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A photo of Yizhao Ni.

Member, Division of Biomedical Informatics

Assistant Professor, UC Department of PediatricsUC Department of Biomedical Informatics


Biography & Affiliation


Yizhao Ni’s research interest lies in the development of machine learning, natural language processing (NLP) and information retrieval techniques to assist clinical decision making. His research is application-oriented and the overall objective is to improve the quality of health care by: providing more effective provisioning of usable data (efficiency); helping clinicians generate more objective clinical decisions (effectiveness); and providing more reliable proactive prediction of clinical outcomes (safety). To achieve these objectives, he collaborates with clinical providers, information service administrators and biomedical and computational scientists.

Dr. Ni’s team has participated in a variety of research projects, including the Electronic Medical Records and Genomics Network (eMERGE) project, and , medication safety in intensive care units (R01), detection of surgery cancellation (R21), and sustainable surveillance of diabetes (U18). He has active collaborations with the divisions of Emergency Medicine, Hospital Medicine, Center for Autoimmune Genomics and Etiology, Neonatology & Pulmonary Biology, Anesthesia, Psychiatry and Oncology at Cincinnati Children’s; and with Neurology and Rehabilitation Medicine in the UC College of Medicine.

In addition to his research, Dr. Ni is serving as a machine learning specialist in multiple quality improvement projects such as the safety and situation awareness project.

Research Interests

Clinical informatics; natural language processing; machine learning (predictive modeling)

Academic Affiliation

Assistant Professor, UC Department of PediatricsUC Department of Biomedical Informatics


Biomedical Informatics

Science Blog


BSc: Xiamen University, Xiamen, PR China, 2005.

MSc: University College London, London, UK, 2006.

PhD: University of Southampton, Southampton, UK, 2010.

Post-doctoral: University of Bristol, Bristol, UK, 2012.

Certification: Epic Clarity Data Model, 2013.


Selected Publication

Automated Risk Assessment for School Violence: a Pilot Study. Barzman, D; Ni, Y; Griffey, M; Bachtel, A; Lin, K; Jackson, H; Sorter, M; DelBello, M. Psychiatric Quarterly. 2018; 89:817-828.

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit. Ni, Y; Lingren, T; Hall, ES; Leonard, M; Melton, K; Kirkendall, ES. Journal of the American Medical Informatics Association. 2018; 25:555-563.

Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis. Ni, Y; Alwell, K; Moomaw, CJ; Woo, D; Adeoye, O; Flaherty, ML; Ferioli, S; Mackey, J; La Rosa, FD L R; Martini, S; et al. PLoS ONE. 2018; 13:e0192586-e0192586.

Using Health Information Technology to Improve Safety in Neonatal Care A Systematic Review of the Literature. Melton, KR; Ni, Y; Tubbs-Cooley, HL; Walsh, KE. Clinics in Perinatology. 2017; 44:583-616.

Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department. Ni, Y; Beck, AF; Taylor, R; Dyas, J; Solti, I; Grupp-Phelan, J; Dexheimer, JW. Journal of the American Medical Informatics Association. 2016; 23:671-680.

An end-to-end hybrid algorithm for automated medication discrepancy detection. Li, Q; Spooner, SA; Kaiser, M; Lingren, N; Robbins, J; Lingren, T; Tang, H; Solti, I; Ni, Y. BMC Medical Informatics and Decision Making. 2015; 15:37-37.

Increasing the efficiency of trial-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients. Ni, Y; Wright, J; Perentesis, J; Lingren, T; Deleger, L; Kaiser, M; Kohane, I; Solti, I. BMC Medical Informatics and Decision Making. 2015; 15:28-28.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department. Ni, Y; Kennebeck, S; Dexheimer, JW; McAneney, CM; Tang, H; Lingren, T; Li, Q; Zhai, H; Solti, I. Journal of the American Medical Informatics Association. 2015; 22:166-178.

The Effect of Inversion at 8p23 on BLK Association with Lupus in Caucasian Population. Namjou, B; Ni, Y; Harley, IT W; Chepelev, I; Cobb, B; Kottyan, LC; Gaffney, PM; Guthridge, JM; Kaufman, K; Harley, JB. PLoS ONE. 2014; 9:e115614-e115614.

Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research. Deleger, L; Lingren, T; Ni, Y; Kaiser, M; Stoutenborough, L; Marsolo, K; Kouril, M; Molnar, K; Solti, I. Journal of Biomedical Informatics. 2014; 50:173-183.