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 language | English (US) |
|---|---|
| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Pages | 609-612 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2008 |
| Event | 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States Duration: Mar 31 2008 → Apr 4 2008 |
Other
| Other | 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP |
|---|---|
| Country | United States |
| City | Las Vegas, NV |
| Period | 3/31/08 → 4/4/08 |
Fingerprint
Keywords
- Cognitive loss detection
- Continuous monitoring
- Medication adherence
- Pattern recognition
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
- Acoustics and Ultrasonics
Cite this
Detecting mild cognitive loss with continuous monitoring of medication adherence. / Huang, Yonghong; Erdogmus, Deniz; Lu, Zhengdong; Leen, Todd K.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2008. p. 609-612 4517683.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detecting mild cognitive loss with continuous monitoring of medication adherence
AU - Huang, Yonghong
AU - Erdogmus, Deniz
AU - Lu, Zhengdong
AU - Leen, Todd K.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Cognitive loss detection
KW - Continuous monitoring
KW - Medication adherence
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=51449083114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51449083114&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4517683
DO - 10.1109/ICASSP.2008.4517683
M3 - Conference contribution
SN - 1424414849
SN - 9781424414840
SP - 609
EP - 612
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -