Temporal progress model of metabolic syndrome for clinical decision support system |
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| Affiliation: | 1. ETRI/PEC, 218 Gajeongno, Yusung-gu, Daejeon, 305-700, Republic of Korea;2. KAIST/Dept. of Electrical Eng., 291 Daehak-ro, Yusung-gu, Daejeon, 305-701, Republic of Korea;3. Department of Business and Accounting, Hanbat National Univ., Daejeon, Republic of Korea;1. Dept. of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, 18071 Granada, Spain;2. Dept. of Computer Science, University of Jaén, 23071 Jaén, Spain;3. Dept. of Physical Therapy, University of Granada, 18071 Granada, Spain;1. Computer Science Department, State University of Londrina (UEL), Rodovia Celso Garcia Cid, Pr 445 Km 380, Campus Universitário, 86051-980 Londrina, Brazil;2. Instituto de Telecomunicações, University of Beira Interior, Rua Marquês D''Ávila e Bolama, 6201-001 Covilhã, Portugal;3. University ITMO, St. Petersburg, Russia;2. Deusto Institute of Technology – DeustoTech (University of Deusto), Av. Universidades 24, 48007, Bilbao, Spain;1. ViGIR Lab, Electrical Computer Engineering Dept., University of Missouri, United States;2. Sinclair School of Nursing, University of Missouri, United States;3. Electrical and Computer Engineering Dept. and the Informatics Institute, University of Missouri, United States;1. Nova University of Lisbon, Faculty of Sciences and Technology, Campus de Caparica, 2829-616 Caparica, Portugal;2. Conforto em Casa, Lda., Taguspark, Oeiras, Portugal;1. Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan;2. Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan;3. Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan |
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| Abstract: | The development of an integrated and personalized healthcare system is becoming an important issue in the modern healthcare industry. One of main objectives of integrated healthcare system is to effectively manage patients having chronic diseases that require long term care and its temporal information plays an important role to manage the statuses of diseases. Thus, a patient having chronic disease needs to visit the hospital periodically, which generates large volume of medical examination data. Among the various chronic diseases, metabolic syndrome (MS) has become a popular chronic disease in many countries. There have been efforts to develop an MS risk quantification and prediction model and to integrate it into personalized healthcare system, so as to predict the risk of having MS in the future. However, the development of methods for temporal progress management of metabolic syndrome has not been widely investigated. This paper proposes a method for identifying the temporal progress of MS patients' status based on the chronological clustering methodology. To investigate the temporal changes of disease status, we develop a chronological distance variance model that quantifies the difference of areal similarity degree (ASD) values between estimated and examined results of MS risk factors. We evaluate the clinical effectiveness of the temporal progress model by using sample subjects' examination results that have been measured for 10 years. We further elaborate the accuracy of the proposed temporal progress estimation method by using multiple linear regression method. Then, we develop a tier-based patients' MS status classification based on the chronological distance variance. The tier classification is based on the sensitivity for temporal change of MS status according to different values of control range of chronological distance variance. Our proposed temporal change identification method and patients' tier classification are expected to be incorporated with the integrated healthcare systems to help physicians with identifying the temporal progress of MS patients' health status and MS patients with self-management at home environments. |
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