Yejin Jaylynn Kim
Assistant Professor
School of Biomedical Informatics
University of Texas Health Science Center at Houston
7000 Fannin, Suite 600
Houston, Texas 77030
Yejin Kim is an assistant professor in the School of Biomedical Informatics at University of Texas Health Science Center (UTHealth) and a founding member of Center for Secure Artificial Intelligence for Healthcare since 2018. Her research interest is on data mining and machine learning for healthcare applications, particularly computational phenotyping with electronic health records (EHRs).
She received her Ph.D. in Computer Science at Pohang University of Science and Technology (Big data and Artificial Intelligence Lab) and B.S in Industrial Engineering at Pohang University of Science and Technology. She was a visiting scholar at University of California San Diego advised by Dr. Xiaoqian Jiang.
Research Interests
- Heterogeneous treatment effect analysis
- Computational phenotyping with longitudinal data
- Sequential decision making models
Service
- Reviewer, JAMA Open Network, Nature Scientific Reports, J. Biomedical Informatics, J. Am. Medical Informatics, Knowledge and Information Syste
- Program Comittee Member, 2021 IJCAI
- Program Comittee Member, 2021 AAAI
- Program Comittee Member, 2020 BIBM IEEE International Conference on Bioinformatics & Biomedicine
- Program Committee Member, 2020 AMIA Knowledge Discovery and Data Mining Working Group
- Program Committee Member, [2020 KDD Workshop on Applied Data Science for Healthcare]
- Program Committee Member, IEEE International Conference on Healthcare Informatics. 2020
- Co-organizer, SBMI Machine Learning Hackathon 2019-
- Program Committee Member, AAAI 2020
- Program Committee Member, 2020 WSDM Health Day
- Program Committee Member, 2019 KDD Workshop on Applied Data Science for Healthcare
- Program Committee Member, 2019 IEEE BIBM International Conference on Bioinformatics and Biomedicine
- Reviewer, AMIA 2019 Annual Symposium
- Program Committee Member, AAAI 2019
- Reviewer, AMIA 2018 Annual Symposium
- Reviewer, World Wide Web Conference 2018
- Reviewer, AMIA 2017 Annual Symposium
- Program Committee Member, ACM-BCB 2017 Conference on Bioinformatics, Computational Biology, and Health Informatics
Selected publications
Full publication list is available at google scholar
- Yejin Kim, Jessika Suescun, Mya Schiess, Xiaoqian Jiang. Computational medication regimen for Parkinson’s disease using reinforcement learning. Nature Scientific Reports. 2021. [paper]
- Yejin Kim, Xiaoqian Jiang, Samden D Lhatoo, Guo-Qiang Zhang, Shiqiang Tao, Licong Cui, Xiaojin Li, Robert D Jolly 3rd, Luyao Chen, Michael Phan, Cung Ha, Marijane Detranaltes, Jiajie Zhang. A community effort for automatic detection of postictal generalized EEG suppressioin in epilepsy. BMC Medical Informatics and Decision Making. 2020. [paper]
- Yejin Kim, Shuyu Zheng, Jing Tang, W. Jim Zheng, Zhao Li, Xiaoqian Jiang. Anticancer drug synergy prediction in understudied tissues using transfer learning. Journal of American Medical Informatics. 2020. [paper]
- Yejin Kim, Samden D Lhatoo, GQ Zhang, Luyao Chen, Xiaoqian Jiang. Temporal Phenotyping for Transitional Disease Progress: an Application to Epilepsy and Alzheimer’s Disease. Journal of Biomedical Informatics. 2020 [paper]
- Yejin Kim, Xiaoqian Jiang, Luca Giancardo, Danilo Pena, Avram S. Bukhbinder, Albert Y. Amran, and Paul E. Schulz. Multimodal Phenotyping of Alzheimer’s Disease with Longitudinal Magnetic Resonance Imaging and Cognitive Function Data. Scientific Reports, 2020 [paper]
- Yejin Kim, Xiaoqian Jiang, Luyao Chen, Xiaojin Li, Licong Cui. Discriminative Sleep Patterns of Alzheimer’s Disease via Tensor Factorization. AMIA Annual Symposium. 2019. [paper]
- Yejin Kim, Kwangseob Kim, Chanyoung Park, Hwanjo Yu. Sequential and Diverse Recommendation with Long Tail. IJCAI 2019. [paper] [slides]
- Yejin Kim, Luca Giancardo, Danilo Pena, Xiaoqian Jiang. Finding Discriminative Subgroups of Brain Regions using Tensor Factorization. ACM KDD workshop on Applied Data Science for Healthcare. 2019. [paper]
- Jingyun Choi, Yejin Kim, HS Kim, In Young Choi, Hwanjo Yu, Phenotyping of Korean patients with better-than-expected efficacy of moderate-intensity statins using tensor factorization, PLoS One. 2018. [paper]
- Yejin Kim, Jimeng Sun, Hwanjo Yu, and Xiaoqian Jiang. Federated Tensor Factorization for Computational Phenotyping. KDD 17 [paper] [slides] [video]
