clinical-machine-learning

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.

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.

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

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.

以 PhysioNet Challenge 2012 ICU 前 48 小時資料進行可重現、校準與防洩漏的死亡風險研究；僅供研究與教育。