@inproceedings{28b625f7e82c4dafa98b677e81db7eee,
title = "Detecting mild cognitive loss with continuous monitoring of medication adherence",
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.",
keywords = "Cognitive loss detection, Continuous monitoring, Medication adherence, Pattern recognition",
author = "Yonghong Huang and Deniz Erdogmus and Zhengdong Lu and Leen, {Todd K.}",
year = "2008",
month = sep,
day = "16",
doi = "10.1109/ICASSP.2008.4517683",
language = "English (US)",
isbn = "1424414849",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "609--612",
booktitle = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP",
note = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ; Conference date: 31-03-2008 Through 04-04-2008",
}