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EM-Psychiatry: An Ambient Intelligent System for Psychiatric Emergency
Alam, Md. Golam Rabiul,Haw, Rim,Kim, Sung Soo,Azad, Md. Abul Kalam,Abedin, Sarder Fakhrul,Hong, Choong Seon IEEE 2016 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS - Vol.12 No.6
<P>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.</P>
Edge-of-things computing framework for cost-effective provisioning of healthcare data
Alam, Md. Golam Rabiul,Munir, Md. Shirajum,Uddin, Md. Zia,Alam, Mohammed Shamsul,Dang, Tri Nguyen,Hong, Choong Seon Elsevier 2019 Journal of parallel and distributed computing Vol.123 No.-
<P><B>Abstract</B></P> <P>Edge-of-Things (EoT)-based healthcare services are forthcoming patient-care amenities related to autonomic and persuasive healthcare, where an EoT broker usually works as a middleman between the Healthcare Service Consumers (HSC) and Computing Service Providers (CSP). The computing service providers are the edge computing service providers (ECSP) and cloud computing service provider (CCSP). Sensor observations from a patient’s body area networks (BAN) and patients’ medical and genetic historical data are very sensitive and have a high degree of interdependency. It follows that EoT based patient monitoring systems or applications are tightly coupled and require obstinate synchronization. Therefore, this paper proposes a portfolio optimization solution for the selection of virtual machines (VMs) of edge and/or cloud computing service providers. The dynamic pricing for an EoT computation service is considered by the EoT broker for optimal VM provisioning in an EoT environment. The proposed portfolio optimization solution is compared with the traditional certainty equivalent approach. As the portfolio optimization is a centralized solution approach, this paper also proposes an alternating direction method of multipliers (ADMM) based distributed provisioning method for the healthcare data in the EoT computing environment. A comparative study shows the cost-effective provisioning for the healthcare data through portfolio optimization and ADMM methods over the traditional certainty equivalent and greedy approach, respectively.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Edge-of-Things (EoT) computation framework for healthcare service provisioning. </LI> <LI> Portfolio optimization approach for cost-effective healthcare data provisioning. </LI> <LI> Alternating direction method of multipliers (ADMM) for healthcare data offloading. </LI> </UL> </P>