Multiple model analytics for adverse event prediction in remote health monitoring systems

2014 IEEE Healthcare Innovation Conference, HIC 2014

Volumn , Issue , 2014, Pages 106-110

  Pourhomayoun, Mohammad ^a   Alshurafa, Nabil ^a   Mortazavi, Bobak ^b   Ghasemzadeh, Hassan ^c   Sideris, Konstantinos ^a   Sadeghi, Bahman ^a   Ong, Michael ^a   Evangelista, Lorraine ^d   Romano, Patrick ^e   Auerbach, Andrew ^f   Kimchi, Asher ^g   Sarrafzadeh, Majid ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b YALE UNIVERSITY   (United States)

^c WASHINGTON STATE UNIVERSITY   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF CALIFORNIA   (United States)

^g CEDARS SINAI MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; FORECASTING; HEALTH; HEALTH CARE; MEDICAL INFORMATION SYSTEMS; PATIENT MONITORING; PHYSIOLOGICAL MODELS; REMOTE PATIENT MONITORING;

ACCURATE PREDICTION; EARLY INTERVENTION; MULTIPLE-MODELING; PATIENT POPULATION; PHYSIOLOGICAL DATA; PREDICTION ACCURACY; PREDICTIVE MODELING; REMOTE HEALTH MONITORING;

MONITORING;

EID: 84949922867     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HIC.2014.7038886     Document Type: Conference Paper

References (18)

1. M. Lan, N. Alshurafa, H. Ghasemzadeh and M. Sarrafzadeh, "WANDA: An End-to-End Remote Health Monitoring and Analytics System for Heart Failure Patients, " Wireless Health Conf., 2012b.
2. M. K. Suh, et al., "Dynamic Self-adaptive Remote Health Monitoring System for Diabetics, " EMBC, 2012.
3. M. K. Suh, et al., "A Remote Patient Monitoring System for Congestive Heart Failure, " JOMS, June 2011a.
4. N. Alshurafa, et al. "Anti-Cheating: Detecting Self-Inflicted and Impersonator Cheaters for Remote Health Monitoring Systems with Wearable Sensors, " BSN 2014.
5. M. K. Suh, et al., "WANDA B: Weight and Activity with Blood Pressure Monitoring System for Heart Failure Patients, " in IEEE Workshop on IREHSS, 2010b.
6. N. Alshurafa, et al. "Battery optimization in smartphones for remote health monitoring systems to enhance user adherence." Int. Conf. on PErvasive Technologies Related to Assistive Environments. 2014.
7. N. Alshurafa, et al. "Improving Compliance in a Remote Health Monitoring System through Smartphone Battery Optimization". IEEE J Biomed Health Inform, 2014.
8. S. I. Lee, H. Ghasemzadeh, B. J. Mortazavi, M. Lan, N. Alshurafa, M. Ong, M. Sarrafzadeh, Remote Health Monitoring Systems: What Impact Can Data Analytics Have on Cost?, in Wireless Health, 2013.
9. M. Pourhomayoun, Z. Jin and M.L. Fowler, "Accurate Localization of In-Body Medical Implants Based on Spatial Sparsity, " IEEE Transactions on Biomedical Engineering, 2013.
10. M. Pourhomayoun, M. Fowler, Z. Jin, "A Novel Method for Medical Implant In-Body Localization, " Int. Conf. of the IEEE Engineering in Medicine & Biology Society (EMBC), 2012.
11. M. Pourhomayoun, Z. Jin and M.L. Fowler, "Indoor Localization, Tracking and Fall Detection for Assistive Healthcare Based on Spatial Sparsity and Wireless Sensor Network, " International Journal of Monitoring and Surveillance Technologies Research, 2013.
12. K. Hung and Z. Yuan-Ting, Implementation of a WAP-based telemedicine system for patient monitoring, Information Technology in Biomedicine, IEEE Trans. on, vol. 7, no. 2, pp. 101-107, 2003.
13. A. G. Logan, et al., "Mobile Phone-Based Remote Patient Monitoring System for Management of Hypertension in Diabetic Patients, " American Journa of Hypertention, 2007.
14. A. Smith, Pew Internet. Nearly half of American adults are smartphone owners, http://pewinternet.org/Reports/2012/Smartphone-Update-2012/Findings.aspx, March 2012.
15. S. I. Chaudhry, Y. Wang, J. Concato, T. M. Gill and H. M. Krumholz, "Patterns of weight change preceding hospitalization for heart failure, " Circulation, vol. 116, pp. 1549-1554, 2007.
16. Isabelle Guyon, Andre Elisseeff, " An Introduction to Variable and Feature Selection", Journal of Machine Learning Research 3 (2003).
17. Breiman, Leo. "Random Forests". Machine Learning 45 (1): 5-32, (2001).
18. Black JT, Romano PS, Sadeghi B, Auerbach AD, Ganiats TG, Greenfield S, Kaplan SH, Ong MK; BEAT-HF Research Group. A remote monitoring and telephone nurse coaching intervention to reduce readmissions among patients with heart failure: study protocol for the Better Effectiveness After Transition-Heart Failure (BEATHF) randomized controlled trial. Trials. 2014 Apr 13;15(1):124. doi: 10.1186/1745-6215-15-124.