Abstract Artificial intelligence offers a great opportunity in critical care, particularly when a vast amount of continuously acquired physiological data is incorporated. High-quality, reliably labelled data are paramount for developing and training artificial intelligence methods. However, routinely recorded data in critical care are often noisy, and the sheer volume of high-resolution data is challenging to manage. Generalizable solutions for these problems are lacking, restricting progress. To address these barriers, we developed, an open-source package for loading, visualising, aligning, and annotating medical time series with minimal coding. The tool integrates seamlessly into -notebooks, providing an interactive, customizable interface to interact with the data visually. In this publication, we demonstrate its utility across three use cases. The code and exemplary data are provided as browser-based demos. is freely available and published under the MIT license accompanying this publication.
Orlob et al. (Mon,) studied this question.