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https://www.selleckchem.com/products/s64315-mik665.html BACKGROUND Electronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioritization. Without prioritization, physicians must mentally identify and collate relevant data, an activity that can lead to cognitive overload. To mitigate cognitive overload, a Learning EMR (LEMR) system prioritizes the display of relevant medical record data. Relevant data are those that are pertinent to a context-defined as the combination of the user, clinical task, and patient case. To determine which data are relevant in a specific context, a LEMR system uses supervised machine learning models of physician information-seeking behavior. Since obtaining information-seeking behavior data via manual annotation is slow and expensive, automatic methods for capturing such data are needed. OBJECTIVE The goal of the research was to propose and evaluate eye tracking as a high-throughput method to automatically acquire physician information-seeking behavior useful for tiver operating characteristic curve (P=.40). CONCLUSIONS We used eye tracking to automatically capture physician information-seeking behavior and used it to train models for a LEMR system. The models that were trained using eye tracking performed like models that were trained using manual annotations. These results support further development of eye tracking as a high-throughput method for training clinical decision support systems that prioritize the display of relevant medical record data. ©Andrew J King, Gregory F Cooper, Gilles Clermont, Harry Hochheiser, Milos Hauskrecht, Dean F Sittig, Shyam Visweswaran. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 02.04.2020.BACKGROUND The worldwide expansion of preexposure prophylaxis (PrEP) with oral tenofovir-disoproxil-fumarate/emtricitabine will be critical to ending the HIV epidemic. However, maintaining daily adherence

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