# DataQuest NASCON'23 — Data

来源：https://www.kaggle.com/competitions/dataquest-nascon23/data

采集时间：2026-10-20T04:56:16Z


These blood glucose records were obtained from two sources: an automatic electronic recording device and paper records. The automatic device had an internal clock to timestamp events, whereas the paper records only provided ”logical time” slots (breakfast, lunch, dinner, bedtime). For paper records, fixed times were assigned to breakfast (08:00), lunch (12:00), dinner (18:00), and bedtime (22:00). Thus paper records have fictitious uniform recording
times whereas electronic records have more realistic time stamps.

1 Patient records consist of four fields per record. Each field is separated by a tab and each record is separated by a newline.

### File Names and format:
1. Date in MM-DD-YYYY format
2. Time in XX:YY format
3. Code
4. Value

### The Code field is deciphered as follows:
33 = Regular insulin dose
34 = NPH insulin dose
35 = UltraLente insulin dose
48 = Unspecified blood glucose measurement
57 = Unspecified blood glucose measurement
58 = Pre-breakfast blood glucose measurement
59 = Post-breakfast blood glucose measurement
60 = Pre-lunch blood glucose measurement
61 = Post-lunch blood glucose measurement
62 = Pre-supper blood glucose measurement
63 = Post-supper blood glucose measurement
64 = Pre-snack blood glucose measurement
65 = Hypoglycemic symptoms
66 = Typical meal ingestion
67 = More-than-usual meal ingestion
68 = Less-than-usual meal ingestion
69 = Typical exercise activity
70 = More-than-usual exercise activity
71 = Less-than-usual exercise activity
72 = Unspecified special event

**NOTE: ”Ignore all the codes that are not available in the description.”

In this competition, we challenge participants to generate new features from a dataset containing diabetes patient records obtained from two sources: an automatic electronic recording device and paper records. The primary objective of this competition is to improve predictive models for diabetes-related outcomes by creating new features that capture meaningful information from the available data.

The dataset consists of four fields per record, including the date, time, code, and value. The code field represents various diabetes-related events, such as blood glucose measurements, insulin doses, and exercise activities. Participants are expected to create at least 30 features that capture additional information about these events and their relationship with diabetes related outcomes.