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| Name | Class |
|---|---|
| Johns Hopkins University | OTHER |
| University of Rochester | OTHER |
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Neuropsychiatric symptoms (NPS) refer to a range of mental and emotional issues that can be observed through how patients move, perform daily tasks, and express feelings on their faces. In this study, the investigators want to find ways to accurately and unobtrusively track these symptoms in people's homes over time. Our goals are to note when these symptoms happen, predict potential problems, and gather clear data to help doctors make accurate diagnoses.
To do this, the investigators will first collect information from participants who have in-home sensors. the investigators will then use special computer programs that can recognize everyday activities and identify features that connect to scores from the Mild Behavioral Impairment Checklist (MBI-C). These scores will be compared to a questionnaire (NPIQ) filled out by caregivers or family members, along with any relevant information from doctors about the patients' symptoms. The investigators aim to see how these features can help differentiate between types of NPS, such as mood changes and agitation.
Finally, the investigators will create a dashboard for doctors that summarizes the patterns of these symptoms in patients, making it easier to monitor and manage their mental health.
What the investigators would need from participants:
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| Measure | Description | Time Frame |
|---|---|---|
| develop a personalized and clinically interpretable NPS assessment system | Assessing NPS relies primarily on clinician observation & interviews with caregivers, usually within brief & infrequent clinical visits. Precise assessment and monitoring methods would incorporate objective, patient- & caregiver-friendly, real-time tools and provide reliable & frequently captured data. We aim to create a system that can monitoring individual and subsyndromes of NPS; and detect early changes in NPS by automatically quantifying & detecting relevant behavioral abnormalities based on both intra- & inter-individual comparisons. Our approach is a video sensor, information processing, & alert system that utilizes in-home cameras & ambient sensors with video features that can recognize & track behaviors securely, pre-defines domains involved in a behavior; personalizes the results by assessing the individual's behavioral norms and the home environment; and analyzes, learns, and provides explainable items from different modalities of data: time, location, video, and audio. | 2 year |
| NPS Clinical Dashboard | Overall, the investigators envision the clinical dashboard as a transformative tool that delivers clear and comprehensive data on each individual. Therefore enhancing the quality of diagnosis of NPS for individuals, leading to better patient interventions and more effective long term management strategies. | 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| generate Data and support for a large-scale study or clinical trial within the context of an R01. | the ultimate goal of this project is to generate feasibility data and infrastructure for a large-scale study with a possible clinical trial within the context of an R01. In parallel, to translate the technology into clinical practice, we will work with the Stanford Office of Technology Licensing (OTL) to productize the outcomes. |
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Inclusion criteria:
Exclusion criteria:
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Older Adults - 65+
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| Name | Affiliation | Role |
|---|---|---|
| Ehsan Adeli, PhD | Stanford University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Clinical Excellence Research Center | Palo Alto | California | 94304 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41987876 | Result | Gould CE, Davis CH, Schuz N, Lin FV, Samus QM, Terada T, Daniel M, Tee S, Adeli E. Designing AI-Enabled Video Monitoring Clinician Dashboard for Neuropsychiatric Symptoms: A Survey of User Needs. Am J Geriatr Psychiatry Open Sci Educ Pract. 2026 Mar;9:46-51. doi: 10.1016/j.osep.2026.01.001. Epub 2026 Feb 8. |
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| ID | Term |
|---|---|
| D060825 | Cognitive Dysfunction |
| D000544 | Alzheimer Disease |
| D003704 | Dementia |
| D008569 | Memory Disorders |
| ID | Term |
|---|---|
| D003072 | Cognition Disorders |
| D019965 | Neurocognitive Disorders |
| D001523 | Mental Disorders |
| D001927 | Brain Diseases |
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| 2 years |
| D002493 |
| Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D024801 | Tauopathies |
| D019636 | Neurodegenerative Diseases |
| D019954 | Neurobehavioral Manifestations |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |