首页|Research Study Findings from University Health Network Update Understanding of M achine Learning (Prediction of Social Engagement in Long-Term Care Homes by Sex: A Population-Based Analysis Using Machine Learning)
Research Study Findings from University Health Network Update Understanding of M achine Learning (Prediction of Social Engagement in Long-Term Care Homes by Sex: A Population-Based Analysis Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting originating from Toronto, Ca nada, by NewsRx correspondents, research stated, "The objective of this study wa s to use population-based clinical assessment data to build and evaluate machine -learning models for predicting social engagement among female and male resident s of long-term care (LTC) homes." Funders for this research include Ices; Ontario Ministry of Health; Ministry of Long-term Care; Walter & Maria Schroeder Institute. The news editors obtained a quote from the research from University Health Netwo rk: "Routine clinical assessments from 203,970 unique residents in 647 LTC homes in Ontario, Canada, collected between April 1, 2010, and March 31, 2020, were u sed to build predictive models for the Index of Social Engagement (ISE) using a data-driven machine-learning approach. General and sex-specific models were buil t to predict the ISE. The models showed a moderate prediction ability, with rand om forest emerging as the optimal model. Mean absolute errors were 0.71 and 0.73 in females and males, respectively, using general models and 0.69 and 0.73 usin g sex-specific models."
University Health NetworkTorontoCana daNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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