[논문]사용자 참여형 웨어러블 디바이스 데이터 전송 연계 및 딥러닝 대사증후군 예측 모델

이현식; 이웅재; 정태경

doi:10.9723/jksiis.2020.25.6.033

[국내논문] 사용자 참여형 웨어러블 디바이스 데이터 전송 연계 및 딥러닝 대사증후군 예측 모델
Deep Learning Algorithm and Prediction Model Associated with Data Transmission of User-Participating Wearable Devices 원문보기

한국산업정보학회논문지 = Journal of the Korea Industrial Information Systems Research, v.25 no.6, 2020년, pp.33 - 45

이현식 (CHA Univ. Dept. of Integrated Medicine) , 이웅재 (Seoul Women's Univ. Dept. of Digital Media) , 정태경 (Sehan Univ. Dept. of Artificial Intelligence)

초록
AI-Helper

본 논문은 최근 다양한 종류의 웨어러블 디바이스가 헬스케어 도메인에 급증하여 사용되고 있는 상황에서 최신 첨단 기술이 실제 메디컬 환경에서 개인의 질병예측이라는 관점을 바라본다. 사용자 참여형 웨어러블 디바이스를 통하여 임상 데이터와 유전자 데이터, 라이프 로그 데이터를 병합하여 데이터를 수집, 처리, 전송하는 과정을 걸쳐 딥뉴럴 네트워크의 환경에서 학습모델의 제시와 피드백 모델을 연결하는 과정을 제시한다. 이러한 첨단 의료 현장에서 일어나는 메디컬 IT의 임상시험 절차를 걸친 실제 현장의 경우 대사 증후군에 의한 특정 유전자가 질병에 미치는 영향을 측정과 더불어 임상 정보와 라이프 로그 데이터를 병합하여 서로 각기 다른 이종 데이터를 처리하면서 질병의 특이점을 확인하게 된다. 즉, 이종 데이터의 딥뉴럴 네트워크의 객관적 적합성과 확실성을 증빙하게 되고 이를 통한 실제 딥러닝 환경에서의 노이즈에 따른 성능 평가를 실시한다. 이를 통해 자동 인코더의 경우의 1,000 EPOCH당 변화하는 정확도와 예측치가 변수의 증가 값에 수차례 선형적으로 변화하는 현상을 증명하였다.

Abstract ▼ AI-Helper

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

주제어

표/그림 (13)

그림 Fig. 1 Overall System Design Diagram of Various Public Data and Health Prediction Models through Wearable Devices
그림 Fig. 2 Schematic Diagram of the Data Transmission Process according to the Transmission and Collection Procedures of Various Clinical, Gene, and Life-logging Data
그림 Fig. 3 Procedure and Flow Chart of Mobile Implementation Clinical Trial Application by Merging Predictive Model 1 and Predictive Model 2 for Deep Learning-based Disease Risk Prediction
그림 Fig. 4 A General Wearable Device that Transmits Basic Clinical Information (Blood Sugar, Cholesterol, Blood Pressure, etc.) and Life-log Data, and Deep Learning-based Data Collection Method and Transmission Schematic Diagram
그림 Fig. 5 The First Feedback Model Procedure in the Process of Generating the First Prediction Value of the Deep Learning Model by Inputting Various Data
그림 Fig. 6 The Procedure Diagram of the Second Feedback Model in the Process of Merging Life Log Data by Generating the First Prediction Value and the Process of Generating the Second Prediction Value
그림 Fig. 7 Condition Value Formation Process of Forced Input, Deletion and Disregard of Heterogeneous Data Processing
그림 Fig. 8 Sample of the Object to which 'SaltAndPepper Noise', an Image Noise Addition Method for Denoise Autoencoder (Above Right)
그림 Fig. 9 The Actual Predicted Value of the Heterogeneous Data Result by Smoking in Metabolic Syndrome (for Smoking) Data Preprocessing Procedure Diagram
그림 Fig. 10 Denoise AutoEncoder's Encoding Process for Data Compression and Parameter (W) per Epoch Transfer Process
그림 Fig. 11 Generation of Input Values (Smoking) and Output Values (Predicted Values) for the Execution of Neural Networks of Deep Neural Networks
그림 Fig. 12 Deep Learning-based Real Data for Disease Prediction (for Smoking) Comparison of Predicted Value Performance by Variable 10 by Adding Noise Level
그림 Fig. 13 Deep Learning-based Real Data for Disease Prediction (for Smoking) Comparison of Predicted Value Performance by Variable 40 by Adding Noise Level

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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