RECOVER - PASC

This study is part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, which seeks to understand, treat, and prevent the post-acute sequelae of SARS-CoV-2 infection (PASC). For more information on RECOVER, visit https://recovercovid.org/.

Related Works

1. Understanding Post-Acute Sequelae of SARS-CoV-2 Infection through Data-Driven Analysis with Longitudinal Electronic Health Records: Findings from the RECOVER Initiative
2. Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative

Test Systems

Windows 10 PC, 16 GB memory, 500 GB hard disk, 6 GB NVIDIA GeForce GTX 1060 GPU Linux Ubuntu 18.04.2 LTS server, 62 GB memory, 500 GB hard disk, 11 GB GeForce RTX 2080 Ti GPU, and 16 CPU cores. Python environment install and activation Notes: recommend using tmux at the terminal to run all the following commands git clone https://github.com/calvin-zcx/pasc_phenotype.git cd pasc_phenotype/ conda env create -f environment.yml
conda activate pasc

Code Structure

1. preprocess- EHR preorpcessing
2. iptw - High-throughput screening of PASC by machine learning-based propensity-score reweighting method
3. prediction - PASC prediction
4. misc - all the related functions for different entities in EHR system

shell commands are located in each package to run different functions