Vitabel: A Python Framework for Visualizing and Labelling High-Resolution Physiological Data for Critical Care Machine Learning

Artificial intelligence offers great opportunities 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 Vitabel, an open-source Python framework for post hoc loading, visualizing, aligning, and annotating medical time series. The framework provides sensible defaults and interactive components for efficient use in preconfigured workflows, while remaining flexible and extendable for custom analysis and annotation pipelines. It integrates seamlessly into Jupyter Notebooks, providing an interactive, customizable interface for visual interaction with the data. In this publication, we demonstrate its utility across three use cases. The code and exemplary data are provided as browser-based demos. Vitabel is freely available and published under the MIT license accompanying this publication.