YAMAC ALICAN ISIK
Yamac Alican Isik works closely with Duke Private Diagnostic Clinic (PDC) and Duke Health Technology Solutions (DHTS) on quality improvement projects for outpatient clinics. He holds a master’s in computer science and economics from Duke University and a master’s in econometrics from Barcelona Graduate School of Economics. After graduating from Duke, he worked as a data scientist for a Raleigh-based startup before finding his way back to Duke as a data science fellow. His current projects include optimization of patient appointments to reduce wait times and use of a real-time location system to improve clinic workflows via time series, temporal point process, and causal inference models. He has also played an essential role in developing the analysis that was used for back-to-work planning of essential research staff following the closure of many Duke labs in the early phase of the COVID-19.
Allison R. Young is analyzing data for use in research and publications, as well as contributing to the evaluation of predictive models used by the Duke University Health System. One of her primary projects explores ways in which big data from insurance claims can be used to analyze treatment effectiveness for patients hospitalized with COVID-19, as well as variations in their effectiveness for different patient subgroups. She is a communicator at heart, but with an analytical mind. She received a BA in journalism and an MPH from UNC-Chapel Hill before adding a master’s in interdisciplinary data science from Duke University. She is passionate about social determinants of health, heath equity, and ethical data science in research and in practice. She is excited and thankful for the opportunity to grow in these areas through AI Health at Duke.
Hamed Zaribafzadeh works with the Duke Surgery Learning Health Unit (LHU) and Laboratory for Transformative Administration (LTA) on several projects. His current projects include developing a machine learning model to predict requiring of small bowel obstruction (SBO) surgery for patients admitted to the emergency department and reduce length of stay. The goal of his other projects include the ability to predict surgery case length, operating room usage, length of stay, and resource allocation to improve patient care. He holds a BS in mechanical engineering from Isfahan University of Technology and an MS in biomedical engineering from Amirkabir University of Technology. He is also interested in predictive analytics, informed strategic planning, and resource and personnel management.
Connor Davis is working on projects to predict admissions from Duke Emergency Departments, identify significant risk factors for patients with COVID-19, and isolate data elements from patient electronic health records that are related to solid organ transplants. After graduating from UNC-Chapel Hill with degrees in biology and English, he received an MS in biomedical engineering from Duke. During his graduate program, he began working with the Duke Institute for Health Innovation as an intern for the Woo Center for Big Data and Precision Health. Soon after graduating, he began the fellowship program with AI Health. His additional interests include exploring methods for implementing machine learning technologies into healthcare workflows.
Xinghong Tang is part of the Pediatric Complex Care Integration (PCCI) Learning Health Project, where she is responsible for constructing the pipeline for predictive modeling, designing quantitative evaluations for program implementation, and developing a clinical decision-support tool using the Tableau data visualization suite. She received an MS in biomedical engineering from Duke University and a BS in materials science and engineering from UCLA. Before becoming an AI Health Fellow, she worked as a Health Data Scientist Intern at Duke Clinical Research Institute where she developed models for the Medicare Shared Savings Program (MSSP) Project. Her interest in data science stems from her desire to apply artificial intelligence in many different areas. She believes AI will revolutionize the healthcare industry and exhibit its power in medical applications.
Gavin Karr is helping the Duke Department of Neurosurgery to develop an artificial intelligence model capable of assisting staff in the emergency department with the triage of patients suspected of having a traumatic brain injury. His primary goal is to be able to predict the risk of events such as a patient’s need for surgery and chance of experiencing a stroke. He uses patient CT scans and electronic health records in his model to form these predictions. He attended the Rose-Hulman Institute of Technology, where he received his bachelor’s in computer engineering and an MS in electrical engineering. His interests include the use of artificial intelligence and machine learning to summarize, describe, or manipulate image and audio data for the purpose of assisting medical professionals in learning more about their patient populations.