EM-Psychiatry: An Ambient Intelligent System for Psychiatric Emergency
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2016
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/17737/ http://dx.doi.org/10.1109/TII.2016.2610191 |
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| Summary: | 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. |
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