首页|Studies from Mississippi State University Reveal New Findings on Machine Learnin g (A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Ca usal Effects: Applications To a Critical Care Trial)
Studies from Mississippi State University Reveal New Findings on Machine Learnin g (A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Ca usal Effects: Applications To a Critical Care Trial)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Machine Learning is now available. According to news reporting out ofMississippi State, Mississippi, b y NewsRx editors, research stated, “Assessing heterogeneity in the effectsof tr eatments has become increasingly popular in the field of causal inference and ca rries importantimplications for clinical decision-making. While extensive liter ature exists for studying treatment effectheterogeneity when outcomes are fully observed, there has been limited development in tools for estimatingheterogene ous causal effects when patient-centered outcomes are truncated by a terminal ev ent, such asdeath.”
Mississippi StateMississippiUnited S tatesNorth and Central AmericaCritical Care MedicineCyborgsEmerging Tech nologiesHealth and MedicineMachine LearningMississippi State University
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