The myth of generalisability in clinical research and machine learning in health care.
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| Title: | The myth of generalisability in clinical research and machine learning in health care. |
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| Authors: | Futoma J; School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA., Simons M; Department of Medicine, NYU Langone Health, New York, NY, USA., Panch T; Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA.; Wellframe, Boston, MA, USA., Doshi-Velez F; School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA., Celi LA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.; Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.; Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA. |
| Source: | The Lancet. Digital health [Lancet Digit Health] 2020 Sep; Vol. 2 (9), pp. e489-e492. Date of Electronic Publication: 2020 Aug 24. |
| Publication Type: | Journal Article; Review |
| Journal Info: | Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101751302 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2589-7500 (Electronic) Linking ISSN: 25897500 NLM ISO Abbreviation: Lancet Digit Health |
| Database: | MEDLINE Ultimate |
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