ArticleInternational journal of medical informatics2023
HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare.
Article in International journal of medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges.BMC medical informatics and decision making · 2026Pooled it
- SpeechDETECT: an explainable automated speech processing pipeline for early detection of neurological and health changes.Health information science and systems · 2026Article
- Applying the Brief Interview for Mental Status in a Diverse Home Health Patient Population.Journal of the American Medical Directors Association · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Recruitment and Participation of Black Home Health Care Patients in Speech-Based Cognitive Research: Mixed Methods Feasibility Study.JMIR formative research · 2026Article
- Large Language Model Adaptation Strategies in Speech-Based Cognitive Screening: Systematic Evaluation.JMIR AI · 2026Article
- Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.JMIR research protocols · 2026Article
- Undocumented Dementia Diagnosis During Skilled Home Health Care: Prevalence and Associated Factors.Journal of the American Geriatrics Society · 2025Article
- Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea.Brain and behavior · 2025Article
- Natural language processing of electronic health records for early detection of cognitive decline: a systematic review.NPJ digital medicine · 2025Article
- Skilled home healthcare utilization and outcomes for older adults with dementia: A scoping review.Journal of the American Geriatrics Society · 2025Article
- Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis.JMIR formative research · 2023Article
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundMore than 50 % of patients with Alzheimer's disease and related dementia (ADRD) remain undiagnosed. This is specifically the case for home healthcare (HHC) patients.
objectivesThis 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.
methodsThe 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 (OASIS-structured data collected in the HHC setting) and clinical notes-risk factors across the four-time windows.
resultsThe 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.
conclusionHomeADScreen 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.
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