How can we use data and technology to reduce clinician burden in making decisions?
A central theme of my research is concerned with developing health informatics and data science algorithms that elicit actual healthcare practice patterns from data sources such as electronic health records (EHR), to provide data-driven evidence to inform clinical decision support. I am especially passionate about using my research to empower patients with chronic conditions whose data reflect their extremely complex long-term clinical needs. My work is funded by NIH, AHRQ and US-DOT.
I have lived in China (early childhood), Japan (tween to teenage years), and the US (since college). I enjoy spending time with my family in New York City.
Really enjoyed meeting old and new friends at AMIA in San Diego. Also presented our recent work: DICE and MyEDCare, published in JAMIA and ACI.
"Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups" out in JAMIA https://doi.org/10.1093/jamia/ocab203
Excited about the interests on our work on predictive model for postpartum depression - presenting it for discussion at Epic, University of Washington, UT Southwestern, and more!
Invited talk at ImproveCareNow Live Online Community Conference, Match 2021
Oral Presentation at AMIA Virtual Informatics Summit, Match 2021
My work was cited in ASHP Statement on the Use of Artificial Intelligence in Pharmacy
I was invited to give a presentation at Epic on the development of a postpartum depression model (paper below), Feb 2021
Editor's Choice! Zhang Y, Wang S, Hermann A, Joly R, Pathak J. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J Affect Disorders (Selected as Editor's Chioce).279:1-8.
I was invited to give a webinar on the Covid-19 dashboard at Hospital Association of New York State, May 2020
I was invited to attend the Women in Science Translational Research Symposium organized by Deerfield Management.
Our work in predicting postpartum depression was presented at Frontier of AI-Assisted Care (FAC) Scientific Symposium.
I am honored to be awarded a K01 award on Career Development in Biomedical Informatics and Data Science by the National Library of Medicine. Excited to be working in such an exciting field!
Our R03 study on the ordering patterns of opioids funded by AHRQ has begun. Look forward to the next 2 years!
Our work on predicting postpartum depression was featured in APA News Room.
I was invited to speak at the 1st Biomedical Big Data Symposium at New York City College of Technology
This Allscripts blog mentioned my work on order set development using machine learning
Presented in CTECH Annual Meeting, Davis, CA, Nov 2018
Presented in AMIA 2018 Annual Symposium, San Francisco, CA, Nov 2018
Goutham Rao, Katherine Kirley, Paul Epner, Yiye Zhang, Victoria Bauer, Ying Zhou, Anthony Solomonides. Identifying, Analyzing, and Visualizing Diagnostic Paths for Patients with Non-Specific Abdominal Pain. Applied Clinical Informatics 2018 (accepted)
My presentation in LinkedIn NYC Machine Learning and Data Science Meetup, Sep 2018
Presented "Developing and Maintaining Clinical Decision Support Using Clinical Knowledge and Machine Learning: The Case of Order Sets " at Machine Learning in Medicine. Ithaca, NY. Sep 2018
Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets (Journal of the American Medical Informatics Association, Aug 2018)
AI in Medicine
Clinical decision support, Clinical pathways, Care quality and safety
Web and mobile applications
SOCIAL DETERMINANTS OF HEALTH
Postpartum depression, prenatal care
July 2016 -
Assistant Professor, Walsh McDermott Scholar in Public Health
Division of Health Informatics, Department of Health Policy and Research
Weill Cornell Medicine
Weill Cornell Graduate School of Medical Sciences
Cornell University, New York, NY
April 2011 - March 2012
PhD 2011 – 2016 Information Systems and Management, Carnegie Mellon University
(Thesis Committee: Rema Padman, PhD; Larry Wasserman, PhD; Ole Mengshoel, PhD)
MS 2009 – 2011 Biostatistics, Columbia University
BA 2005 – 2009 Mathematics (major) & Biology (minor), Washington University in St. Louis
JOURNAL AND REFEREED CONFERENCE PROCEEDINGS
November 2009 - January 2011
Rao G, Kirley K, Epner P, Zhang Y, Bauer V, Padman R, Zhou Y, Solomonides A. Identifying, Analyzing, and Visualizing Diagnostic Paths for Patients with Nonspecific Abdominal Pain. Appl Clin Inform. 2018 Oct;9(4):905-913.
Zhang Y, Trepp R, Wang W, Luna J, Vawdrey DK, Tiase V. Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets. J Am Med Inform Assoc. 2018 Aug 7.
Zhang Y, Padman R, Epner P, Bauer V, Solomonides A, Rao G. Identifying Diagnostic Paths for Undifferentiated Abdominal Pain from Electronic Health Record Data. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:290-299.
Ancker JS, Kim MH, Zhang Y, Zhang Y, Pathak J. The potential value of social determinants of health in predicting health outcomes. J Am Med Inform Assoc. 2018 Aug 1;25(8):1109-1110.
Thomas D, Chung C, Zhang Y, Te A, Gratzke C, Woo H, Chughtai B. Clinical Trials in Benign Prostatic Hyperplasia: A Moving Target of Success. Eur Urol Focus. 2018 May 24.
