MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A Remote Patient Monitoring System for Congestive Heart Failure
Journal of Medical Systems
MARHS: mobility assessment system with remote healthcare functionality for movement disorders
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Dynamic Task Optimization in Remote Diabetes Monitoring Systems
HISB '12 Proceedings of the 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology
WANDA: an end-to-end remote health monitoring and analytics system for heart failure patients
Proceedings of the conference on Wireless Health
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While significant effort has been made on designing Remote Monitoring Systems (RMS), limited research has been conducted on the potential cost savings that these systems offer in terms of reduction in readmission costs, as well as the costs associated with human resources involved in the intervention process. This paper is particularly interested in exploring potential cost savings that an analytics engine can provide in presence of intelligent back-end data processing and machine learning algorithms against conventional RMS that operate based on simple thresholding approaches. Using physiological data collected from 486 heart failure patients through a clinical study in collaboration with the UCLA School of Medicine, we conduct a retrospective data analysis to estimate prediction accuracy as well as associated costs of the two remote monitoring approaches. Our results show that analytics-based RMS can reduce false negative rates by 61.4% while maintaining a false positive performance close to that of conventional RMS. Furthermore, the proposed analytics engine achieves 61.5% reduction in the overall readmission costs.