Time to reality check the promises of machine learning-powered precision medicine
| Autor: | Jack Wilkinson, PhD, Kellyn F Arnold, PhD, Eleanor J Murray, ScD, Maarten van Smeden, PhD, Kareem Carr, MSc, Rachel Sippy, PhD, Marc de Kamps, PhD, Andrew Beam, PhD, Stefan Konigorski, PhD, Christoph Lippert, ProfPhD, Mark S Gilthorpe, ProfPhD, Peter W G Tennant, PhD |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | The Lancet: Digital Health, Vol 2, Iss 12, Pp e677-e680 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2589-7500 35710594 |
| DOI: | 10.1016/S2589-7500(20)30200-4 |
| Popis: | Summary: Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste. |
| Databáze: | Directory of Open Access Journals |
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