc:.5f}\")\n    macro_auc = np.mean(auc_scores)\n    print(f\"Macro AUC: {macro_auc:.5f}\")\n    return macro_auc\n```",
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      "name": "rules",
      "content": "## ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.\n\n**[See Section 3.18 for defined terms](#18-terms)**\n\n*The Competition named below is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. To enter the Competition, you must agree to these Official Competition Rules, which incorporate by reference the provisions and content of the Competition Website and any Specific Competition Rules herein (collectively, the \"Rules\"). Please read these Rules carefully before entry to ensure you understand and agree. You further agree that Submission in the Competition constitutes agreement to these Rules. You may not submit to the Competition and are not eligible to receive the prizes associated with this Competition unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Sponsor with respect to the Competition. Your competition Submissions must conform to the requirements stated on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes, if any, will be awarded to Participants with the best scores, based on the merits of the data science models submitted. See below for the complete Competition Rules.*\n\n**You cannot sign up to Kaggle from multiple accounts and therefore you cannot enter or submit from multiple accounts.**\n\n---\n\n### 1. COMPETITION-SPECIFIC TERMS\n\n#### 1. COMPETITION TITLE\nAI in Emergency Medicine - Turkiye (AI-EMT)\n\n#### 2. COMPETITION SPONSOR\nInformatiqs Community Organization\nOrganized by: Kutluk Atalay (Community President), Ozan Mohurcu (Community Vice President), and Olaf Yunus Laitinen Imanov (Lead Competition Organizer)\n\n#### 3. COMPETITION SPONSOR ADDRESS\nInformatiqs Community Organization\nContact via Kaggle Competition Discussion Forum or the official Informatiqs community communication channels.\n\n#### 4. COMPETITION WEBSITE\nhttps://www.kaggle.com/competitions/ai-emergency-medicine-turkiye\n\n#### 5. TOTAL PRIZES AVAILABLE\nThis is a non-monetary, community-recognition competition. No cash prizes are awarded.\n\nThe following non-monetary prizes are available:\n\n- First Place: Official Informatiqs Community Champion Certificate, permanent recognition on the Informatiqs community leaderboard, and featured solution write-up publication in Informatiqs community channels.\n- Second Place: Official Informatiqs Community Excellence Certificate and recognition on the community leaderboard.\n- Third Place: Official Informatiqs Community Merit Certificate and recognition on the community leaderboard.\n- All participants who submit at least one valid scored submission will receive an Official Informatiqs Participation Certificate.\n\n#### 6. WINNER LICENSE TYPE\nOpen Source. Winners must license their winning Submission and the source code used to generate the Submission under an Open Source Initiative-approved license (see [www.opensource.org](http://www.opensource.org)) that in no event limits commercial use of such code or model containing or depending on such code.\n\n#### 7. DATA ACCESS AND USE\nYou may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any Participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.\n\nThe Competition Data is entirely synthetic and does not contain or derive from real patient records or personally identifiable clinical information. It was generated under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Full license terms are available at: https://creativecommons.org/licenses/by/4.0/\n\n---\n\n### 2. COMPETITION-SPECIFIC RULES\n\nIn addition to the provisions of the General Competition Rules below, you understand and agree to these Competition-Specific Rules required by the Competition Sponsor:\n\n#### 1. TEAM LIMITS\n\na. The maximum Team size is four (4).\n\nb. Team mergers are allowed and can be performed by the Team leader. In order to merge, the combined Team must have a total Submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of Submissions per day multiplied by the number of days the competition has been running. The Team Merger Deadline is five (5) days before the Final Submission Deadline.\n\n#### 2. SUBMISSION LIMITS\n\na. You may submit a maximum of five (5) Submissions per day.\n\nb. You may select up to two (2) Final Submissions for judging on the private leaderboard. If no selection is made prior to the Final Submission Deadline, the system will automatically select the two (2) Submissions with the highest public leaderboard scores.\n\n#### 3. COMPETITION TIMELINE\n\na. Competition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition's Overview > Timeline page.\n\nb. The private leaderboard will be released no later than seven (7) days after the Final Submission Deadline.\n\n#### 4. COMPETITION DATA\n\na. Data Access and Use.\n\nYou may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any Participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.\n\nThe Competition Data is subject to the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Full terms are available at: https://creativecommons.org/licenses/by/4.0/\n\nb. Data Security.\n\nYou agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.\n\nc. Data Nature and Limitations.