干旱区生态环境知识资源中心(Arid): HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare

Arid

DOI	10.1016/j.ijmedinf.2023.105146
	HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare
	Zolnoori, Maryam; Barron, Yolanda; Song, Jiyoun; Noble, James; Burgdorf, Julia; Ryvicker, Miriam; Topaz, Maxim
通讯作者	Zolnoori, M
来源期刊	INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
ISSN	1386-5056
EISSN	1872-8243
出版年	2023
卷号	177
英文摘要	Background: More than 50 % of patients with Alzheimer's disease and related dementia (ADRD) remain undiagnosed. This is specifically the case for home healthcare (HHC) patients.Objectives: This study aimed at developing HomeADScreen, an ADRD risk screening model built on the combination of HHC patients' structured data and information extracted from HHC clinical notes. Methods: The study's sample included 15,973 HHC patients with no diagnosis of ADRD and 8,901 patients diagnosed with ADRD across four follow-up time windows. First, we applied two natural language processing methods, Word2Vec and topic modeling methods, to extract ADRD risk factors from clinical notes. Next, we built the risk identification model on the combination of the Outcome and Assessment Information Set (OASISstructured data collected in the HHC setting) and clinical notes-risk factors across the four-time windows.Results: The top-performing machine learning algorithm attained an Area under the Curve = 0.76 for a four-year risk prediction time window. After optimizing the cut-off value for screening patients with ADRD (cut-off-value = 0.31), we achieved sensitivity = 0.75 and an F1-score = 0.63. For the first-year time window, adding clinical note-derived risk factors to OASIS data improved the overall performance of the risk identification model by 60 %. We observed a similar trend of increasing the model's overall performance across other time windows. Variables associated with increased risk of ADRD were hearing impairment and impaired patient ability in the use of telephone. On the other hand, being non-Hispanic White and the absence of impairment with prior daily functioning were associated with a lower risk of ADRD.Conclusion: HomeADScreen has a strong potential to be translated into clinical practice and assist HHC clinicians in assessing patients' cognitive function and referring them for further neurological assessment.
英文关键词	Alzheimer's disease and related dementia (ADRD) Home healthcare (HHC) Clinical notes Machine learning Natural language processing
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001043899500001
WOS关键词	OLDER-ADULTS ; PREDICTION ; PEOPLE ; SCORE ; FALLS
WOS类目	Computer Science, Information Systems ; Health Care Sciences & Services ; Medical Informatics
WOS研究方向	Computer Science ; Health Care Sciences & Services ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397009
推荐引用方式 GB/T 7714	Zolnoori, Maryam,Barron, Yolanda,Song, Jiyoun,et al. HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare[J],2023,177.
APA	Zolnoori, Maryam.,Barron, Yolanda.,Song, Jiyoun.,Noble, James.,Burgdorf, Julia.,...&Topaz, Maxim.(2023).HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare.INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS,177.
MLA	Zolnoori, Maryam,et al."HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare".INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 177(2023).