Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

Oliver Higgins, Stephan K Chalup, Rhonda L Wilson
{"title":"Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.","authors":"Oliver Higgins, Stephan K Chalup, Rhonda L Wilson","doi":"10.1111/inm.13402","DOIUrl":null,"url":null,"abstract":"<p><p>This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. This study aims to leverage machine learning (ML) models to assess the likelihood of admission to acute MH wards for this vulnerable population. 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 presenting problem. Analysis was conducted using Python and the Interpretable Machine Learning (InterpretML) machine learning library. InterpretML calculates overall importance based on the mean absolute score, which was used to measure the impact of each feature on admission. A person's 'Age' and 'Triage category' are ranked significantly higher than 'Facility identifier', 'Presenting problem' and 'Active Client'. The contribution of other presentation features on admission shows 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 represents the largest number of presentations. The nurse's role at triage is a critical factor in assessing the needs of the presenting individual. The gap that emerges in this context is significant; MH triage requires a complex understanding of MH and presents a significant challenge in the ED. Further research is required to explore the role that ML can provide in assisting clinicians in assessment.</p>","PeriodicalId":94051,"journal":{"name":"International journal of mental health nursing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of mental health nursing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/inm.13402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. This study aims to leverage machine learning (ML) models to assess the likelihood of admission to acute MH wards for this vulnerable population. 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 presenting problem. Analysis was conducted using Python and the Interpretable Machine Learning (InterpretML) machine learning library. InterpretML calculates overall importance based on the mean absolute score, which was used to measure the impact of each feature on admission. A person's 'Age' and 'Triage category' are ranked significantly higher than 'Facility identifier', 'Presenting problem' and 'Active Client'. The contribution of other presentation features on admission shows 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 represents the largest number of presentations. The nurse's role at triage is a critical factor in assessing the needs of the presenting individual. The gap that emerges in this context is significant; MH triage requires a complex understanding of MH and presents a significant challenge in the ED. Further research is required to explore the role that ML can provide in assisting clinicians in assessment.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
机器学习模型从急诊科病例中揭示入住急性精神疾病病房的决定因素。
这项研究旨在解决一个关键问题,即找出导致急诊科(ED)收治以精神疾病(尤其是自杀)为主要问题的患者的因素。本研究旨在利用机器学习(ML)模型来评估这一弱势群体入住急性精神疾病病房的可能性。本研究的数据收集使用了 2016 年 1 月 1 日至 2021 年 12 月 31 日的现有 ED 数据。数据选择基于与出现的问题相关的特定标准。分析使用 Python 和可解释机器学习(InterpretML)机器学习库进行。InterpretML 根据平均绝对分数计算总体重要性,用来衡量每个特征对入院的影响。一个人的 "年龄 "和 "分诊类别 "明显高于 "设施标识符"、"出现的问题 "和 "活跃客户"。其他表现特征对入院的影响微乎其微。将模型与服务提供紧密结合起来将有助于服务机构了解其服务对象,并深入了解财务和临床差异。自杀意念与入院呈负相关,但在入院患者中数量最多。护士在分诊时的角色是评估患者需求的关键因素。在这种情况下出现的差距是巨大的;MH 分诊需要对 MH 有复杂的了解,这对急诊室来说是一个巨大的挑战。需要进一步开展研究,探索 ML 在协助临床医生进行评估方面所能发挥的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Unveiling Shadows: Challenges Encountered by Mental Health Nurses and Health Cadres in the Delivery of Care to Persons With Mental Illness in Indonesia. 2nd International Conference on Mental Health and Behavioral Medicine (MHBM2024), 13 - 15 September 2024, Kuala Lumpur, Malaysia. Mental Health Experiences Among Undergraduate Nursing Students in a New Zealand Tertiary Institution: A Time for Change. Why Are We Not Asking About Suicidal Mental Imagery? The Impact of Clinical Supervision on the Mental Health Nursing Workforce: A Scoping Review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1