TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine
This library addresses the need for standardized tools in medical machine learning, facilitating innovation for researchers, healthcare professionals, and industry practitioners, though it is incremental as it builds on existing temporal ML concepts.
The authors introduced TemporAI, an open-source Python library designed to support machine learning tasks with time-domain data in medicine, offering tools for prediction, causal inference, and time-to-event analysis to standardize development and benchmarking.
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.