Jay S Patel

Assistant Professor
Health Services Administration and Policy
1301 Cecil B. Moore Ave.


Dr. Jay S. Patel received his bachelor of dental surgery from Rajiv Gandhi University, a master of science in biomedical informatics from Rutgers University, and a PhD in health informatics from the School of Informatics and Computing and School of Dentistry at Indiana University Purdue University Indianapolis. Dr. Patel then completed an assistant research scientist fellowship at Temple University College of Public Health.

Dr. Patel’s research focuses on developing and implementing health information technology to improve patient care and outcomes. Dr. Patel’s lab focuses on two areas: 1) clinical research and 2) methodological research. His clinical and public health research is focused on developing electronic computational surveillance strategies to estimate the prevalence of diseases and their consequences, such as COVID-19, periodontal disease, and oral cancers. He has developed clinical decision support systems and learning health systems using electronic health records (EHR), administrative data, social media data, and social determinants of health to predict the risk of diseases before disease initiation and progression. He has also developed clinical natural language processing (NLP) methods to extract information from big social media data and EHR data. His methodological research focuses on the development of computational methods such as NLP and machine learning to 1) extract information from the free text of clinical notes and social media data, 2) apply machine learning methods to predict diseases and assess treatment outcomes, 3) develop clinical decision support systems to improve patient care and outcomes, 4) develop learning health systems to improve healthcare quality, 5) develop image processing methods to extract information from radiographs, and 6) apply human factors engineering methods to improve user-interface of EHR systems.


  • PhD, Health Informatics, Indiana University Purdue University Indianapolis
  • MS, Biomedical Informatics, Rutgers University
  • BDS, Dental Surgery, Rajiv Gandhi University

Curriculum Vitae 

Courses Taught




HIM 5102

Applications of Computer Programming in Health Informatics


HIM 5128

Health Data: Standards and Interoperability


Selected Publications

  • Patel, J.S., Zhan, S., Siddiqui, Z., Dzomba, B., & Wu, H. (2023). Automatic Identification of Self-Reported COVID-19 Vaccine Information from Vaccine Adverse Events Reporting System. Methods Inf Med. Germany. doi: 10.1055/s-0042-1760248

  • Patel, J.S., Brandon, R., Tellez, M., Albandar, J.M., Rao, R., Krois, J., & Wu, H. (2022). Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records. Methods Inf Med, 61(S 02), pp. e125-e133. Germany. doi: 10.1055/s-0042-1757880

  • Patel, J.S., Vo, H., Nguyen, A.n., Dzomba, B., & Wu, H. (2022). A Data-Driven Assessment of the U.S. Health Informatics Programs and Job Market. Appl Clin Inform, 13(2), pp. 327-338. Germany. doi: 10.1055/s-0042-1743242

  • Patel, J.S., Su, C., Tellez, M., Albandar, J.M., Rao, R., Iyer, V., Shi, E., & Wu, H. (2022). Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data. Front Artif Intell, 5, p. 979525. Switzerland. doi: 10.3389/frai.2022.979525

  • Li, S., Williams, K.S., Medam, J.K., Patel, J.S., Gonzalez, T., & Thyvalikakath, T.P. (2022). Retrospective Study of the Reasons and Time Involved for Dental Providers' Medical Consults. Front Digit Health, 4, p. 838538. Switzerland. doi: 10.3389/fdgth.2022.838538

  • Mehta, S., Wu, H., Srinivasan, S., Singhal, V., Mital, D., & Patel, J. (2022). Feasibility of automatic differential diagnosis of endodontic origin periapical lesions - a pilot study. International Journal of Medical Engineering and Informatics, 1(1), pp. 1-1. Inderscience Publishers. doi: 10.1504/ijmei.2022.10044462

  • Siddiqui, Z., Wang, Y., Patel, J., & Thyvalikakath, T. (2021). Differences in medication usage of dental patients by age, gender, race/ethnicity and insurance status. Technology and Health Care, 29(6), pp. 1099-1108. IOS Press. doi: 10.3233/thc-202171

  • Watson, J.I., Patel, J.S., Ramya, M.B., Capin, O., Diefenderfer, K.E., Thyvalikakath, T.P., & Cook, N.B. (2021). Longevity of Crown Margin Repairs Using Glass Ionomer Cement: A Retrospective Study. Oper Dent, 46(3), pp. 263-270. United States. doi: 10.2341/20-062-C

  • Patel, J., Siddiqui, Z., Krishnan, A., & Thyvalikakath, T. (2018). Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity. Methods of Information in Medicine, 57(05/06), pp. e2-e2. Georg Thieme Verlag KG. doi: 10.1055/s-0038-1675817

  • Holden, R., Binkheder, S., Patel, J., & Viernes, S. (2018). Best Practices for Health Informatician Involvement in Interprofessional Health Care Teams. Applied Clinical Informatics, 09(01), pp. 141-148. Georg Thieme Verlag KG. doi: 10.1055/s-0038-1626724

  • Patel, J.S., Siddiqui, Z., Krishnan, A., & Thyvalikakath, T. (2017). Identifying patients’ smoking status from electronic dental records data. Studies in Health Technology and Informatics. IOS Press.

  • Patel, J., Wang, Y., Siddiqui, Z., Krishnan, A., & Thyvalikakath, T. (2017). Extraction and evaluation of medication data from Electronic Dental Records. Studies in Health Technology and Informatics. IOS Press.

  • Oro, J.E.C.G.d.e., Koch, P.J., Krois, J., Ros, A.G.C., Patel, J., Meyer-Lueckel, H., & Schwendicke, F. Hyperparameter Tuning and Automatic Image Augmentation for Deep Learning-Based Angle Classification on Intraoral Photographs—A Retrospective Study. Diagnostics, 12(7), pp. 1526-1526. MDPI AG. doi: 10.3390/diagnostics12071526

  • Rohrer, C., Krois, J., Patel, J., Meyer-Lueckel, H., Rodrigues, J.A., & Schwendicke, F. Segmentation of Dental Restorations on Panoramic Radiographs Using Deep Learning. Diagnostics, 12(6), pp. 1316-1316. MDPI AG. doi: 10.3390/diagnostics12061316