Diabetes

This diabetes dataset is from AIM '94

Dataset Characteristics

Multivariate, Time-Series

Subject Area

Life Science

Associated Tasks

-

Feature Type

Categorical, Integer

# Instances

-

# Features

20

Dataset Information

Additional Information

Diabetes patient 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. Diabetes files 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

Has Missing Values?

No

Variable Information

Diabetes files 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

Papers Citing this Dataset

Uncoupled Regression from Pairwise Comparison Data

By Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama. 2019

Published in ArXiv.

Scalable Fair Clustering

By Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner. 2019

Published in ArXiv.

SAFE ML: Surrogate Assisted Feature Extraction for Model Learning

By Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon, Przemyslaw Biecek. 2019

Published in ArXiv.

Rank-one Convexification for Sparse Regression

By Alper Atamturk, Andres Gomez. 2019

Published in ArXiv.

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Keywords

health

Creators

Michael Kahn

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