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학술대회 Frequency-Aware Attention based LSTM Networks for Cardiovascular Disease
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저자
박흰돌, 한영웅, 최재훈
발행일
201810
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1503-1505
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539509
협약과제
18HS1500, 심혈관질환을 위한 인공지능 주치의 기술 개발, 김승환
초록
There are various medical features associated with cardiovascular disease in the EMR data, but the frequency of each medical feature is different. Less frequent feature may be considered as non-critical feature, although cardiovascular disease is closely associated in the cardiovascular disease risk prediction model. We propose a frequency-aware based Attention-based LSTM (FA-Attn-LSTM) that weighs on important medical features using an attention mechanism that considers the frequency of each medical feature. Our model predicts the risk for cardiovascular disease using the ejection fraction as a prediction target and shows RMSE = 3.65 and MAE = 2.49.
키워드
attention-based LSTM, cardiovascular disease, deep learning, EMR, time series analysis
KSP 제안 키워드
Attention mechanism, Cardiovascular diseases(CVD), Disease risk prediction, Ejection fraction, LSTM network, Time Series Analysis(TSA), cardiovascular disease risk, deep learning(DL), non-critical, prediction model