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The future of precision health is data‐driven decision support
Author(s) -
Sperger John,
Freeman Nikki L. B.,
Jiang Xiaotong,
Bang David,
Marchi Daniel,
Kosorok Michael R.
Publication year - 2020
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11475
Subject(s) - interpretability , data science , computer science , closing (real estate) , wearable computer , management science , point (geometry) , decision support system , artificial intelligence , engineering , political science , mathematics , law , embedded system , geometry
In the applied sciences, the ultimate goal is not just to acquire knowledge but to turn knowledge into action. The next wave for data disciplines may be experimental designs and analytical methods for closing the gap between the “real‐world” situations faced by decision‐makers and their idealized representations in optimization problems, and the health sciences are poised to be the discipline where these developments substantially improve lives. We discuss three recent trends in research—experimental designs and analytical methods for precision medicine and pragmatic trials; technological developments in sensors, wearables, and smartphones for measuring health data; and methods addressing algorithmic bias and model interpretability—and argue that these seemingly disparate trends point to a future where data‐driven decision support tools are increasingly used to promote wellbeing.