Time to reality check the promises of machine learning-powered precision medicine
| Autor: | Andrew L. Beam, Kellyn F Arnold, Kareem Carr, Peter W.G. Tennant, Maarten van Smeden, Christoph Lippert, Jack Wilkinson, Marc de Kamps, Rachel Sippy, Eleanor J Murray, Mark S. Gilthorpe, Stefan Konigorski |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry MEDLINE Medicine (miscellaneous) Health Informatics Precision medicine Machine learning computer.software_genre lcsh:Computer applications to medicine. Medical informatics Medical care Reality check Machine Learning Health Information Management Algorithmic complexity lcsh:R858-859.7 Humans Decision Sciences (miscellaneous) Artificial intelligence Precision Medicine business computer Delivery of Health Care |
| Zdroj: | The Lancet: Digital Health, Vol 2, Iss 12, Pp e677-e680 (2020) Wilkinson, J, Arnold, K, Murray, E, van Smeden, M, Carr, K, Sippy, R, de Kamps, M, Beam, A, Konigorski, S, Lippert, C, Gilthorpe, M & Tennant, P 2020, ' Time to reality check the promises of machine learning-powered precision medicine ', The Lancet Digital Health, vol. 2, no. 12 . https://doi.org/10.1016/S2589-7500(20)30200-4 |
| ISSN: | 2589-7500 |
| 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: | OpenAIRE |
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