A photo of Yizhao Ni.

Member, Division of Biomedical Informatics

Assistant Professor, UC Department of PediatricsUC Department of Biomedical Informatics


Biography & Affiliation


My greatest areas of interest are machine learning and natural language processing (NLP), and their applications in clinical informatics. I have more than 15 years of research experience in machine learning and NLP. I began my work at Cincinnati Children’s in 2012. Using these technologies, I want to improve the efficiency, effectiveness and safety of healthcare across the nation.

I lead the Design, Analytics, Integration (dAIn) Program at the Division of Biomedical Informatics (BMI) to facilitate the implementation and integration of artificial intelligence (AI) solutions at Cincinnati Children’s. My team collaborates with clinical providers, information service engineers, and biomedical and computational scientists. The ultimate goal is to advance clinical informatics with state-of-the-art AI technologies across clinical divisions. I have active collaborations with over 15 clinical divisions at Cincinnati Children’s and the University of Cincinnati, including:

  • Anesthesia
  • Biostatistics and Epidemiology
  • Center for Clinical & Translational Science & Training
  • Endocrinology
  • Emergency Medicine
  • Gastroenterology
  • General & Community Pediatrics
  • Heart Institute
  • Hospital Medicine
  • James M Anderson Center
  • Neonatology
  • Neurology
  • Oncology
  • Psychiatry
  • Psychology
  • Pulmonary Biology
  • Rehabilitation Medicine (CCHMC & University of Cincinnati)
  • The Center for Autoimmune Genomics and Etiology

My research is application-oriented, which aims at improving the quality of healthcare by providing usable data (efficiency), aiding clinicians in objective decision-making (effectiveness) and providing reliable and proactive prediction of clinical outcomes (safety). I designed an automated clinical trial eligibility screener© to efficiently assist with the recruitment of research participants. I also developed an automated risk assessment system for detecting subject potential for school violence.

I have led and participated in various research projects such as:

  • Automated risk assessment for school violence prevention (R01) 
  • A multisite study of psychiatric treatment on suicidal adolescents (PCORI)
  • Electronic Medical Records and Genomics Network (eMERGE) project (U01)
  • Sustainable surveillance of diabetes (U18)
  • Medication safety in intensive care units (R01)
  • Investigation of environmental contributions to rapid lung disease progression (R01)

In addition to my research, I’m a machine learning specialist for 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

Research Divisions

Biomedical Informatics

Blog Posts


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

DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity. Li, G; Iyer, B; Prasath, VB S; Ni, Y; Salomonis, N. 2020.

Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence. Ni, Y; Barzman, D; Bachtel, A; Griffey, M; Osborn, A; Sorter, M. International Journal of Medical Informatics. 2020; 139.

A Real-Time Automated Patient Screening System for Clinical Trials Eligibility in an Emergency Department: Design and Evaluation. Ni, Y; Bermudez, M; Kennebeck, S; Liddy-Hicks, S; Dexheimer, J. JMIR Medical Informatics. 2019; 7.

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 : JAMIA. 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.

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 : JAMIA. 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.

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.

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 : JAMIA. 2015; 22:166-178.