Detecting mild cognitive loss with continuous monitoring of medication adherence

Yonghong Huang, Deniz Erdogmus, Zhengdong Lu, Todd K. Leen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes an approach for detecting early cognitive loss using medication adherence behavior. We investigate the discriminative power of a comprehensive set of recurrent medication timing features extracted from time-of-day and inter-dose timing statistics. We adopt information theoretic measures for feature ranking for initial dimensionality reduction and conduct exhaustive leave-one-out cross validation for final feature selection and regularization. The selected feature set is subjected to a support vector machine for classification. The results demonstrate that patterns of adherence based on the data from relatively unobtrusive behavior monitoring can make reliable inference for mild cognitive loss individuals.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages609-612
Number of pages4
DOIs
StatePublished - Sep 16 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

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Keywords

  • Cognitive loss detection
  • Continuous monitoring
  • Medication adherence
  • Pattern recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Huang, Y., Erdogmus, D., Lu, Z., & Leen, T. K. (2008). Detecting mild cognitive loss with continuous monitoring of medication adherence. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 609-612). [4517683] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2008.4517683