\n\nThe Competition Data is entirely synthetic. It was generated using a mechanistic simulation framework calibrated against published epidemiological parameters. It does not represent real patients, real hospitals, or real clinical outcomes in Turkiye or any other jurisdiction. No warranty is made regarding the clinical applicability of any model developed using this dataset. Models developed in this competition must not be used for clinical decision-making without independent validation on real-world data.\n\nd. Prohibited Use.\n\nThe `disposition` column present in `train.csv` and `supplemental_data.csv` encodes a post-decision clinical variable (ED discharge disposition) that constitutes a direct proxy for the target variables. Use of this column as a predictor feature in any submitted model is prohibited and constitutes data leakage. Any submission found to have used this feature will be disqualified.\n\n#### 5. WINNER LICENSE\n\na. Under Section 2.8 (Winners Obligations) of the General Rules below, you hereby grant and will grant the Competition Sponsor the following license with respect to your Submission if you are a Competition winner:\n\nYou hereby license and will license your winning Submission and the source code used to generate the Submission under an Open Source Initiative-approved license (see [www.opensource.org](http://www.opensource.org)) that in no event limits commercial use of such code or model containing or depending on such code.\n\nFor generally commercially available software that you used to generate your Submission that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, you do not need to grant the license in the preceding Section for that software.\n\nIn the event that input data or pretrained models with an incompatible license are used to generate your winning solution, you do not need to grant an open source license in the preceding Section for that data and/or model(s).\n\nb. Winners may be required to provide a detailed written description of how the winning Submission was generated, to the Competition Sponsor's specifications, as outlined in Section 2.8 (Winners Obligations). This description must include: model architecture and rationale, preprocessing and feature engineering pipeline, loss function and optimization details, training hyperparameters and computational environment, and a link to a public code repository with complete and reproducible instructions sufficient to regenerate the submitted predictions from the provided data.\n\n#### 6. EXTERNAL DATA AND TOOLS\n\na. You may use data other than the Competition Data (\"External Data\") to develop and test your Submissions. However, you will ensure the External Data is either publicly available and equally accessible to use by all Participants of the Competition for purposes of the competition at no cost to the other Participants, or satisfies the Reasonableness criteria as outlined in Section 2.6.b below. The ability to use External Data under this Section does not limit your other obligations under these Competition Rules, including but not limited to Section 2.8 (Winners Obligations).\n\nAll External Data sources used must be disclosed in the final solution write-up submitted by winners.\n\nb. The use of external data and models is acceptable unless specifically prohibited by the Host. Because of the potential costs or restrictions associated with obtaining rights to use external data or certain software and associated tools, their use must be reasonably accessible to all and of minimal cost. The Host will employ an assessment of whether or not the following criteria can exclude the use of the particular LLM, dataset, or tool:\n\n1. Are Participants being excluded from a competition because of the excessive costs for access to certain LLMs, external data, or tools that might be used by other Participants. The Host will assess the excessive cost concern by applying a Reasonableness Standard. The Reasonableness Standard will be determined and applied by the Host in light of things like cost thresholds and accessibility.\n\n2. By way of example only, a small subscription charge to use additional elements of a large language model such as Gemini Advanced are acceptable if meeting the Reasonableness Standard of Sec. 8.2. Purchasing a license to use a proprietary dataset that exceeds the cost of a prize in the competition would not be considered reasonable. Given that this is a non-monetary prize competition, the use of any proprietary dataset or tool requiring a paid commercial license is not considered reasonable and is therefore prohibited.\n\nc. Automated Machine Learning Tools (AMLT).\n\nIndividual Participants and Teams may use automated machine learning tools (AMLT) (e.g., Google AutoML, H2O Driverless AI, etc.) to create a Submission, provided that the Participant or Team ensures that they have an appropriate license to the AMLT such that they are able to comply with the Competition Rules.\n\nd. Pre-trained Models.\n\nParticipants may use publicly available pre-trained models (e.g., models from Hugging Face, PyTorch Hub, or similar public repositories) subject to the Reasonableness Standard in Section 2.6.b above. All pre-trained models used must be disclosed in the final solution write-up.\n\n#### 7. ELIGIBILITY\n\na. This competition is open exclusively to members of the Informatiqs community who join via the official competition invitation link.\n\nb. Members of the organizing team, specifically Kutluk Atalay, Ozan Mohurcu, and Olaf Yunus Laitinen Imanov, are ineligible to compete, submit, or receive prizes or certificates in the competitive standings.