Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk

Published July 26, 2024 | Version v1

Dataset Open

1. Université de Sherbrooke
2. Cambridge Memorial Hospital
3. McGill University Health Centre
4. Research Institute of the McGill University Health Centre, Montreal, Canada
5. Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, Canada

Paper Title: Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk

Paper: https://doi.org/10.1007/s13755-024-00332-4 (full-text view-only version: https://rdcu.be/eccmN)

GitHub Link: https://github.com/MEDomics-UdeS/POYM

Description:

This dataset accompanies Laribi et al. (2024) and contains synthetic data generated using the AVATAR method in partnership with Octopize.

Files:

dataset.csv: This file contains 248,485 rows and 247 columns, representing 248,485 synthetic visits from 123,646 synthetic patients. Detailed descriptions of each column can be found in Laribi et al. (2024). To preserve patient's privacy, we did not save admission and discharge dates. Consequently, it is not possible to split the dataset temporally as done with the original dataset or to identify admissions with same-day discharge.

Comparison of synthetic and original data: https://doi.org/10.21203/rs.3.rs-5363467/v1

Files

Name	Size	Download all
dataset.csv md5:a88c63da84b1438a720499df8a04a3ce	133.1 MB	Preview Download