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Sunghwan Sohn, Ph.D., has expertise in mining large-scale electronic health records (EHRs) to unlock unstructured and hidden information through natural language processing (NLP) and machine learning techniques. Dr. Sohn develops strategies for the best use of informatics, ranging from precision medicine to population health, in order to achieve better solutions for people.
Active medical device surveillance. Despite the release of a final rule by the Food and Drug Administration to establish the unique device identifier (UDI) system, its impact remains limited. This limited impact is due to the unavailability of UDIs in a structured format in EHRs or administrative claims data.
However, descriptions of medical devices and adverse outcomes are routinely recorded in the unstructured text of EHRs. Dr. Sohn's aim is to develop automated and scalable AI models using EHRs to promote safter medical device use. These AI models overcome the limitations of current passive device surveillance and enhance real-world evidence generation.
Dr. Sohn's research helps the best use of EHRs to solve clinical problems and improve public health. His work provides biomedical scientists and clinicians access to the rich yet untapped information embedded in clinical narratives. Leveraging AI-driven EHR data analytics, his work provides the healthcare community with the valuable insights needed for advancing clinical research and improving care to people.
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