# PyHealth: A Python Library for Health Predictive Models

arXiv: https://arxiv.org/abs/2101.04209 · HTML: https://mlsanity.com/paper/2101.04209/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [StageNet: Stage-Aware Neural Networks for Health Risk Prediction](https://mlsanity.com/paper/2001.10054/) (2020)
- [RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data](https://mlsanity.com/paper/1807.08820/) (2018)
- [Inferring Degrees from Incomplete Networks and Nonlinear Dynamics](https://mlsanity.com/paper/2004.10546/) (2020)
- [MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals](https://mlsanity.com/paper/1905.11333/) (2019)
- [Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text](https://mlsanity.com/paper/2009.14578/) (2020)
- [FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection](https://mlsanity.com/paper/2010.16039/) (2020)
- [K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection](https://mlsanity.com/paper/1908.06857/) (2019)

## Built on by (most cited first, 9 of 9)

- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond](https://mlsanity.com/paper/2204.08200/) (2022)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [Multi-task Heterogeneous Graph Learning on Electronic Health Records](https://mlsanity.com/paper/2408.07569/) (2024)
- [Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs](https://mlsanity.com/paper/2502.13555/) (2025)
- [MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets](https://mlsanity.com/paper/2411.00200/) (2024)
- [Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction](https://mlsanity.com/paper/2502.10689/) (2025)
- [Temporal Clustering with External Memory Network for Disease Progression Modeling](https://mlsanity.com/paper/2109.14147/) (2021)
- [MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records](https://mlsanity.com/paper/2608.06430/) (2026)

## Abstract (arXiv)

Despite the explosion of interest in healthcare AI research, the reproducibility and benchmarking of those research works are often limited due to the lack of standard benchmark datasets and diverse evaluation metrics. To address this reproducibility challenge, we develop PyHealth, an open-source Python toolbox for developing various predictive models on healthcare data. PyHealth consists of data preprocessing module, predictive modeling module, and evaluation module. The target users of PyHealth are both computer science researchers and healthcare data scientists. With PyHealth, they can conduct complex machine learning pipelines on healthcare datasets with fewer than ten lines of code. The data preprocessing module enables the transformation of complex healthcare datasets such as longitudinal electronic health records, medical images, continuous signals (e.g., electrocardiogram), and clinical notes into machine learning friendly formats. The predictive modeling module provides more than 30 machine learning models, including established ensemble trees and deep neural network-based approaches, via a unified but extendable API designed for both researchers and practitioners. The evaluation module provides various evaluation strategies (e.g., cross-validation and train-validation-test split) and predictive model metrics. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, and interactive examples are introduced in the library's development. PyHealth can be installed through the Python Package Index (PyPI) or https://github.com/yzhao062/PyHealth .