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
Notice bibliographique
Résumé
BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in routine care, particularly in home health care (HHC), where clinical decisions are made under time constraints and cognitive status may be incompletely documented. Federally mandated HHC assessments, such as the Outcome and Assessment Information Set (OASIS), capture health and functional status but may miss subtle early cognitive changes. Speech, language, and interactional patterns during routine patient-nurse communication, together with information embedded in unstructured clinical notes, may provide complementary signals for earlier identification. OBJECTIVE: This protocol describes the development and evaluation of a multimodal screening approach for identifying MCI-ED in HHC by integrating (1) speech and interaction features from routine patient-nurse encounters (verbal communication), (2) large language model-based extraction of MCI-ED-related information from HHC notes and encounter transcripts, and (3) structured variables from OASIS. METHODS: This ongoing cross-sectional case-control study is being conducted in collaboration with VNS Health (formerly Visiting Nurse Service of New York). Eligible participants are adults aged ≥60 years receiving HHC services. Case/control assignment uses a 2-stage process: electronic health record (EHR) prescreening followed by clinician-reviewed cognitive assessment (Montreal Cognitive Assessment and Clinical Dementia Rating) for consented participants without an existing mild cognitive impairment diagnosis. For Aim 1, each participant contributes 3 audio-recorded routine patient-nurse encounters linked to EHR data, including OASIS and free-text clinical notes. Aim 1 extracts acoustic, linguistic, emotional, and interactional features from patient-nurse verbal communication. Aim 2 uses a schema-guided large language model pipeline to extract and normalize MCI-ED-related symptoms, lifestyle risk factors, and communication deficits from HHC notes and encounter transcripts, supported by a human-annotated gold-standard dataset. Aim 3 integrates speech, extracted text variables, and OASIS predictors using supervised machine learning with stratified nested cross-validation; evaluation will include discrimination, calibration, and subgroup performance checks across race, sex, and age. RESULTS: Between February 2024 and July 2025, a total of 114 HHC patients completed study-administered cognitive assessments and were classified as 55 MCI-ED cases and 59 cognitively normal controls. Audio-recorded patient-nurse encounters had a median duration of 19 (IQR 12-23) minutes and a median of 56 (IQR 31-80) utterances per encounter; nurses contributed more words than patients (median 842, IQR 461-1218 vs median 589, IQR 303-960). In exploratory feasibility analyses, multimodal models integrating speech, interactional features, and structured EHR/OASIS variables outperformed single-source models. CONCLUSIONS: This protocol describes a reproducible multimodal framework for MCI-ED screening in HHC using routinely generated data streams. Initial implementation results support feasibility of data collection and end-to-end processing and suggest potential value of integrating interactional speech features with clinical text and OASIS variables. Final model evaluation, subgroup analyses, and validation will follow the prespecified analytic procedures on the finalized study dataset. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/82731.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,067 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».