데이터센터프로그래밍 강의 일환으로 University of Washington 박사과정에 있는 이대현님을 모시고 특강을 진행하니 많은 참석 바랍니다. 수강생이 아니더라도 1학년, 다전공, 부전공 등 학생들도 참석해 원하는바 얻어갈 수 있는 기회가 되었으면 합니다.
- 일시: 2018년 12월 17일, 13:20-14:50
- 장소: 전 B111
특강 제목 및 개요
제목: Predictive Approaches for Acute Adverse Events in Electronic Health Records
Since the late 1990s, medical error has been widely discussed as a significant societal burden, and it was the third leading cause of patient death in 2013. Although harm caused by medical professionals’ actions during clinical interventions has been addressed relatively well, patient harm caused by missing the required actions has been reported to be twice as high as the harm resulting from adverse reactions from clinical interventions. The patient harm caused by preventable complications is termed Failure-to-Rescue (FTR) and proposed as a patient safety indicator in hospitals.
The nationwide implementation of the electronic health record (EHR) in the United States has resulted in clinical data being accumulated at an unprecedented scale, enabling researchers to conduct large-scale, longitudinal clinical data analyses; also, many machine learning approaches have been applied to predict a patient’s risk for various adverse clinical outcomes, such as in-hospital mortality, discharge diagnoses, and the onset of clinically adverse events. Although these models could be utilized as a clinical decision support tool in clinical practice, there are several challenges that occur when building data-driven models for clinically adverse events, including estimation of target event time-of-onset and the interpretability of the models.
The goal of the doctoral dissertation is to illustrate the value of predictive modeling in clinical practice, here focusing on adverse clinical events that develop acutely. The present dissertation will also present case studies that show how predictions can be used for identifying FTR cases. If successful, the proposed system could expedite the implementation of predictive models in clinical practice that identify the common patterns appearing in cases where the existing clinical workflow cannot help, coming up with policy measures to deal with these cases in practice.
약력: 2006-2014 연세대학교 컴퓨터과학과 졸업
2014-Present Ph.D. Candidate, Biomedical and Health Informatics, University of Washington