DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yang, Eunho | - |
dc.contributor.advisor | 양은호 | - |
dc.contributor.author | Chung, Ingyo | - |
dc.date.accessioned | 2021-05-13T19:32:32Z | - |
dc.date.available | 2021-05-13T19:32:32Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=911012&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/284678 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전산학부, 2020.2,[iv, 25 p. :] | - |
dc.description.abstract | We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN). | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Time-series prediction▼aMixed effect model▼aGaussian process▼aRecurrent neural network▼ahealthcare | - |
dc.subject | 시계열 예측▼a혼합 효과 모델▼a가우시안 프로세스▼a순환 인공 신경망▼a헬스케어 | - |
dc.title | Deep mixed effect model using gaussian processes | - |
dc.title.alternative | 가우시안 프로세스를 이용한 심층 혼합 효과 모델 : 의료 분야에서 신뢰 가능하고 개인화된 예측 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 정인교 | - |
dc.title.subtitle | a personalized and reliable prediction for healthcare | - |
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