Author: Chen, Kun

Suicide risk research is funded by NIH

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The National Institutes of Health awards a grant to UConn Health Center in support of our project entitled “Improving the Identification of Patients at Risk of Suicide” (R01-MH112148; PI: Robert Aseltine). Chen is the PI on sub-award at UConn. The project aims to build a data-driven suicide prevention framework, by utilizing data from disparate sources in healthcare system and performing integrative statistical learning.

Chen delivered a short course at the 2017 ICSA Applied Statistical Symposium

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Dr. Kun Chen and Dr. Dingfeng Jiang offered a short course titled “Integrative Multivariate Statistical Learning in Healthcare Research with Real-World Data” on April 25 at the 2017 ICSA Applied Statistical Symposium, Chicago, IL. The course was designed to bridge the academic research and industrial practice on utilizing real-work data. The course was well attended by researchers from both academia and industry.

 

Abstract

This short course starts with an overview of recent problems arising from healthcare studies with large-scale heterogeneous data; examples include pragmatic trial, drug development, outcome research, suicide prevention, and opioid abuse. In these problems, a common scheme is that measurements of several distinct yet interrelated characteristics pertaining to a single set of subjects are collected from an array of disparate sources. For example, individual health data may come from insurance claims, pharmacy visits, clinical records, patient surveys, and government statistics. The availability of such complex data makes tackling many scientific and practical problems possible through “integrative statistical learning”, which is undergone exciting development and is pushing for a refinement of the conventional multivariate learning toolkit. In this short course, several classes of multivariate learning techniques for simultaneous dimension reduction, feature selection and model estimation will be introduced, together with discussions of several practical case studies in healthcare. The course consists of 4 modules: 1) overview of problems and statistical challenges in healthcare studies with big data; 2) integrative multivariate data reduction techniques with case studies; 3) integrative predictive modeling techniques with case studies; 4) recent developments on multi-view data fusion. The participants will have the opportunity to go through examples using newly developed R packages.

Instructor Bio:

Dr. Kun Chen is an Assistant Professor in the Department of Statistics, University of Connecticut (UConn), and a Research Fellow at the Center for Public Health and Health Policy, UConn Health Center. Chen’s research interests include multivariate statistical learning, high-dimensional statistics, and health informatics with large-scale heterogeneous data. He has extensive interdisciplinary research experience in a variety of fields including insurance, ecology, biology, agriculture, medical imaging, and public health. Chen’s research projects have received funding from the National Institutes of Health, the Simons Foundation, the National Science Foundation, etc. Currently he is involved in a data-driven suicide prevention study through integrating big data from disparate sources. Chen was a Co-Editor of the 2015 ICSA Symposium Proceeding Book, and serves as an Associate Editor of Sankhya: The Indian Journal of Statistics since 2016. He was recognized for Teaching Excellence at UConn for multiple times.

Dr. Dingfeng Jiang is a statistical manager at AbbVie Inc. Jiang’s research interest include high-dimensional statistics, variable selection, and causal inference in observational studies. He has extensive research experience in designing observational outcome research using big healthcare data, with application in diabetes, oncology and immunology therapy areas. He has served as reviewers for multiple statistical journals. He is an editorial board member for Heliyon, an open access journal published by Elsevier.

Short course at NESS 2017

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Dr. Robert Aseltine and Dr. Kun Chen will offer a short course entitled “Practical Integrative Statistical Learning: Recent Developments and Case Studies” at the 2017 New England Statistics Symposium (NESS), to be held at UConn, April 21-22.

http://ness.stat.uconn.edu/short-courses#aseltine

This short course focuses on practical predictive modeling and statistical learning techniques for analyzing large-scale heterogeneous data. In many fields, measurements of several distinct yet interrelated sets of characteristics pertaining to a single set of subjects and possibly collected from an array of sources, has become increasingly common. For example, individual health data may come from insurance claims, pharmacy visits, clinical records, patient surveys, and government statistics. The availability of such complex data makes tackling many fundamental scientific problems possible through “integrative statistical learning”, which is undergoing exciting development and is pushing for a genuine refinement of the conventional multivariate learning toolkit. In this course, several classes of interpretable predictive modeling techniques for simultaneous dimension reduction, feature construction and model estimation will be introduced. Practical case studies in health informatics regarding suicide prevention, drug abuse, race and ethnic disparities in health outcomes, etc, together with examples from insurance, finance and industrial engineering will be discussed. The course consists of 4 modules: 1) overview of integrative statistical learning and health informatics; 2) dimension reduction techniques with case studies; 3) integrative predictive modeling techniques with case studies; 4) More recent developments on data fusion and demonstrations with R.