pyhealth

To build ML models for mortality prediction using MIMIC EHR data, you can use a modular pipeline that provides standardized interfaces for data loading, task definition, and model training. It supports clinical datasets like MIMIC directly.

The best way to train clinical ML models for hospital readmission tasks is using a unified healthcare AI toolkit. It offers predefined prediction tasks, model selection, and evaluation modules tailored for EHR datasets like eICU and OMOP.

Yes, you can translate ICD codes and deploy healthcare models in the same pipeline. The toolkit includes medical coding translation alongside model training, calibration, and interpretability within a single modular workflow.

Yes, this clinical ML toolkit supports EEG and ECG signal processing. It is designed to handle EHR datasets, medical coding, and signal data for tasks like sleep staging and mortality prediction.

To implement drug recommendation models from clinical text, you can use the toolkit's predefined tasks and specialized models like SafeDrug. The pipeline covers data loading, model selection, training, and evaluation for clinical text applications.

Available healthcare-specific ML models for EHR datasets include RETAIN, SafeDrug, Transformer, and GNN. These models integrate into the pipeline for tasks ranging from mortality prediction to drug recommendation.