首页|University of Naples 'Federico II' Researchers Publish New Studies and Findings in the Area of Machine Learning (Investigation of emergency department abandonme nt rates using machine learning algorithms in a single centre study)
University of Naples 'Federico II' Researchers Publish New Studies and Findings in the Area of Machine Learning (Investigation of emergency department abandonme nt rates using machine learning algorithms in a single centre study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news originating from the University of Naples “Federic o II” by NewsRx correspondents, research stated, “A critical problem that Emerge ncy Departments (EDs) must address is overcrowding, as it causes extended waitin g times and increased patient dissatisfaction, both of which are immediately lin ked to a greater number of patients who leave the ED early, without any evaluati on by a healthcare provider (Leave Without Being Seen, LWBS). This has an impact on the hospital in terms of missing income from lost opportunities to offer tre atment and, in general, of negative outcomes from the ED process.” Our news editors obtained a quote from the research from University of Naples “F ederico II”: “Consequently, healthcare managers must be able to forecast and con trol patients who leave the ED without being evaluated in advance. This study is a retrospective analysis of patients registered at the ED of the ‘San Giovanni di Dio e Ruggi d’Aragona’ University Hospital of Salerno (Italy) during the year s 2014-2021. The goal was firstly to analyze factors that lead to patients aband oning the ED without being examined, taking into account the features related to patient characteristics such as age, gender, arrival mode, triage color, day of week of arrival, time of arrival, waiting time for take-over and year. These fa ctors were used as process measures to perform a correlation analysis with the L WBS status. Then, Machine Learning (ML) techniques are exploited to develop and compare several LWBS prediction algorithms, with the purpose of providing a usef ul support model for the administration and management of EDs in the healthcare institutions. During the examined period, 688,870 patients were registered and 3 9188 (5.68%) left without being seen. Of the total LWBS patients, 5 9.6% were male and 40.4% were female. Moreover, from the statistical analysis emerged that the parameter that most influence the aba ndonment rate is the waiting time for take-over. The final ML classification mod el achieved an Area Under the Curve (AUC) of 0.97, indicating high performance i n estimating LWBS for the years considered in this study.”
University of Naples “Federico II”Algo rithmsCyborgsEmerging TechnologiesHospitalsMachine Learning
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