Clinical Manifestations.

Background: This study responds to the urgent need for automated and reliable methods to detect cognitive impairments on a large scale. It leverages natural language processing (NLP) techniques to predict dementia and mild cognitive impairment (MCI) using clinical notes from electronic health records (EHR).

Method: Our study used an EHR dataset from Massachusetts General Brigham, which included clinical notes from a 2-year period (2017-2018) covering 12 types of patient encounters.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

Introduction: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials.

Methods: We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases.

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

Introduction: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.

Methods: A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics.

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test.

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool.

Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia.