Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Electronic Health Records

Published: 31 August 2023| Version 4 | DOI: 10.17632/tkwzysr4y6.4
Contributor:
Robert Chen

Description

Contains resources needed to train, test, and analyze performance of gradient boosting models used to predict venous thromboembolism (VTE) from electronic health record (EHR) data. Prediction.ipynb: Contains code needed to run trained models. Small, Medium, and Large.xlsx: Excel templates to correctly format data for prediction generation. Models.zip: Contains trained models. Note that this is 0.4 GB once unzipped. Analysis.ipynb: Contains code used to train the models. Dependencies: Python 3.10.9; Pandas 1.5.1; LightGBM 3.3.2.

Files

Institutions

  • Icahn School of Medicine at Mount Sinai

Categories

Machine Learning

Licence