Code for the paper "ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?"
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Updated
Jun 18, 2025 - Python
Code for the paper "ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?"
Advanced machine learning system for cardiovascular disease risk prediction using clinical biomarkers and patient health indicators
Módulo de inteligencia artificial para dabetai. Modelos de predicción de complicaciones diabéticas tipo 1 (retinopatía, nefropatía, neuropatía, pie diabético). Componente de backend para plataforma de monitoreo de la diabetes.
ICU LOS prediction for pneumonia patients using MIMIC-III time-series data & interpretable ML
Open-source, reproducible LSTM implementation for in-hospital mortality prediction with Focal Loss and calibration
Clinical predictive model for fetal health classification from cardiotocogram data using logistic regression in SAS. Supports early intervention and maternal-fetal health monitoring.
End-to-end ML project: 30-day hospital readmission prediction using XGBoost, PostgreSQL, Python and Tableau
GCE framework for interpretable modeling in cardiac sarcoma survival using machine and deep learning. It captures complex feature relationships to enhance predictive insights and clinical understanding.
Predicting early functional mobility decline using longitudinal biomarker trajectories from OMOP EHR data (AMIA submission).
WiDS Datathon 2026 clinical prediction pipeline with gradient boosting ensembles, calibration, and leaderboard score 0.97089
Reference implementation of MicrobiomeRiskScore (MRS): a treatment-aware Transformer that predicts healthcare-associated infections from longitudinal gut microbiome dynamics. Companion code to Ma et al., npj Digital Medicine.
Code repository for arXiv:2602.06110
Foundation model for health trajectory prediction — architecture shootout across Transformer, Mamba, and Continuous-Time
TrajGPT vs CLMBR-T-base on EHRSHOT: training, embedding extraction, and few-shot evaluation across 15 clinical prediction tasks.
🩺 Predict in-hospital mortality using a reproducible deep learning model with LSTM, Focal Loss, and calibrated thresholds based on synthetic MIMIC-III data.
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