Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

ABSTRACT:

Objective

Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question.

Materials and methods

Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation

SUBMITTER: Bergquist T 

PROVIDER: S-EPMC10746301 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature