pyhealth

To build a clinical deep learning pipeline for MIMIC-III, you can use this skill to standardize the dataset loading, model instantiation, and training workflow. It provides modular components to process EHR data and train models like Transformers for mortality prediction.

Yes, you can use PyTorch to develop drug recommendation models from electronic health records. This skill integrates PyTorch-based architectures and supports cross-mapping medical coding systems like ATC, NDC, and RxNorm for drug recommendation tasks.

The best way to map medical codes like ICD-9 to ICD-10 for EHR analysis is using built-in medical code utilities. This skill provides native support for cross-mapping and looking up ICD-9/10, ATC, NDC, and RxNorm coding systems.

Yes, this skill supports mortality prediction across both MIMIC-IV and eICU datasets. It standardizes the dataset, task, model, and trainer workflow, allowing researchers to train and evaluate clinical models across diverse EHR and physiological signal datasets.

You should use a unified pipeline for clinical machine learning to avoid writing repetitive boilerplate code. This skill streamlines the complex multi-stage process, allowing researchers to move from raw EHR data to trained models using standardized data loading, training, and clinical metric evaluation.