ArXiv Preprint
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 a number of 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 a machine learning framework,
AutoPrognosis 2.0, to develop 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. Our framework eliminates the major technical obstacles to
predictive modeling with machine learning that currently impede clinical
adoption. 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. Our risk score has been implemented as a web-based
decision support tool and can be publicly accessed by patients and clinicians
worldwide. In addition, AutoPrognosis 2.0 is provided as an open-source python
package. By open-sourcing our framework as a tool for the community, clinicians
and other medical practitioners will be able to readily develop new risk
scores, personalized diagnostics, and prognostics using modern machine learning
techniques.
Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar
2022-10-21
