干旱区生态环境知识资源中心(Arid): Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care

Arid

DOI	10.1016/j.jbi.2022.104039
	Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care
	Song, Jiyoun; Hobensack, Mollie; Bowles, Kathryn H.; V. McDonald, Margaret; Cato, Kenrick; Rossetti, Sarah Collins; Chae, Sena; Kennedy, Erin; Barron, Yolanda; Sridharan, Sridevi; Topax, Maxim
通讯作者	Song, JY
来源期刊	JOURNAL OF BIOMEDICAL INFORMATICS
ISSN	1532-0464
EISSN	1532-0480
出版年	2022
卷号	128
英文摘要	Background/Objective: Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learningbased predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC. Methods: Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naive Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics. Results: During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC. Conclusion: All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.
英文关键词	Home health care Predictive modeling Natural language processing Risk assessment Clinical deterioration Nursing informatics
类型	Article
语种	英语
开放获取类型	Green Accepted, Bronze
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000767877600012
WOS关键词	NURSING DOCUMENTATION ; PREVALENCE
WOS类目	Computer Science, Interdisciplinary Applications ; Medical Informatics
WOS研究方向	Computer Science ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393351
推荐引用方式 GB/T 7714	Song, Jiyoun,Hobensack, Mollie,Bowles, Kathryn H.,et al. Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care[J],2022,128.
APA	Song, Jiyoun.,Hobensack, Mollie.,Bowles, Kathryn H..,V. McDonald, Margaret.,Cato, Kenrick.,...&Topax, Maxim.(2022).Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.JOURNAL OF BIOMEDICAL INFORMATICS,128.
MLA	Song, Jiyoun,et al."Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care".JOURNAL OF BIOMEDICAL INFORMATICS 128(2022).