The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Design of Rough Set Theory Based Disease Monitoring System for Healthcare

헬스 케어를 위한 RDMS 설계

  • Received : 2013.10.30
  • Accepted : 2013.11.29
  • Published : 2013.12.31
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Abstract

This paper proposes the RDMS(Rough Set Theory based Disease Monitoring System) which efficiently manages diseases in Healthcare System. The RDMS is made up of DCM(Data Collection Module), RDRGM(RST based Disease Rules Generation Module), and HMM(Healthcare Monitoring Module). The DCM collects bio-metric informations from bio sensor of patient and stores it in RDMS DB according to the processing procedure of data. The RDRGM generates disease rules using the core of RST and the support of attributes. The HMM predicts a patient's disease by analyzing not only the risk quotient but also that of complications on the patient's disease by using the collected patient's information by DCM and transfers a visualized patient's information to a patient, a family doctor, etc according to a patient's risk quotient. Also the HMM predicts the patient's disease by comparing and analyzing a patient's medical information, a current patient's health condition, and a patient's family history according to the rules generated by RDRGM and can provide the Patient-Customized Medical Service and the medical information with the prediction result rapidly and reliably.

본 논문에서는 헬스 케어 시스템에서 효율적으로 질병을 관리할 수 있는 RDMS(Rough Set Theory based Disease Monitoring System)을 제안한다. RDMS는 DCM(Data Collection Module), RDRGM(RST based Disease Rule Generation Module), HMM(Healthcare Monitoring Module)로 구성된다. DCM은 바이오센서로부터 환자의 생체 정보를 수집하고, 데이터 처리 절차에 따라 RDMS DB에 저장한다. RDRGM은 RST의 코어와 속성의 지지율을 이용하여 질병 규칙을 생성한다. HMM은 DCM에 의해 수집된 환자 정보를 이용하여 환자의 질병에 대한 위험지수뿐만 아니라 질병에 대한 합병증에 관한 위험지수까지 분석함으로써 환자의 질병을 예측하고, 환자의 위험지수에 따라 환자, 주치의 등에 시각화된 환자의 정보를 전달한다. 또한, RDRGM에 의해 생성된 규칙들에 따라 환자의 의료정보, 현재의 환자건강상태, 환자 가족력 등을 비교분석하여 환자의 질병을 예측하고, 예측결과에 따라 환자 맞춤형 의료 서비스와 의료 정보를 신속하고 신뢰성 있게 제공할 수 있다.

Keywords

References

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