A photo of Yizhao Ni.

Member, Division of Biomedical Informatics

Assistant Professor, UC Department of PediatricsUC Department of Biomedical Informatics

513-803-4269

Biography & Affiliation

Biography

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 10 clinical divisions at Cincinnati Children’s and the University of Cincinnati, including:

  • Anesthesia
  • Biostatistics and Epidemiology
  • Emergency Medicine
  • Heart Institute
  • Hospital Medicine
  • Neonatology
  • Neurology
  • Oncology
  • Psychiatry
  • Pulmonary Biology
  • Rehabilitation Medicine (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.

Our team has participated in various research projects such as:

  • 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

Divisions

Biomedical Informatics



Blog Posts

Cincinnati Children’s Launches 6 COVID-19 Research Projects

Infectious Diseases and Vaccines

Cincinnati Children’s Launches 6 COVID-19 Research Projects

Yizhao Ni, PhD, Ming Tan, PhD ...5/26/2020

How Artificial Intelligence Can Improve Clinical Trial Recruitment

Tools for Science

How Artificial Intelligence Can Improve Clinical Trial Recruitment

Yizhao Ni, PhD8/12/2019

Scientists Teaching Machines to Make Clinical Trials More Successful

Emergency and Critical Care

Scientists Teaching Machines to Make Clinical Trials More Successful

Yizhao Ni, PhD6/29/2019

Education

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.

Publications

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.