\n\nc. Unless otherwise stated in the Competition-Specific Rules above or prohibited by internal policies of the Competition Entities, employees, interns, contractors, officers and directors of Competition Entities may enter and participate in the Competition, but are not eligible to win any Prizes. \"Competition Entities\" means the Competition Sponsor, Kaggle Inc., and their respective parent companies, subsidiaries and affiliates. If you are such a Participant from a Competition Entity, you are subject to all applicable internal policies of your employer with respect to your participation.\n\nd. Participants must have a Kaggle account in good standing. Accounts created solely for the purpose of this competition or with a history of rule violations may be disqualified at the organizers' discretion.\n\n#### 8. WINNERS OBLIGATIONS\n\na. As a condition to being awarded a Prize, a Prize winner must fulfill the following obligations:\n\n1. Deliver to the Competition Sponsor the final model's software code as used to generate the winning Submission and associated documentation. The delivered software code must follow standard documentation guidelines, must be capable of generating the winning Submission, and must contain a description of resources required to build and/or run the executable code successfully. Delivered software code must include training code, inference code, and a description of the required computational environment (hardware specifications, software versions, random seeds).\n\n   To the extent that the final model's software code includes generally commercially available software that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, then instead of delivering the code for that software to the Competition Sponsor, you must identify that software, method for procuring it, and any parameters or other information necessary to replicate the winning Submission.\n\n   Individual Participants and Teams who create a Submission using an AMLT may win a Prize. However, the potential winner's Submission must still meet the requirements of these Rules, including but not limited to Section 2.5 (Winners License), Section 2.8 (Winners Obligations), and Section 3.14 (Warranty, Indemnity, and Release).\n\n2. Grant to the Competition Sponsor the license to the winning Submission stated in the Competition Specific Rules above, and represent that you have the unrestricted right to grant that license.\n\n3. Publish a solution write-up in the Kaggle competition discussion forum within fourteen (14) days of the private leaderboard release. The write-up must describe the methodology at a level of detail sufficient for reproducibility, include all external data and tools used, and include a link to a public code repository.\n\n4. Sign and return all Prize acceptance documents as may be required by the Competition Sponsor or Kaggle, including without limitation: eligibility certifications and licenses and releases required under the Rules. Note: as this competition offers no cash prizes, U.S. tax forms are not required.\n\n#### 9. GOVERNING LAW\n\na. Unless otherwise provided in the Competition Specific Rules above, all claims arising out of or relating to these Rules will be governed by California law, excluding its conflict of laws rules, and will be litigated exclusively in the Federal or State courts of Santa Clara County, California, USA. The parties consent to personal jurisdiction in those courts. If any provision of these Rules is held to be invalid or unenforceable, all remaining provisions of the Rules will remain in full force and effect.\n\nb. Notwithstanding Section 2.9.a above, matters pertaining exclusively to the issuance of community certificates, community recognition, and participation acknowledgment by the Informatiqs organization are governed solely by the internal policies of the Informatiqs community organization and are not subject to judicial arbitration.\n\n---\n\n### 3. GENERAL COMPETITION RULES\n\n#### 1. BINDING AGREEMENT\nTo enter the Competition, you must agree to these Rules. If you do not agree to these Rules, do not enter the Competition. When you enter the Competition (including registering on the Competition Website), you are agreeing to these Rules and forming a binding legal agreement with the Competition Sponsor. You also agree to these Rules by entering a Submission into the Competition.\n\n#### 2. COMPETITION PERIOD\nThe Competition will run according to the timeline stated on the Competition Website Overview page. All Submissions must be made during the Competition Period. Submissions made after the Final Submission Deadline will not be eligible for consideration.\n\n#### 3. ELIGIBILITY REQUIREMENTS\nParticipants must meet all eligibility requirements as stated in Section 1.7 above. A \"Participant\" is an individual who enters the Competition. Participants may not use multiple Kaggle accounts to enter the Competition. Any violation of this requirement will result in disqualification.\n\n#### 4. TEAMS\nUnless otherwise stated in the Competition-Specific Rules, the maximum Team size is four (4). Participants may form Teams and compete collectively. A Participant may not be a member of more than one Team at any time. A Participant wishing to leave a Team may do so, but may not subsequently join another Team after the Team Merger Deadline.