AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning.
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....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-06-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000276 |
| _version_ | 1797698205014556672 |
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| author | Fergus Imrie Bogdan Cebere Eoin F McKinney Mihaela van der Schaar |
| author_facet | Fergus Imrie Bogdan Cebere Eoin F McKinney Mihaela van der Schaar |
| author_sort | Fergus Imrie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T03:50:53Z |
| format | Article |
| id | doaj.art-bf29cdfab67c40f297bbffc642f42f22 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T03:50:53Z |
| publishDate | 2023-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-bf29cdfab67c40f297bbffc642f42f222023-09-03T12:26:40ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-06-0126e000027610.1371/journal.pdig.0000276AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning.Fergus ImrieBogdan CebereEoin F McKinneyMihaela van der SchaarDiagnostic 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.https://doi.org/10.1371/journal.pdig.0000276 |
| spellingShingle | Fergus Imrie Bogdan Cebere Eoin F McKinney Mihaela van der Schaar AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. PLOS Digital Health |
| title | AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. |
| title_full | AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. |
| title_fullStr | AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. |
| title_full_unstemmed | AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. |
| title_short | AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. |
| title_sort | autoprognosis 2 0 democratizing diagnostic and prognostic modeling in healthcare with automated machine learning |
| url | https://doi.org/10.1371/journal.pdig.0000276 |
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