Clinical Augmented Intelligence (CLAI) Group

Integrating Medicine, Data Science, & Phenomenology to enrich human phenotype models.

About Us

Our goal at CLAI is to integrate phenomenological theoryand clinical knowledge with state-of-the-art Machine Learning/Artificial Intelligence methods to develop enriched computational models of human phenotypes.

Projects

Propensity Score Analysis for Medical Research : A Primer and Tutorial (PS Tutorial)

Propensity score analysis is a popular method of adjusting for confounding in observational studies, i.e., studies where patients are not randomly assigned into treatment groups. Despite its popularity in medical research, there are many nuances to the method that are often missed by researchers, including about the assumptions required, the quantities that can be estimated, and the correct procedures for performing and reporting an analysis

Machine Learning for Health Outcomes (MLHO)

MLHO is a thinkin’ Machine Learning framework that implements iterative sequential representation mining, and feature and model selection to predict health outcomes. MLHO’s architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. 

Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) Analysis

This project aims to implement a computational algorithm for defining long COVID patients based on the WHO definition. We provide different scripts to streamline multi-site implementation.