\n\n#### 5. SUBMISSIONS\nEach Submission must comply with all requirements specified on the Evaluation page of the Competition Website. Submissions must be uploaded through the official Competition submission interface. Submissions are scored against the solution file using the evaluation metric described on the Competition Website. The Competition uses a public/private leaderboard split: the public leaderboard is computed on 50% of the test data; the private leaderboard is computed on the remaining 50% and is revealed after the Final Submission Deadline.\n\n#### 6. EXTERNAL DATA\nAs specified in Section 2.6 above.\n\n#### 7. INTERNET AND ONLINE CONDUCT\nParticipants agree to engage in the Competition and on Kaggle discussion forums in a respectful and professional manner consistent with the Informatiqs community code of conduct and Kaggle's community guidelines. Harassment, discrimination, or abusive conduct directed toward any participant or organizer will result in immediate disqualification.\n\n#### 8. INTELLECTUAL PROPERTY\nYour Submissions remain your intellectual property. By submitting, you grant the Competition Sponsor a license as specified in Section 2.5 (Winner License) if you are a winner. For non-winning Submissions, no license is granted except as necessary to operate the Competition (i.e., to score submissions and display leaderboard results).\n\n#### 9. PRIVACY\nKaggle's privacy policy applies to information collected through the Competition Website. By entering the Competition, you consent to the collection and use of your information as described in Kaggle's privacy policy. If the organizer has enabled the option to collect email addresses, you consent to being contacted by the Competition Sponsor for competition-related purposes only.\n\n#### 10. WARRANTY, INDEMNITY, AND RELEASE\nBy entering the Competition, you warrant that:\n\na. Your Submission is your own original work, or you have the rights to use and submit it under these Rules.\n\nb. Your Submission does not infringe any third party intellectual property rights, privacy rights, or other legal rights.\n\nc. You agree to indemnify and hold harmless the Competition Entities from any claims arising from your Submission or your participation in the Competition.\n\nd. You release the Competition Entities from any liability in connection with the Competition, including but not limited to any claim related to scores, rankings, prizes, or the data provided.\n\n#### 11. DISQUALIFICATION\nCompetition Entities reserve the right to disqualify any Participant who violates these Rules, engages in data leakage, uses prohibited features, submits plagiarized solutions, or otherwise acts in bad faith. Disqualified Participants forfeit all prizes and recognition.\n\n#### 12. MODIFICATIONS\nCompetition Entities reserve the right to modify these Rules at any time, with notice provided via the Competition discussion forum. Continued participation after such notice constitutes acceptance of the modified Rules.\n\n#### 13. NO OBLIGATION TO AWARD\nIf, in the Competition Sponsor's sole judgment, an insufficient number of eligible and qualified entries are received, the Competition Sponsor reserves the right to forgo awarding prizes or recognition.\n\n#### 14. USE OF NAME AND LIKENESS\nBy participating, you grant the Competition Sponsor the right to use your Kaggle username and any submitted solution write-up for the purposes of announcing winners and promoting the Competition within Informatiqs community channels.\n\n#### 15. ENTIRE AGREEMENT\nThese Rules constitute the entire agreement between you and the Competition Sponsor with respect to the Competition and supersede all prior or contemporaneous oral or written agreements, if any.\n\n#### 16. SEVERABILITY\nIf any provision of these Rules is found to be unenforceable, that provision will be modified to the minimum extent necessary to make it enforceable, and the remaining provisions will remain in full force and effect.\n\n#### 17. RELATIONSHIP OF PARTIES\nNothing in these Rules creates a partnership, joint venture, agency, franchise, or employment relationship between you and the Competition Sponsor. You do not have authority to make or accept any offers or representations on behalf of the Competition Sponsor.\n\n#### 18. TERMS\n\nFor the purposes of these Rules, the following terms have the following meanings:\n\n| Term | Definition |\n|------|------------|\n| Competition | The AI in Emergency Medicine - Turkiye (AI-EMT) competition hosted on Kaggle |\n| Competition Data | The datasets provided by the Competition Sponsor: train.csv, test.csv, sample_submission.csv, supplemental_data.csv, hospital_reference.csv, icd10_reference.csv |\n| Competition Entities | The Competition Sponsor (Informatiqs), Kaggle Inc., and their respective parent companies, subsidiaries, and affiliates |\n| Competition Period | The period from the Start Date to and including the Final Submission Deadline as stated on the Competition Website |\n| Competition Sponsor | Informatiqs Community Organization |\n| Competition Website | https://www.kaggle.com/competitions/ai-emergency-medicine-turkiye |\n| Final Submission Deadline | The date and time by which all Submissions must be uploaded and Final Submissions must be selected |\n| Participant | An eligible individual who has registered for the Competition via the Competition Website |\n| Prize | Non-monetary community recognition certificates as described in Section 1.5 |\n| Submission | A properly formatted CSV file uploaded to the Competition Website containing predicted probabilities for the two target variables across all test encounters |\n| Team | One or more Participants who have merged their accounts to compete collectively, subject to the maximum Team size of four (4) |\n| Team Merger Deadline | Five (5) days before the Final Submission Deadline |\n| External Data | Any data used in generating a Submission that is not provided as part of the Competition Data |\n| AMLT | Automated Machine Learning Tool |",
