Electronic Health Records

AI2HEAL DATATHON 2022

Join our team

Electronic Health Record (EHR) is an electronic version of a patients medical history, that may include all of the key administrative clinical data relevant to that persons' care, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports. AI in EHRs is primarily applied for the improvement of data discovery, extraction, and personalized recommendations for treatments.

ehr

Team Leaders


Benjamin Schickle PhD

Assistant Director for AI Research, Intelligent Critical Care Center

Benjamin Shickel, PhD

Associate Professor, Department of Medicine, Dr. Shickel’s research focuses on applications of artificial intelligence and deep learning for enhanced clinical decision support using electronic health records data. He is interested in unifying patient data of multiple modalities for more comprehensive and personalized health representations, and in the human element of clinical AI, including explainability, fairness, causality, and hybrid systems integrating expert knowledge with data-driven methods.

Image of Dr. Baslanti

Associate Director For Research, Intelligent Critical Care Center

Tezcan Ozrazgat Baslanti, PhD

Associate Professor, Department of Medicine, Her research focuses on the use of AI in nephrology and critical care including reinforcement learning and phenotyping and prediction models related to kidney health and other hospital complications utilizing large electronic health records data.

Image of Dr. Luftus

Associate Director of Research, Intelligent Critical Care Center

Tyler Loftus, MD

Tyler Loftus, MD, is an assistant professor with the UF Department of Surgery and an acute care surgeon at UF Health Shands. His peer-reviewed publications and research presentations at national and international meetings have evolved from translational science focusing on bone marrow failure and anemia after traumatic injury to data science focusing on machine learning to augment personalized, patient-centered decision-making.