- Yejin Kim, Jingyun Choi , Yosep Chong, Xiaoqian Jiang, and Hwanjo Yu. DiagTree: Diagnostic Tree for Differential Diagnosis. CIKM 17 [paper] [slides]
- Yejin Kim, Robert El-Kareh, Jimeng Sun, Hwanjo Yu, and Xiaoqian Jiang. Discriminative and distinct phenotype by constrained tensor factorization. Scientific Reports 2017. [paper] [slides]
- Jingyun Choi, Mi Jung Rho, Yejin Kim, In Hye Yook, Hwanjo Yu, Dai-Jin Kim, In Young Choi, Smartphone Over-dependence Classification using Tensor Factorization. PLoS One 2017. [paper]
- Jingyun Choi, Yejin Kim, HS Kim, In Young Choi, Hwanjo Yu, Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins. ACM BCB 17 [paper]
- Yejin Kim, YongHyun Park, JY Lee, In Young Choi, Hwanjo Yu. Discovery of prostate specic antigen pattern to predict castration resistant prostate cancer of androgen deprivation therapy. BMC Decision Making and Medical Informatics 2016 [paper] [slides]
- YongHyun Park, Yejin Kim, Hwanjo Yu, In Young Choi, Byun SS, Kwak C, Chung BH, Lee HM, Kim CS, Lee JY. Is lymphovascular invasion a powerful predictor for biochemical recurrence in pT3 N0 prostate cancer? Results from the K-CaP database. Scientific reports. 2016 [paper]
- Yejin Kim, JE Jeong, H Cho , DJ Jung, M Kwak, MJ Rho, Hwanjo Yu, Dai-Jin Kim, In Young Choi. Personality factors predicting smartphone addiction predisposition: behavioral inhibition and activation systems, impulsivity, and self-control. PLoS One. 2016 [paper]
Selected presentation
- “Population-based Drug Repurposing to Treat Thrombosis in COVID-19 Patients”. ISC 2021 ePoster
- “Literature Mining Approach to Extract Drug Effectiveness in Clinical Trials. An Application to Alzheimer’s Disease” AMIA Informatics Summit, 2021
- “Causal Pathway to Analyze Racial Disparities in Alzheimer’s Disease and Related Dementia”, Alzheimer’s Association International Conference (AAIC), 2020 video
- “Influenza Vaccination is associated with a reduced incidence of Alzheimer’s Disease”, AAIC, 2020
- “Temporal Phenotyping for Transitional Disease Progress: an Application to Epilepsy and Alzheimer’s Disease”, WSDM Workshop Healthcare Day 2020, Houston, Texas
- “Discriminative Sleep Patterns of Alzheimer’s Disease via Tensor Factorization”, AMIA 2019, Washington D.C.
- “Optimized Medication Regimen for Parkinson’s Disease”, Rice Data Science Conference October 15, 2019, Houston, Texas
- “Sequential and Diverse Recommendation with Long Tail”, International Joint Conference on Artificial Intelligence August 14, 2019, Macau, China
- “Finding Discriminative Subgroups of Brain Regions using Tensor Factorization”, ACM KDD workshop on Applied Data Science for Healthcare. August 4, 2019, Anchorage, Alaska
- “Discovering Underlying Concepts with Tensor Factorization”, Center for Health Security and Phenotyping, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Jan 18, 2019, Houston, Texas
- “Interpretable and Federated Tensor Factorization for Computational Phenotyping”, Department of Computer Engineering, Hong Kong Baptist University, June 20 2018, Hong Kong
- “Tensor Factorization for Computational Phenotyping”, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Nov 28 2018, Houston, Texas
- “Data Science and Applications”, Department of Management Engineering, Ulsan National Institute of Science and Technology, May 29 2018, Ulsan, Korea
- “Data Science and Applications”, Korea University WISET, Sep 12 2018, Seoul, Korea
- “Tensor Factorization for Computational Phenotyping”, KCC 2017 Spotlight Session for Young Women Scholars Jun 21 2017, Jeju, Korea
- “Tutorial: Matrix/Tensor factorization and its applications”, Department of Medical Informatics, Catholic University of Korea, Nov 20 2017, Seoul, Korea
- “DiagTree: Diagnostic Tree for Differential Diagnosis”, The Korea Society of Medical Informatics, Nov 17 2017, Seoul, Korea
- “DiagTree: Diagnostic Tree for Differential Diagnosis”, International Conference on Information and Management, Nov 12 2017, Singapore
- “Federated Tensor Factorization for Computational Phenotyping (poster)”. The 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 14, 2017, Halifax, Nova Scotia, Canada
- “Discovery of prostate specific antigen pattern to predict castration resistant prostate cancer of androgen deprivation therapy”, ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics in conjunction with CIKM, Oct 23, 2015, Melbourne, Australia
Media Coverage
Alzheimer’s disease and Flu shot. NPR, CNN