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https://www.selleckchem.com/products/derazantinib.html Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early. While fluid bolus therapy and vasopressors are common treatments, it is often unclear which interventions to give, in what amounts, and for how long. Observational data in the form of electronic health records can provide a source for helping inform these choices from past events, but often it is not possible to identify a single best strategy from observational data alone. In such situations, we argue it is important to expose the collection of plausible options to a provider. To this end, we develop SODA-RL Safely Optimized, Diverse, and Accurate Reinforcement Learning, to identify distinct treatment options that are supported in the data. We demonstrate SODA-RL on a cohort of 10,142 ICU stays where hypotension presented. Our learned policies perform comparably to the observed physician behaviors, while providing different, plausible alternatives for treatment decisions.The effective use of EHR data for clinical research is challenged by the lack of methodologic standards, transparency, and reproducibility. For example, our empirical analysis on clinical research ontologies and reporting standards found little-to-no informatics-related standards. To address these issues, our study aims to leverage natural language processing techniques to discover the reporting patterns and data abstraction methodologies for EHR-based clinical research. We conducted a case study using a collection of full articles of EHR-based population studies published using the Rochester Epidemiology Project infrastructure. Our investigation discovered an upward trend of reporting EHR-related research methodologies, good practice, and the use of informatics related methods. For example, among 1279 articles, 24.0% reported training for data abstraction, 6% reported the abstractors were blinded, 4.5% tested the inter-observer agre

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