Dynamic self-adaptive remote health monitoring system for diabetics

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2012, Pages 2223-2226

  Suh, Myung Kyung ^a   Moin, Tannaz ^b   Woodbridge, Jonathan ^a   Lan, Mars ^a   Ghasemzadeh, Hassan ^a   Bui, Alex ^c   Ahmadi, Sheila ^d   Sarrafzadeh, Majid ^a  

^a University of California   (United States)

^b VETERANS AFFAIRS MEDICAL CENTER   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DAILY TASKS; DATA ANALYTICS; DATA-DRIVEN METHODS; DIABETIC PATIENT; HEALTH CARE PROFESSIONALS; HEALTH MONITORING; HEALTH MONITORING SYSTEM; HEALTH PROJECTS; HEALTH STATUS; INFORMATION GAIN; MINIMUM SUPPORT; MINIMUM SUPPORT THRESHOLDS; OPTIMIZATION TECHNIQUES; QUALITY OF CARE; REAL-TIME FEEDBACK; SELF-ADAPTIVE; SENSOR TECHNOLOGIES; VITAL SIGN; WIRELESS COMMUNICATIONS;

ALGORITHMS; BLOOD PRESSURE; DIGITAL STORAGE; MONITORING; WIRELESS TELECOMMUNICATION SYSTEMS;

HEALTH;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED THERAPY; COMPUTER SYSTEM; DIABETES MELLITUS; HUMAN; METHODOLOGY; PSYCHOPHYSIOLOGY; TELEMEDICINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOFEEDBACK, PSYCHOLOGY; COMPUTER SYSTEMS; DIABETES MELLITUS; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; TELEMEDICINE; THERAPY, COMPUTER-ASSISTED;

MLCS; MLOWN;

EID: 84880939022     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EMBC.2012.6346404     Document Type: Conference Paper

References (16)

1. CDC. Centers for disease control and prevention. http://www.cdc.gov/. 2012.
2. Diabetes Control and Complications Trial Research Group. Effect of intensive diabetes treatment on the development and progression of long-term complications in adolescents with insulin-dependent diabetes mellitus: Diabetes Control and Complications Trial. The journal of pediatrics, 125(2), 177-188. 1994
3. Hanefeld, M. Risk factors for myocardial infarction and death in newly detected NIDDM: the Diabetes Intervention Study, 11-year follow-up. Diabetologia, 39(12), 1577-1583. 1996.
4. P. Ciechanowski, W. Katon, J. Russo, and I. Hirsch. The relationship of depressive symptoms to symptom reporting, self-care and glucose control in diabetes. General Hospital Psychiatry, 25(4):246-252. 2003.
5. S. Vijan and R. Hayward. Treatment of hypertension in type 2 diabetes mellitus: blood pressure goals, choice of agents, and setting priorities in diabetes care. Annals of internal medicine, 138(7):593-602. 2003.
6. The Diabetes Prevention Program Research Group. The diabetes prevention program (DPP): Description of lifestyle intervention, 2002.
7. Shea, S. A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus: 5 year results of the IDEATel study. Journal of the American Medical Informatics Association, 16(4), 446-456. 2009.
8. Chaudhry, S I. Randomized trial of telemonitoring to improve heart failure outcomes (Tele-HF): study design. Journal of cardiac failure, 13(9), 709-714. 2007.
9. Suh, M. A Remote Patient Monitoring System for Congestive Heart Failure. Journal of medical systems, 35(5), 1165-1178. 2011.
10. Or, C K L. Factors affecting home care patients' acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association, 18(1), 51-59.2011.
11. Lu, H P. An empirical study of perceived usefulness and perceived ease of use on computerized support system use over time. International journal of information management, 14(5), 317-3290. 1994.
12. Tang, Z. Applying heuristic evaluation to improve the usability of a telemedicine system. Telemedicine and e-health, 12(1), 24-34. 2006.
13. Dabbs, A D V. User-centered design and interactive health technologies for patients. Computers, informatics, nursing, 27(3), 175-183. 2009.
14. Suh, M. Missing data imputation for remote CHF patient monitoring systems. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 3184-3187. 2011.
15. Agrawal, R. Fast algorithms for mining association rules. VLDB, pp.487-489.1994.
16. Flach, P A. Confirmation-guided discovery of first-order rules with Tertius. Machine learning, 42(1/2), 61-95. 2001.