Researcher’s Profile

Nicholas P. Tatonetti, PhD

Research Statement: 

We are making drugs safer through the analysis of data. Everyday millions of us or our loved ones take medications to manage our health. We trust in these prescriptions to improve our lives and give us hope for a healthier future. Often, however, these drugs have harmful side effects or dangerous interactions. Adverse drug reactions are experienced by millions of patients each year and cost the healthcare industry billions of dollars. In the Tatonetti Lab we use advanced data science methods, including artificial intelligence and machine learning, to investigate these medicines. Using emerging resources, such as electronic health records (EHR) and genomics databases, we are working to identify for whom these drugs will be safe and effective and for whom they will not.


Hao, Yun, Kayla Quinnies, Ronald Realubit, Charles Karan, and Nicholas P. Tatonetti. 2018. “Tissue‐Specific Analysis of Pharmacological Pathways.” CPT: Pharmacometrics & Systems Pharmacology 7 (7). John Wiley and Sons Inc. 453–63. doi:10.1002/psp4.12305.

Tatonetti, Nicholas P. 2018. “The next Generation of Drug Safety Science: Coupling Detection, Corroboration, and Validation to Discover Novel Drug Effects and Drug-Drug Interactions.” Clinical Pharmacology and Therapeutics 103 (2): 177–79. doi:10.1002/cpt.949.

Alexandre Yahi, Rami Vanguri, Noemie Elhadad, Nicholas P Tatonetti, Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories 31st Conference on Neural Information Processing Systems (NIPS 2017) December 2017

Nenad Macesic, Fernanda Polubriaginof, Nicholas P Tatonetti Machine learning: novel bioinformatics approaches for combating antimicrobial resistance Current opinion in infectious diseases December 2017

Robert Moskovitch, Fernanda Polubriaginof, Aviram Weiss, Patrick Ryan, Nicholas Tatonetti Procedure prediction from symbolic electronic health records via time intervals analytics Journal of biomedical informatics November 2017