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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210909T110000
DTEND;TZID=Asia/Seoul:20210909T120000
DTSTAMP:20260502T223510
CREATED:20210902T140000Z
LAST-MODIFIED:20210903T055016Z
UID:4981-1631185200-1631188800@www.ibs.re.kr
SUMMARY:COVID19 – Mathematical Modeling and Machine Learning
DESCRIPTION:Abstract \nThis presentation include the following two topics. First of all\, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore\, we validated the model with the conventional model to confirm its convergent nature. Next\, We also developed a machine learning model that predicts the mortality of infected patients by using basic patients information such as age\, residence\, comorbidity\, and past medical history. Furthermore\, we aim to establish a medical system that allows patients to check their own severity\, and informs them to visit the appropriate clinic center by referring to the past treatment details of other patients with similar severity.
URL:https://www.ibs.re.kr/bimag/event/covid19-mathematical-modeling-and-machine-learning/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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