Qing T. Zeng, Ph.D.

Research Interests

  • Consumer Health Informatics
  • Semantic Integration of Information
  • Natural Language Processing

Languages

  • Chinese
  • English

Academic Information

  • Departments: Biomedical Informatics - Adjunct Professor

Academic Office Information

  • (801) 213-3357
  • Biomedical Informatics
    421 Wakara Way, Room: Suite 140
    Salt Lake City, UT 84108

Academic Bio


Qing T. Zeng earned her PhD degree in Medical Informatics from Columbia University. She is a Professor in the University of Utah’s Department of Biomedical Informatics, and an IDEA Center Principal Investigator at the University of Utah and the VA Hospital.

Her expertise is in clinical and consumer health information extraction, analysis, and presentation. In 2011, Dr. Zeng was elected Fellow of the American College of Medical Informatics. She is an associate editor of the journal Computers in Biology and Medicine and an editorial board member of the Journal of Biomedical Informatics. Three of Dr. Zeng’s papers were selected by the International Medical Informatics Association’s yearbooks.

Prior to joining the Biomedical Informatics Department in 2009, she was an Associate Professor at the Harvard Medical School. From 2008-2010, she chaired the American Medical Informatics Association, Consumer Health Working Group.

“When I applied to the medical Informatics PhD program in Columbia, a faculty told me many hospitals were less computerized than supermarkets. Now, Electronic Medical Records are commonplace, but informatics has not yet fundamentally changed how medicine is practiced. So, more work lies ahead.”

Education History

Type School Degree
Doctoral Training Columbia University
Medical Informatics
Ph.D.
Graduate Training Columbia University
Medical Informatics
M.Phil.
Graduate Training University of Hawaii
Information and computer science
M.S.
Undergraduate Beijing Polytechnic University
Computer Science
B.S.

Global Impact

Education History

Type School Degree Country
Undergraduate Beijing Polytechnic University
Computer Science
B.S. China

Selected Publications

Journal Article

  1. Gundlapalli AV, Redd D, Gibson B, Carter M, Korhonen C, Nebeker J, Samore M, Zeng-Treiltler Q (3/5/14). Exploring the Value of Free Text Queries for Patient Cohort Identification Using Electronic Medical Records. BMC Health Serv Res.
  2. Limbic system white matter microstructure and long-term treatment outcome in major depressive disorder: a diffusion tensor imaging study using legacy data.Hoogenboom WS, Perlis RH, Smoller JW, Zeng-Treitler Q, Gainer VS, Murphy SN, Churchill SE, Kohane IS, Shenton ME, Iosifescu DV (2014). Limbic system white matter microstructure and long-term treatment outcome in major depressive disorder: a diffusion tensor imaging study using legacy data. World J Biol Psychiatry, 15(2), 122-34.
  3. Text classification performance: is the sample size the only factor to be considered?Figueroa RL, Zeng-Treitler Q (2013). Text classification performance: is the sample size the only factor to be considered? Stud Health Technol Inform, 192, 1193.
  4. Synonym, topic model and predicate-based query expansion for retrieving clinical documents.Zeng QT, Redd D, Rindflesch T, Nebeker J (2012). Synonym, topic model and predicate-based query expansion for retrieving clinical documents. AMIA Annu Symp Proc, 2012, 1050-9.
  5. Active learning for clinical text classification: is it better than random sampling?Figueroa RL, Zeng-Treitler Q, Ngo LH, Goryachev S, Wiechmann EP (2012). Active learning for clinical text classification: is it better than random sampling? J Am Med Inform Assoc, 19(5), 809-16.
  6. Mining online social network data for biomedical research: a comparison of clinicians' and patients' perceptions about amyotrophic lateral sclerosis treatments.Nakamura C, Bromberg M, Bhargava S, Wicks P, Zeng-Treitler Q (2012). Mining online social network data for biomedical research: a comparison of clinicians' and patients' perceptions about amyotrophic lateral sclerosis treatments. J Med Internet Res, 14(3), e90.
  7. Qing T Zeng, Doug Redd, Guy Divita, SamahJarad, Cynthia Brandt, Jonathan R Nebeker (12/26/2011). Characterizing Clinical Text and Sublanguage: A Case Study of the VA Clinical Notes. J Health Med Informat.
  8. A bootstrapping algorithm to improve cohort identification using structured data.Kandula S, Zeng-Treitler Q, Chen L, Salomon WL, Bray BE (2011). A bootstrapping algorithm to improve cohort identification using structured data. J Biomed Inform, 44 Suppl 1, S63-8.
  9. Electronic medical records for discovery research in rheumatoid arthritis.Liao KP, Cai T, Gainer V, Goryachev S, Zeng-treitler Q, Raychaudhuri S, Szolovits P, Churchill S, Murphy S, Kohane I, Karlson EW, Plenge RM (2010). Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken), 62(8), 1120-7.
  10. Estimating consumer familiarity with health terminology: a context-based approach.Zeng-Treitler Q, Goryachev S, Tse T, Keselman A, Boxwala A (2008). Estimating consumer familiarity with health terminology: a context-based approach. J Am Med Inform Assoc, 15(3), 349-56.
  11. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.Zeng QT, Goryachev S, Weiss S, Sordo M, Murphy SN, Lazarus R (2006). Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak, 6, 30.