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      "name": "abstract",
      "content": "AI in Emergency Medicine - Turkiye (AI-EMT) is a community competition organized by Informatiqs. The goal is to predict two clinically critical outcomes for emergency department encounters across Turkiye using a large-scale tabular clinical dataset of 1,000,000 records spanning 2019 through 2024.\n\n**Primary Target:** adverse_outcome - composite of ICU admission or in-hospital death\n\n**Secondary Target:** readmission_30d - unplanned ED re-presentation within 30 calendar days\n\n**Evaluation Metric:** Macro-Averaged AUC-ROC across both targets\n\n**Dataset Size:** 1,000,000 encounters | 135 features | 25 cities across all NUTS-2 regions of Turkiye\n\n---\n\n## Competition Organizers\n\nThis competition is organized by the **Informatiqs** community.\n\n| Role | Name |\n|------|------|\n| Community President | Kutluk Atalay |\n| Community Vice President | Ozan Mohurcu |\n| Lead Competition Organizer | Olaf Yunus Laitinen Imanov |\n\n---\n\n## Timeline\n\n| Milestone | Date |\n|-----------|------|\n| Competition Start | Set via launch checklist |\n| Team Merger Deadline | 5 days before close |\n| Final Submission Deadline | See competition header |\n| Private Leaderboard Release | Up to 7 days after close |\n\n---\n\n## Quick Start\n\nDownload the data, run the official starter notebook `setup_solution.ipynb`, and submit `submission.csv`. The starter notebook provides a working LightGBM baseline with 5-fold cross-validation achieving a macro AUC of approximately 0.85 on the training set.",
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      "name": "foundational-rules",
      "content": "The following Kaggle Competition Foundational Rules (“ Foundational Rules ”) apply to every competition regardless of whether the Sponsor creates competition-specific rules. Any competition-specific rules provided by the Sponsor are in addition to these rules, and in the case of any conflict or inconsistency, these Foundational Rules control and nullify contrary competition-specific rules.\n###GENERAL COMPETITION RULES - BINDING AGREEMENT\n####1. ELIGIBILITY\na. To be eligible to enter the Competition, you must be:</h5>\n1. a registered account holder at Kaggle.com; </h6>\n2. the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Sponsor and appropriate parental/guardian consents have been obtained by Competition Sponsor); </h6>\n3. not a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, or North Korea; and</h6>\n4. not a person or representative of an entity under U.S. export controls or sanctions (see: [https://www.treasury.gov/resourcecenter/sanctions/Programs/Pages/Programs.aspx][1]).</h6>\n\nb. Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Host reserves the right to forego or award alternative Prizes where needed to comply with local laws. If a winner is located in a country where prizes cannot be awarded, then they are not eligible to receive a prize.</h5>\n\nc. If you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent or where you are an employee. If you are acting within the scope of your employment, or as an agent of another party, you warrant that such party or your employer has full knowledge of your actions and has consented thereto, including your potential receipt of a Prize. You further warrant that your actions do not violate your employer's or entity's policies and procedures.</h5>   \n\nd. The Competition Sponsor reserves the right to verify eligibility and to adjudicate on any dispute at any time. If you provide any false information relating to the Competition concerning your identity, residency, mailing address, telephone number, email address, ownership of right, or information required for entering the Competition, you may be immediately disqualified from the Competition.</h5>\n\n####2. SPONSOR AND HOSTING PLATFORM\n\na. The Competition is sponsored by Competition Sponsor named above. The Competition is hosted on behalf of Competition Sponsor by Kaggle Inc. (\"Kaggle\"). Kaggle is an independent contractor of Competition Sponsor, and is not a party to this or any agreement between you and Competition Sponsor. You understand that Kaggle has no responsibility with respect to selecting the potential Competition winner(s) or awarding any Prizes. Kaggle will perform certain administrative functions relating to hosting the Competition, and you agree to abide by the provisions relating to Kaggle under these Rules. As a Kaggle.com account holder and user of the Kaggle competition platform, remember you have accepted and are subject to the Kaggle Terms of Service at [www.kaggle.com/terms][2] in addition to these Rules.