clinical-machine-learning

Independent research project investigating feature reliability in machine learning prediction of cognitive impairment from sleep-derived physiological signals.

Research project on missing data, calibration, and trustworthy prediction in clinical machine learning.

Retrospective feasibility study and research pipeline for short-horizon ICU hypotension forecasting using ABP, ECG, PPG, and EHR context, with leakage-aware evaluation and sensor-missingness robustness tests.

Research collaboration on explainable federated learning for predicting cardiac complications following THA/TKA.

Early warning for sepsis with a calibrated alert budget. It states how often it will alert and how often it will be right, then checks both on a hospital it never trained on. Open data, no credentials, every number reproducible.

This repository contains the data and analysis code for the study "Machine Learning-driven biomarker discovery for stratifying treatment response in tick-borne illness". It investigates the identification of robust and reproducible baseline predictors of treatment response using a stability-aware, multi-method machine learning framework.

Open, reproducible implementation of the CMS Hospital-Wide Readmission (HWR) measure on MIMIC-IV: faithful 30-day unplanned-readmission labels, a calibrated gradient-boosted baseline with fairness and drift monitoring, and a leakage-controlled test of whether clinical-note LLM embeddings add signal.

Auditable clinical tabular benchmark for hospital and measurement-policy shift, with reconstructable evidence and explicit claim boundaries.

A-ICF: Auditing, Not Predicting — A Causal Bias-Decomposition Framework for Clinical Fairness. Code, Figures, and Tables for OMLET 2026 (Paper ID: 596).

Auditable EHR data infrastructure for patient world models and clinical agents: source-linked conversion of hospital exports into OMOP CDM 5.4 and MEDS, with an executable 38-check validation contract that reads the published artifacts