Conference Proceedings

  1. Redd D, Rindflesch T, Nebeker J, Zeng-Treitler Q (2013). Improve Retrieval Performance on Clinical Notes: A Comparison of Four Methods. The 46th Hawaii International International Conference onSystems Science (HICSS- 46 2013), 2389 - 2397.
  2. Proulx J, Kandula S, Hill B, Zeng-Treitler Q (2013). Creating Consumer Friendly Health Content: Implementing and Testing a Readability Diagnosis and Enhancement Tool. The 46th Hawaii International International Conference onSystems Science (HICSS- 46 2013), 2445-2453.
  3. Zeng-Treitler Q, Kandula S, Kim H, and Hill B (08/12/2012). A Method to Estimate Readibility of Health Content. ACM SIGKDD Workshop on Health Informatics (HI-KDD 2012), Beijing, China.
  4. Nakamura, C amp Zeng, Q (2011). The Pictogram Builder: Development and Testing of a System to Help Clinicians Illustrate Patient Education Materials. World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Chesapeake, VA: AACE, 324-330.
  5. Curtis, D, Kandula, S, Hill, B amp Zeng-Treitler, Q (2011). Using Topic Modeling to Identify Patients’ Educational Needs. World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Chesapeake, VA: AACE, 2342-2350.
  6. A semantic and syntactic text simplification tool for health content.Kandula S, Curtis D, Zeng-Treitler Q (2010). A semantic and syntactic text simplification tool for health content. AMIA Annu Symp Proc, United States, 2010, 366-70.
  7. Improving patient comprehension and recall of discharge instructions by supplementing free texts with pictographs.Zeng-Treitler Q, Kim H, Hunter M (2008). Improving patient comprehension and recall of discharge instructions by supplementing free texts with pictographs. AMIA Annu Symp Proc, United States, 849-53.

Case Report

  1. Zeng QT, Redd D, Divita G, Jarad S, Brandt C, Nebeker JR (2011). Characterizing Clinical Text and Sublanguage: A Case Study of the VA Clinical Notes. Journal of Health and Medical Informatics, S3, 1-9.

Poster

  1. Zeng QT, Divita G, DuVall SL, Samore MH, Nebeker JR (2012). V3NLP: Open Source NLP for Big Data. Poster session presented at National Library of Medicine (NLM) and National Institute of Biomedical Imaging and Bioengineering (NIBIB) workshop on Natural Language Processing: State of the Art, Future Directions and Applications for Enhancing Clinical Decision-Making, Bethesda, MD.

News

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