</h5>\n\n####3. COMPETITION PERIOD\na. For the purposes of Prizes, the Competition will run from the Start Date and time to the Final Submission Deadline (such duration the “Competition Period”). The Competition Timeline is subject to change, and Competition Sponsor may introduce additional hurdle deadlines during the Competition Period. Any updated or additional deadlines will be publicized on the Competition Website. It is your responsibility to check the Competition Website regularly to stay informed of any deadline changes. YOU ARE RESPONSIBLE FOR DETERMINING THE CORRESPONDING TIME ZONE IN YOUR LOCATION.</h5>\n\n####4. COMPETITION ENTRY\na. NO PURCHASE NECESSARY TO ENTER OR WIN. To enter the Competition, you must register on the Competition Website prior to the Entry Deadline, and follow the instructions for developing and entering your Submission through the Competition Website. Your Submissions must be made in the manner and format, and in compliance with all other requirements, stated on the Competition Website (the \"Requirements\"). Submissions must be received before any Submission deadlines stated on the Competition Website. Submissions not received by the stated deadlines will not be eligible to receive a Prize.</h5>\nb. Submissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.</h5>\nc. If the Competition is a multi-stage competition with temporally separate training and/or test data, one or more valid Submissions may be required during each Competition stage in the manner described on the Competition Website in order for the Submissions to be Prize eligible.</h5>\nd. Submissions are void if they are in whole or part illegible, incomplete, damaged, altered, counterfeit, obtained through fraud, or late. Competition Sponsor reserves the right to disqualify any entrant who does not follow these Rules, including making a Submission that does not meet the Requirements. </h5>\n\n####5. INDIVIDUALS AND TEAMS\na. Individual Account. You may make Submissions only under one, unique Kaggle.com account. You will be disqualified if you make Submissions through more than one Kaggle account, or attempt to falsify an account to act as your proxy. You may submit up to the maximum number of Submissions per day as specified on the Competition Website. </h5>\nb. Teams. If permitted under the Competition Website guidelines, multiple individuals may collaborate as a Team; however, you may join or form only one Team. Each Team member must be a single individual with a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message sent to your Kaggle account. Team membership may not exceed the Maximum Team Size stated on the Competition Website.</h5>\nc. Team Merger. Teams may request to merge via the Competition Website. Team mergers may be allowed provided that: (i) the combined Team does not exceed the Maximum Team Size; (ii) the number of Submissions made by the merging Teams does not exceed the number of Submissions permissible for one Team at the date of the merger request; (iii) the merger is completed before the earlier of: any merger deadline or the Competition deadline; and (iv) the proposed combined Team otherwise meets all the requirements of these Rules. </h5>\nd. Private Sharing. No private sharing outside of Teams. Privately sharing code or data outside of Teams is not permitted. It's okay to share code if made available to all Participants on the forums.</h5>\n\n####6. SUBMISSION CODE REQUIREMENTS\na. Private Code Sharing. Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.</h5>\nb. Public Code Sharing. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or notebooks associated specifically with the Competition for the benefit of all competitors. By so sharing, you are deemed to have licensed the shared code under an Open Source Initiative-approved license (see [www.opensource.org][3]) that in no event limits commercial use of such Competition Code or model containing or depending on such Competition Code.</h5>\nc. Use of Open Source. Unless otherwise stated in the Specific Competition Rules above, if open source code is used in the model to generate the Submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see [www.opensource.org][4]) that in no event limits commercial use of such code or model containing or depending on such code.</h5>\n\n####7. DETERMINING WINNERS\na. Each Submission will be scored and ranked by the evaluation metric stated on the Competition Website. During the Competition Period, the current ranking will be visible on the Competition Website's Public Leaderboard. The potential winner(s) are determined solely by the leaderboard ranking on the Private Leaderboard, subject to compliance with these Rules. The Public Leaderboard will be based on the public test set and the Private Leaderboard will be based on the private test set.</h5>\nb. In the event of a tie, the Submission that was entered first to the Competition will be the winner. In the event a potential winner is disqualified for any reason, the Submission that received the next highest score rank will be chosen as the potential winner.</h5>\n\n####8. NOTIFICATION OF WINNERS & DISQUALIFICATION\na. The potential winner(s) will be notified by email.</h5> \nb. If a potential winner (i) does not respond to the notification attempt within one (1) week from the first notification attempt or (ii) notifies Kaggle within one week after the Final Submission Deadline that the potential winner does not want to be nominated as a winner or does not want to receive a Prize, then, in each case (i) and (ii) such potential winner will not receive any Prize, and an alternate potential winner will be selected from among all eligible entries received based on the Competition’s judging criteria.