EM-Psychiatry: An Ambient Intelligent System for Psychiatric Emergency

Alam, M.G.R. and Haw, R. and Kim, S.S. and Azad, A.K. and Abedin, S.F. and Hong, C.S. (2016) EM-Psychiatry: An Ambient Intelligent System for Psychiatric Emergency. IEEE Transactions on Industrial Informatics, 12 (6). pp. 2321-2330. ISSN 1551-3203, DOI https://doi.org/10.1109/TII.2016.2610191.

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Official URL: http://dx.doi.org/10.1109/TII.2016.2610191


The proliferation of the market in patient care services is attracting attention in the healthcare industry; however, a remote mental healthcare system is still unattainable. In this paper, an ambient intelligent system of in-home psychiatric care service for emergency psychiatry (EM-psychiatry) is proposed for the remote monitoring of psychiatric emergency patients. The emergency psychiatric states of patients are modeled as the states of the maximum-entropy Markov model (MEMM), in which sensor observations, psychiatric screening scores, and patients' histories are considered as the observations of MEMM. A modified Viterbi, a machine-learning algorithm, is used to generate the most probable psychiatric state sequence based on such observations; then, from the most likely psychiatric state sequence, the emergency psychiatric state is predicted through the proposed algorithm. The ambient EM-psychiatry model is implemented and the performance of the proposed prediction model is analyzed using the receiver operator characteristics curves, which demonstrates that the use of the EM-psychiatric screening questionnaire with biosensor observations enhances the prediction accuracy.

Item Type: Article
Uncontrolled Keywords: Ambient intelligent (AmI) system; Emergency psychiatry (EM-psychiatry) maximum-entropy Markov model (MEMM); Mental healthcare service
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Economics & Administration
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 07 Sep 2017 05:37
Last Modified: 07 Sep 2017 05:37
URI: http://eprints.um.edu.my/id/eprint/17737

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