Patient History-driven Framework for Healthcare Analytics
Latha R1, Vetrivelan P2
1Latha R, SENSE, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
2Vetrivelan P, SENSE, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 403-408 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10731291S319/19©BEIESP | DOI: 10.35940/ijeat.A1073.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless body are network (WBAN) is evolving more rapidly due to the development of internet of things (IoT). Decision making is the main concern in medical field which leads to optimization. Medical evidences in patient care improve optimization in patient care. Partially observable markov decision process (POMDP) helps in making accurate decisions with the help of observations and past actions in medical field. Hence dynamic decision making makes it possible. In POMDP, the incremental method is designed to incorporate any immediate change and immediately send updates. In this paper, process mining is applied in finding the history of patients who are travelling from one country to another for in search of job or for doing a major clinical operation. Event data is very much important for handling patient’s history. Event data stores the date and time at which the patient gets consultation. Electronic medical records (EMR) are nothing but storage of all the event data of patients visiting the hospital. Event data gives the evidence of patients when they had a consultation with a doctor. Event data is present anywhere. Partially Observable Markov Decision Process for Patient-history and Careflow mining Algorithm for Heuristic Comparison are presented in this paper. Process mining gives a direct relationship by step by step evaluation and improvement of the process. It also exhibits patient care by identifying the execution errors, understanding the process heterogeneity. The online process mining tool, PROM helps finding the history of patient.
Keywords: Decision Making, IoT, POMDP, PROM, WBAN.
Scope of the Article: Patterns and Frameworks