Professor of Biomedical Informatics
My long-term research interest is to accelerate
clinical and translational science using electronic data while
minimizing study design biases and optimizing study results’
generalizability. My approach combines formal methods and
socio-technical approaches. I combine text knowledge engineering
and health data analytics to improve the efficiency and
generalizability of clinical research. My co-authors and I have
created a distribution-based method for quantifying the
collective generalizability of multiple clinical trials and a
novel generalizability index for study traits (GIST), which have enabled scalable and
proactive clinical study generalizability assessment. My team
also explore the symbiosis between knowledge representation and
natural language processing for text knowledge engineering, as
reflected in our work on EliXR. I aim to advance the field of
clinical research informatics on several fronts, including text
knowledge engineering, aggregate analysis of clinical studies,
quality-aware computational reuse of electronic patient data and
public data, and clinical research workflow optimization in
patient care settings towards the achievement of a learning
health system. Currently I also spend a significant amount of my
time leading the Columbia eMERGE
project, as part of a national eMERGE network. I also
participate in the Biomedical
Data Translator consortia.
My current research focus on these areas:
Openings for research officer, postdoc, and research assistant are available immediately to model patient populations and quantify the population representativeness of clinical studies using electronic data sources. Highly motivated individuals with computing background and quantitative analytical skills are encouraged to apply. Prospective candidates can email a CV to me with “job application” in the subject line.
JH, Xie G, Yuan C, Ena L, Li Z, Goldstein A, Huang L, Wang L,
Shen F, Liu H, Mehl K, Groopman E, Marasa M, Kiryluk K,
Gharavi AG, Chung WK, Hripcsak G, Friedman C, Weng C*,
Deep phenotyping on electronic health records facilitates genetic diagnosis by clinical exomes, American Journal of Human Genetics, 2018 Jul 5;103(1):58-73. doi: 10.1016/j.ajhg.2018.05.010. Epub 2018 Jun 28. PMID: 29961570 (*: equal-contribution corresponding author).
Yuan C, Ryan PB, Ta C, Guo Y, Li Z, Hardin J, Markadia R, Jin P, Shang N, Kang T, Weng C, Criteria2Query: A Natural Language Interface to Clinical Databases for Cohort Definition,
J Am Med Inform Assoc, in press, 2019. [Link to paper]
Goldstein A, Venker E, Weng C, Evidence Appraisal: A Scoping Review, Conceptual Framework, and Research Agenda, J Am Med Inform Assoc, 2017.
Sen A, Goldstein A, Chakrabarti S, Shang N, Kang T, Yaman A, Ryan P, Weng C, The Representativeness of Eligible Patients in Type 2 Diabetes Trials: A Case Study Using GIST 2.0. J Am Med Inform Assoc, 2017.
Kang T, Zhang S, Tang Y, Hruby GW, Rusanov A, Elhadad N, Weng C, EliIE: An Open-Source Information Extraction System for Clinical Trial Eligibility Criteria, J Am Med Inform Assoc, 2017 Apr 1. doi: 10.1093/jamia/ocx019. [Epub ahead of print] PMID: 28379377.
Miotto R, Weng C, Case-based Reasoning Using Electronic Health Records Identifies Eligible Patients for Clinical Trials, Journal of American Medical Informatics Association, 2015.
Acknowledgment: Dr. Weng thanks the National Library of Medicine, National Human Genome Research Institute, and National Center for Advancing Translational Science for funding her research.
Chunhua Weng, Ph.D., FACMI
Professor of Biomedical Informatics
622 West 168 Street, PH-20-room 407
New York, NY 10032
University email account (@columbia.edu): chunhua
Last Updated: 07-2019