首页|Newcastle University Reports Findings in Mental Health Diseases and Conditions ( Machine Learning Model Reveals Determinators for Admission to Acute Mental Healt h Wards From Emergency Department Presentations)
Newcastle University Reports Findings in Mental Health Diseases and Conditions ( Machine Learning Model Reveals Determinators for Admission to Acute Mental Healt h Wards From Emergency Department Presentations)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Mental Health Diseases and Conditions is the subject of a report. According to news reporting originat ing in Callaghan, Australia, by NewsRx journalists, research stated, “This resea rch addresses the critical issue of identifying factors contributing to admissio ns to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. T his study aims to leverage machine learning (ML) models to assess the likelihood of admission to acute MH wards for this vulnerable population.” The news reporters obtained a quote from the research from Newcastle University, “Data collection for this study used existing ED data from 1 January 2016 to 31 December 2021. Data selection was based on specific criteria related to the pre senting problem. Analysis was conducted using Python and the Interpretable Machi ne Learning (InterpretML) machine learning library. InterpretML calculates overa ll importance based on the mean absolute score, which was used to measure the im pact of each feature on admission. A person’s ‘Age’ and ‘Triage category’ are ra nked significantly higher than ‘Facility identifier’, ‘Presenting problem’ and ‘ Active Client’. The contribution of other presentation features on admission sho ws a minimal effect. Aligning the models closely with service delivery will help services understand their service users and provide insight into financial and clinical variations. Suicidal ideation negatively correlates to admission yet re presents the largest number of presentations. The nurse’s role at triage is a cr itical factor in assessing the needs of the presenting individual. The gap that emerges in this context is significant; MH triage requires a complex understandi ng of MH and presents a significant challenge in the ED.”
CallaghanAustraliaAustralia and New ZealandCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMental Health Diseases and Conditions
NETL
NSTL
