Evaluating the domains of generalism and equity, diversity and inclusion in preclinical simulated cases for targeted curricular improvements.

Background: Simulated cases are widely used in medical education to develop clinical reasoning skills and discuss key topics around patient care. Such cases present an opportunity to demonstrate real world encounters with diverse patient and health provider identities, impacts of social and structural determinants of health, and demonstrate a generalist approach to problems. However, despite many calls-to-action for medical schools to better incorporate equity, diversity and inclusion (EDI) and generalism, it remains difficult to evaluate how well these goals are being met.

Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis.

Background: Speech data for medical research can be collected noninvasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important, as there is valuable information contained in lexical content.

Classification of Alzheimer's Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data.

Alzheimer's disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification.