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🧪Yet Another ICU Benchmark: a holistic framework for the standardization of clinical prediction model experiments. Provide custom datasets, cohorts, prediction tasks, endpoints, preprocessing, and models. Paper: https://arxiv.org/abs/2306.05109

Python bindings to the ICU (International Components for Unicode) library (ICU4C).

This repository contains the official code for the paper "Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling" (Nature Communications), which presents a deep learning framework for real-time patient acuity prediction using EHR data.

Repository for the Paper: „On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series“

Repository for the paper 'A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking'.

SA-AKI Mortality Prediction — Survival analysis & binary classification for Sepsis-Associated Acute Kidney Injury using MIMIC-IV. CatBoost, LightGBM, XGBoost, Logistic Regression. AUROC ~0.80+

predicting the morality of acute myocardial infarction. 基于机器学习的急性心肌梗死死亡风险预测

Single-file PyTorch pipeline for pathogen class prediction on MIMIC-III/IV EHR data. Hybrid Conv1D+BiLSTM+numeric model, streaming CSV ETL without pandas, ECE calibration, ROC/PR-AUC, and subgroup bias checks. Research and education only.

Python implementation of the Unicode Message Format 2.0 specification

Multi-agent AI assistant for ICU clinical teams, retrieves evidence from medical literature, analyses chest X-rays, brain MRIs & skin lesions, and answers clinical questions using RAG, LangGraph, and GPT-4o.

NodeJS ICU Word Segmenter

Predicting future hospital mortality using only the first 6 hours of admission to the ICU

Python ICU (ICU - International Components for Unicode) examples

AI-powered ICU early warning system predicting sepsis, hypotension, and hemodynamic collapse 2-6 hours before onset. 4-engine ML ensemble (LightGBM, XGBoost, GRU-D, TCN) with physics-based safety net and multi-agent LLM clinical debate.

Acid-base, blood gases status calculations. Superseded by Heval

Relieve ICU staff through a Wifi babyphone network that transmits identifiable monitoring alarms to multiple displays, dedicated smartphones, DECTs, or any computer.

🔍 Predict cardiac mortality in real-time using AI, leveraging routine tests for fast and accurate risk assessments, ensuring timely intervention for patients.

Wheels for PyICU

This is the repo for the course project of IND ENG 174 (2024 Fall @ UC Berkeley)