Health intelligence: how artificial intelligence transforms population and personalized health

npj Digital Medicine

Volumn 1, Issue 1, 2018, Pages

  Shaban Nejad, Arash ^a   Michalowski, Martin ^b   Buckeridge, David L ^c  

^a UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; DATA VISUALIZATION; DECISION SUPPORT SYSTEMS; INFORMATION MANAGEMENT; MEDICAL COMPUTING; MEDICAL IMAGING; MEDICAL INFORMATION SYSTEMS; PREDICTIVE ANALYTICS;

ENGINEERING INNOVATIONS; GEO CODING; INTEGRATED HEALTH INFORMATION SYSTEMS; MANAGEMENT INTEGRATION; MEDICAL DEVICES; MINING CLASSIFICATION; MULTIPLE APPLICATIONS; PATIENT EDUCATION; PERSONALIZED HEALTHS; POPULATION HEALTH;

HEALTH CARE;

ARTIFICIAL INTELLIGENCE; EDITORIAL; HEALTH CARE DELIVERY; HEALTH INTELLIGENCE; HUMAN; INTERNET; PERSONALIZED MEDICINE; POPULATION HEALTH; PRIORITY JOURNAL; SOCIAL DETERMINANTS OF HEALTH; TELEHEALTH;

EID: 85135598803     PISSN: None     EISSN: 23986352     Source Type: Journal    
DOI: 10.1038/s41746-018-0058-9     Document Type: Editorial

References (12)

1. Shaban-Nejad, A., Lavigne, M., Okhmatovskaia, A. & Buckeridge, D. L. PopHR: a knowledge-based platform to support integration, analysis, and visualization of population health data. Ann. N. Y. Acad. Sci. 1387, 44–53 (2017)
2. Shin, E. K., Mahajan, R., Akbilgic, O. & Shaban-Nejad, A. Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits. npj Digital Medicine 10.1038/s41746-018-0056-y
3. Feng, S., Grépin, K. A. & Chunara, R. Tracking health seeking behavior during an Ebola outbreak via mobile phones and sms. npj Digital Medicine https://doi.org/10.1038/s41746-018-0055-z
4. Wartella, E., Rideout, V., Zupancic, H., Beaudoin-Ryan, L. & Lauricella, A. Teens, Health, and Technology: A National Survey (Center on Media and Human Development, School of Communication, Northwestern University, Evanston, IL, USA, 2015)
5. Shaban-Nejad, A., Brownstein, J. S. & Buckeridge, D. L. Public Health Intelligence and the Internet (Springer International Publishing AG, Cham, 2017)
6. Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–5 (2015)
7. The National Institute of Health (NIH). All of Us Research Program. https://allofus.nih.gov/. (Accessed 29 August 2018)
8. Wilk, S. et al. A data- and expert-driven decision support framework for helping patients adhere to therapy: psychobehavioral targets and associated interventions. In Proc. International Joint Workshop on Knowledge Representation for Health Care (KR4HC 2017), 53–66 (Wiedeń, Austria, 2017)
9. Wilk, S. et al. Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines. J. Biomed. Inform. 66, 52–71 (2017)
10. Dimitriou, N., Arandjelović, O., Harrison, D. J. & Caie, P. D. A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis. npj Digital Medicine 10.1038/s41746-018-0057-x
11. Mamiya, H., Shaban-Nejad, A. & Buckeridge, D. L. Online public health intelligence: ethical considerations at the big data era (eds. Shaban-Nejad, A., Brownstein, J. & Buckeridge, D. L.) Public Health Intelligence and the Internet. Lecture Notes in Social Networks 129–148 (Springer, Cham. 2017)
12. Xu, J. Q., Murphy, S. L., Kochanek, K. D. & Arias, E. Mortality in the United States, 2015. NCHS data brief, no 267 (National Center for Health Statistics, Hyattsville, MD, 2016)