</h5>\nc. In case (i) and (ii) above Kaggle may disqualify the Participant.  However, in case (ii) above, if requested by Kaggle, such potential winner may provide code and documentation to verify the Participant’s compliance with these Rules. If the potential winner provides code and documentation to the satisfaction of Kaggle, the Participant will not be disqualified pursuant to this paragraph.</h5>\nd. Competition Sponsor reserves the right to disqualify any Participant from the Competition if the Competition Sponsor reasonably believes that the Participant has attempted to undermine the legitimate operation of the Competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other Participants, Competition Sponsor or Kaggle.</h5>\ne. A disqualified Participant may be removed from the Competition leaderboard, at Kaggle's sole discretion. If a Participant is removed from the Competition Leaderboard, additional winning features associated with the Kaggle competition platform, for example Kaggle points or medals, may also not be awarded.</h5>\nf. The final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.</h5>\n\n####9. PRIZES\na. Prize(s) are as described on the Competition Website and are only available for winning during the time period described on the Competition Website. The odds of winning any Prize depends on the number of eligible Submissions received during the Competition Period and the skill of the Participants. </h5>\nb. All Prizes are subject to Competition Sponsor's review and verification of the Participant’s eligibility and compliance with these Rules, and the compliance of the winning Submissions with the Submissions Requirements. In the event that the Submission demonstrates non-compliance with these Competition Rules, Competition Sponsor may at its discretion take either of the following actions: (i) disqualify the Submission(s); or (ii) require the potential winner to remediate within one week after notice all issues identified in the Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).</h5>\nc. A potential winner may decline to be nominated as a Competition winner in accordance with Section 3.8.</h5>\nd. Potential winners must return all required Prize acceptance documents within two (2) weeks following notification of such required documents, or such potential winner will be deemed to have forfeited the prize and another potential winner will be selected. Prize(s) will be awarded within approximately thirty (30) days after receipt by Competition Sponsor or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed. </h5>\ne. You are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2.7 and Section 3.1 above.</h5>\nf. If a Team wins a monetary Prize, the Prize money will be allocated in even shares between the eligible Team members, unless the Team unanimously opts for a different Prize split and notifies Kaggle before Prizes are issued.</h5>\n\n####10. TAXES\na. ALL TAXES IMPOSED ON PRIZES ARE THE SOLE RESPONSIBILITY OF THE WINNERS. Payments to potential winners are subject to the express requirement that they submit all documentation requested by Competition Sponsor or Kaggle for compliance with applicable state, federal, local and foreign (including provincial) tax reporting and withholding requirements. Prizes will be net of any taxes that Competition Sponsor is required by law to withhold. If a potential winner fails to provide any required documentation or comply with applicable laws, the Prize may be forfeited and Competition Sponsor may select an alternative potential winner. Any winners who are U.S. residents will receive an IRS Form-1099 in the amount of their Prize.</h5>\n\n####11. GENERAL CONDITIONS\na. All federal, state, provincial and local laws and regulations apply.</h5>\n\n####12. PUBLICITY\na. You agree that Competition Sponsor, Kaggle and its affiliates may use your name and likeness for advertising and promotional purposes without additional compensation, unless prohibited by law.</h5>\n\n####13. PRIVACY\na. You acknowledge and agree that Competition Sponsor and Kaggle may collect, store, share and otherwise use personally identifiable information provided by you during the Kaggle account registration process and the Competition, including but not limited to, name, mailing address, phone number, and email address (“Personal Information”). Kaggle acts as an independent controller with regard to its collection, storage, sharing, and other use of this Personal Information, and will use this Personal Information in accordance with its Privacy Policy <[www.kaggle.com/privacy][6]>, including for administering the Competition. As a Kaggle.com account holder, you have the right to request access to, review, rectification, portability or deletion of any personal data held by Kaggle about you by logging into your account and/or contacting Kaggle Support at <[www.kaggle.com/contact][7]>.