Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Data Processing and Visualization Method for Retrospective Data Analysis and Research Using Patient Vital Signs

환자의 활력 징후를 이용한 후향적 데이터의 분석과 연구를 위한 데이터 가공 및 시각화 방법

  • 김수민 (대구가톨릭대학교) ;
  • 윤지영 (대구경북첨단의료산업진흥재단)
  • Received : 2021.06.23
  • Accepted : 2021.08.24
  • Published : 2021.08.31
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Abstract

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

Keywords

Acknowledgement

본 연구는 2019년도 산업통상자원부 및 한국산업기술진흥원(KIAT)의 국제공동기술개발사업(인공지능과 WRS 바이오마커를 이용한 고성능 패혈증 환자 모니터링 시스템) 과제의 지원을 받아 수행하였음.

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