This technology is a natural language processing-based platform for estimating Mini-Mental State Examination (MMSE) scores from clinical notes, enabling cognitive assessment of Alzheimer’s Disease patients without additional testing.
Current methods for tracking cognitive decline in Alzheimer’s Disease rely on in-person administration of the Mini-Mental State Examination (MMSE) or extraction of structured electronic health record data, both of which are limited in availability and often incomplete. Clinical notes frequently contain relevant cognitive information, but this data is unstructured and difficult to analyze at a large scale. Although these notes are routinely collected in standard care, there are currently no tools that enable reliable, automated estimation of cognitive scores directly from unstructured clinical text.
This technology is a natural language processing-based platform that estimates cognitive performance in Alzheimer’s Disease patients by analyzing unstructured clinical notes alongside basic patient demographic data. Natural language processing techniques are used to identify and quantify features relevant to cognitive status, which are then input into a machine learning model trained to predict Mini-Mental State Examination (MMSE) scores. This approach enables retrospective assessment of cognitive decline using routinely collected data without requiring additional clinical testing, allowing the model to generalize across varied clinical documentation styles and provider note formats.
This technology has been validated with >1,000 electronic health records from Alzheimer’s Disease patients.
IR CU25391
Licensing Contact: Joan Martinez