</h5>\nb. As part of Competition Sponsor performing this contract between you and the Competition Sponsor, Kaggle will transfer your Personal Information to Competition Sponsor, which acts as an independent controller with regard to this Personal Information. As a controller of such Personal Information, Competition Sponsor agrees to comply with all U.S. and foreign data protection obligations with regard to your Personal Information. Kaggle will transfer your Personal Information to Competition Sponsor in the country specified in the Competition Sponsor Address listed above, which may be a country outside the country of your residence. Such country may not have privacy laws and regulations similar to those of the country of your residence.</h5>\n\n####14. WARRANTY, INDEMNITY AND RELEASE \na. You warrant that your Submission is your own original work and, as such, you are the sole and exclusive owner and rights holder of the Submission, and you have the right to make the Submission and grant all required licenses.  You agree not to make any Submission that: (i) infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; or (ii) otherwise violates any applicable U.S. or foreign state or federal law.</h5>\nb. To the maximum extent permitted by law, you indemnify and agree to keep indemnified Competition Entities at all times from and against any liability, claims, demands, losses, damages, costs and expenses resulting from any of your acts, defaults or omissions and/or a breach of any warranty set forth herein. To the maximum extent permitted by law, you agree to defend, indemnify and hold harmless the Competition Entities from and against any and all claims, actions, suits or proceedings, as well as any and all losses, liabilities, damages, costs and expenses (including reasonable attorneys fees) arising out of or accruing from: (a) your Submission or other material uploaded or otherwise provided by you that infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; (b) any misrepresentation made by you in connection with the Competition; (c) any non-compliance by you with these Rules or any applicable U.S. or foreign state or federal law; (d) claims brought by persons or entities other than the parties to these Rules arising from or related to your involvement with the Competition; and (e) your acceptance, possession, misuse or use of any Prize, or your participation in the Competition and any Competition-related activity.</h5>\nc. You hereby release Competition Entities from any liability associated with: (a) any malfunction or other problem with the Competition Website; (b) any error in the collection, processing, or retention of any Submission; or (c) any typographical or other error in the printing, offering or announcement of any Prize or winners.</h5>\n\n####15. INTERNET\na. Competition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a Participant’s ability to participate.</h5>\n\n####16. RIGHT TO CANCEL, MODIFY OR DISQUALIFY\na. If for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Sponsor reserves the right to cancel, terminate, modify or suspend the Competition. Competition Sponsor further reserves the right to disqualify any Participant who tampers with the submission process or any other part of the Competition or Competition Website.  Any attempt by a Participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Sponsor and Kaggle each reserves the right to seek damages from any such Participant to the fullest extent of the applicable law.</h5>\n\n####17. NOT AN OFFER OR CONTRACT OF EMPLOYMENT\na. Under no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Sponsor or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Sponsor or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.</h5>\n\n####18. DEFINITIONS\na. \"Competition Data\" are the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to Participants. </h5>\nb. An “Entry” is when a Participant has joined, signed up, or accepted the rules of a competition. Entry is required to make a Submission to a competition.</h5>\nc. A “Final Submission” is the Submission selected by the user, or automatically selected by Kaggle in the event not selected by the user, that is/are used for final placement on the competition leaderboard.</h5>\nd. A “Participant” or “Participant User” is an individual who participates in a competition by entering the competition and making a Submission.</h5>\ne. The “Private Leaderboard” is a ranked display of Participants’ Submission scores against the private test set. The Private Leaderboard determines the final standing in the competition.</h5>\nf. The “Public Leaderboard” is a ranked display of Participants’ Submission scores against a representative sample of the test data. This leaderboard is visible throughout the competition.</h5>\ng. A “Sponsor” is responsible for hosting the competition, which includes but is not limited to providing the data for the competition, determining winners, and enforcing competition rules.</h5>\nh. A “Submission” is anything provided by the Participant to the Sponsor to be evaluated for competition purposes and determine leaderboard position. A Submission may be made as a model, notebook, prediction file, or other format as determined by the Sponsor.</h5>\ni. A “Team” is one or more Participants participating together in a Kaggle competition, by officially merging together as a Team within the competition platform.</h5>\n\n  [1]: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx\n  [2]: http://www.kaggle.com/terms\n  [3]: http://www.opensource.org\n  [4]: http://www.opensource.org\n  [5]: https://www.kaggle.com/WinningModelDocumentationGuidelines\n  [6]: http://www.kaggle.com/privacy\n  [7]: http://www.kaggle.com/contact\n",
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