TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine

TL;DR

TemporAI is an open source Python library designed to advance machine learning in medical time series analysis by providing standardized tools for prediction, causal inference, and interpretability to foster innovation across research and industry.

Contribution

It introduces a comprehensive toolkit for temporal machine learning in medicine, bridging gaps between research, healthcare, and industry communities.

Findings

Supports multiple data modalities including time series and events

Provides utilities for preprocessing and model interpretability

Facilitates benchmarking and prototyping in medical ML tasks

Abstract

TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component, focused on medicine and healthcare use cases. It supports data in time series, static, and eventmodalities and provides an interface for prediction, causal inference, and time-to-event analysis, as well as common preprocessing utilities and model interpretability methods. The library aims to facilitate innovation in the medical ML space by offering a standardized temporal setting toolkit for model development, prototyping and benchmarking, bridging the gaps in the ML research, healthcare professional, medical/pharmacological industry, and data science communities. TemporAI is available on GitHub (https://github.com/vanderschaarlab/temporai) and we welcome community engagement through use, feedback, and code contributions.