AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning

• Published in: PLOS digital health Vol. 2; no. 6; p. e0000276
• Main Authors: Imrie, Fergus, Cebere, Bogdan, McKinney, Eoin F., van der Schaar, Mihaela
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Automation; Biobanks; Biology and life sciences; Classification; Computer and Information Sciences; Datasets; Empowerment; Health care; Machine learning; Medicine and Health Sciences; Missing data; Optimization; Physical Sciences; Research and Analysis Methods; Software; Survival analysis; United Kingdom > UK
• ISSN: 2767-3170; 2767-3170
• DOI: 10.1371/journal.pdig.0000276

Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework, AutoPrognosis 2.0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques. Software : https://github.com/vanderschaarlab/AutoPrognosis