- Jun 2021
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www.bmj.com www.bmj.com
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Li, X., Ostropolets, A., Makadia, R., Shoaibi, A., Rao, G., Sena, A. G., Martinez-Hernandez, E., Delmestri, A., Verhamme, K., Rijnbeek, P. R., Duarte-Salles, T., Suchard, M. A., Ryan, P. B., Hripcsak, G., & Prieto-Alhambra, D. (2021). Characterising the background incidence rates of adverse events of special interest for covid-19 vaccines in eight countries: Multinational network cohort study. BMJ, 373, n1435. https://doi.org/10.1136/bmj.n1435
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- Sep 2020
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www.thelancet.com www.thelancet.com
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Wilkinson, Jack, Kellyn F. Arnold, Eleanor J. Murray, Maarten van Smeden, Kareem Carr, Rachel Sippy, Marc de Kamps, et al. ‘Time to Reality Check the Promises of Machine Learning-Powered Precision Medicine’. The Lancet Digital Health 0, no. 0 (16 September 2020). https://doi.org/10.1016/S2589-7500(20)30200-4.
Tags
- personalised medical approach
- improved diagnosis
- prediction of individual responses
- challenges
- clinical science
- algorithmic complexity
- collaboration
- clinical practice
- machine learning powered precision medicine
- lang:en
- machine learning
- is:report
- electronic health database
- revolution
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