Big data and machine learning in health care

JAMA - Journal of the American Medical Association

Volumn 319, Issue 13, 2018, Pages 1317-1318

  Beam, Andrew L ^a   Kohane, Isaac S ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CARDIOVASCULAR RISK; CLINICAL DECISION MAKING; DATA PROCESSING; DEPENDENT VARIABLE; DIABETIC RETINOPATHY; ELECTRONIC HEALTH RECORD; EYE PHOTOGRAPHY; FRAMINGHAM RISK SCORE; GENOME; HEALTH CARE; HEALTH CARE SYSTEM; HEURISTICS; HOSPITAL ADMISSION; HUMAN; IMAGE ANALYSIS; INDEPENDENT VARIABLE; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL MODEL; MEASUREMENT; MEDICAL HISTORY; OPHTHALMOLOGIST; PARAMETERS; PHYSICIAN; PRIORITY JOURNAL; RETINA; SENSITIVITY ANALYSIS; SHORT SURVEY; SOCIAL MEDIA; ALGORITHM; DATA MINING; INFORMATION PROCESSING; PROCEDURES;

ALGORITHMS; DATA MINING; DATASETS AS TOPIC; HUMANS; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER);

EID: 85044927780     PISSN: 00987484     EISSN: 15383598     Source Type: Journal    
DOI: 10.1001/jama.2017.18391     Document Type: Short Survey

References (3)

1. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410.
2. Brand RJ, Rosenman RH, Sholtz RI, et al. Multivariate prediction of coronary heart disease in theWestern Collaborative Group Study compared to the findings of the Framingham study. Circulation. 1976;53(2):348-355.
3. Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. JAMA. 2014;311 (24):2479-2480.