Gartner D, Zhang Y, Padman R. Cognitive workload reduction in hospital information systems : Decision support for order set optimization. Health Care Manag Sci. 2018 Jun;21(2):224-243.
Zhang Y, John Richard Lee, Bilal Chughtai, Rema Padman. Variable selection for chronic disease outcome prediction using a causal inference technique: A preliminary study. 2018 IEEE International Conference on Healthcare Informatics, New York, NY, 2018
Zhang Y, Padman R. Data-driven Order Set Development Using Meta-Heuristic Optimization. 15th Conference on Artificial Intelligence in Medicine (AIME 2015). Pavia, Italy, 2015.
Zhang Y, Padman R, Wasserman L, Patel N, Teredesai P, Xie Q. On Clinical Pathway Discovery from Electronic Health Record Data. IEEE Intelligent Systems. 2015;30(1):70-5.
Gartner D, Zhang Y, Padman R. Workload Reduction Through Usability Improvement of Hospital Information Systems - The Case of Order Set Optimization. 36th International Conference on Information Systems (ICIS). Fort Worth, TX, 2015.
Zhang Y, Padman R, Levin JE. Paving the COWpath: data-driven design of pediatric order sets. Journal of American Medical Informatics Association (JAMIA). 2014 Oct;21(e2):e304-11.
Zhang Y, Padman R, L. Wasserman. On Learning and Visualizing Practice-based Clinical Pathways for Chronic Kidney Disease. American Medical Informatics Association (AMIA) 2014 Annual Symposium. Washington, DC, 2014. (2nd place, KDDM Student Paper Competition)
Zhang Y, Padman R, L. Wasserman. On Learning Clinical Pathways for Chronic Kidney Disease from Electronic Health Record Data: A Preliminary Graphical Approach. 2nd International Conference on Big Data and Analytics in Healthcare (BDAH). Singapore, 2014. (Best paper Runner-up)
Zhang Y, Padman R, Levin JE. Reducing Provider Cognitive Workload in CPOE use: Optimizing Order Sets. 14th World Congress on Health and Biomedical Informatics (MEDINFO). Copenhagen, Denmark, 2013. (Finalist, Student paper competition)
Zhang Y, Padman R, Levin JE. Toward Order Set Optimization Using Click Cost Criteria in the Pediatric Environment. 46th Hawaii International Conference on System Sciences (HICSS-46). Maui, HI, 2013.
Zhang Y, Padman R, Levin JE. Clustering Methods for Data-driven Order Set Development in the Pediatric Environment. 7th INFORMS Workshop on Data Mining and Healthcare Informatics. Phoenix, AZ, 2012.
Zhang Y, Padman R, Levin JE. Data-driven order set generation and evaluation in the pediatric environment. American Medical Informatics Association (AMIA) 2012 Annual Symposium. Chicago, IL, 2012. (Selected as ‘Hot Pick’ for the conference)
Yao L, Zhang Y, Li Y, Sanseau P, Agarwal P. Electronic health records: Implications for drug discovery. Drug Discovery Today. 2011 Jul;16(13-14):594-9.
HONORS AND AWARDS
June 2012 - May 2014
First Place, Weill Cornell Medicine BioVenture eLab Biomedical Business Plan Challenge, 2019
Walsh McDermott Scholar in Public Health, 2016-2019
Best Paper Award, International Conference on Decision Support System Technology 2016
Top rated poster, Mayo Clinic Delivery Science Summit, 2015
Doctoral Consortium at the 6th Annual Workshop on Health Information and Economics (WHITE), 2015
Honorable Mention, INFORMS Healthcare Poster Competition, 2015
Big Data Coursework for Computational Medicine (BDC4CM) Fellowship, 2015
Second place, American Medical Informatics Association (AMIA) Knowledge Discovery and Data Mining (KDDM) Student Paper Competition, 2014
Best Paper Runner Up, the 2nd International Conference on Big Data and Analytics in Healthcare (BDAH), 2014
Finalist, Student Paper Competition at the 14th World Congress on Health and Biomedical Informatics (MEDINFO), 2013
April 2011 - March 2012
IBM Research, September 2016
Robert Smith School of Business, University of Maryland, College Park, 2017
Value Institute of New York Presbyterian Hospital, August 2016, March 2018
LinkedIn, September 2018
November 2009 - January 2011
Current and Past Mentoring:
Faezeh Movahedi (PhD candidate), Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh
Chensu Xie (PhD candidate), Tri-Institute PhD program in Computational Medicine, Weill Cornell Graduate School of Medical Sciences, Cornell University
Shuojia Wang (PhD candidate), Department of Epidemiology, Zhejiang University
Yonaka Harris, New York City College of Technology
GET IN TOUCH
I am looking for a research assistant to join my team!
Weill Cornell Medicine, Department of Healthcare Policy and Research, Division of Health Informatics
425 E 61st St
New York, New York County 10065