首页|Data on Machine Learning Reported by Luca Marzano and Colleagues (Exploring Hosp ital Overcrowding with an Explainable Time-to-Event Machine Learning Approach)
Data on Machine Learning Reported by Luca Marzano and Colleagues (Exploring Hosp ital Overcrowding with an Explainable Time-to-Event Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Stockholm, Sweden, by NewsRx correspondents, research stated, "Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hos pital departments. Leveraging ED real-world production data provides a unique op portunity to comprehend this multifaceted problem holistically." Our news journalists obtained a quote from the research, "This paper introduces a novel approach to analyse healthcare production data, treating the length of s tay of patients, and the follow up decision regarding discharge or admission to the hospital as atime-to-event analysis problem. Our methodology employs tradit ional survival estimators and machine learning models, and Shapley additive expl anations values to interpret the model outcomes. The most relevant features infl uencing length of stay were whether the patient received a scan at the ED, emerg ency room urgent visit, age, triage level, and the medical alarm unit category. The clinical insights derived from the explanation of the models holds promise f or increase understanding of the overcrowding from the data."
StockholmSwedenEuropeCyborgsEmer ging TechnologiesHospitalsMachine Learning
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