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Symposium

Health Sciences

Novel Automated Tools to Identify Cognitive Decline and Emotional Distress in the Context of MCI and AD/ADRDs

Friday, November 15, 2024
10:00 AM – 11:30 AM PST
Location: Room 608
CE: 1.5

Alzheimer’s disease and related dementias (ADRDs) are rapidly increasing public health concerns with significant impact on individuals, families, and healthcare systems. Though recent technological advances have enabled earlier and more accurate diagnosis for some, the vast majority of individuals experience delays in diagnosis and unmet early support needs. Diagnostic delays and inadequate support exacerbate psychological distress for impacted individuals, which is important to address given 1) the impact of distress on long-term outcomes and 2) the difficulty of treating distress once it becomes chronic. Novel automated technologies have the potential to more effectively and efficiently identify changes in cognition and distress, and if leveraged could provide cost-effective avenues to improve ADRD care. This symposium describes novel methods of leveraging large-scale data and technology to enhance screening and identification of early decline and distress in the context of cognitive impairment and ADRDs.

Ryan Mace, PhD will describe healthcare providers' impressions of barriers to assessing subjective cognitive decline and gaps in current practice.

Sarah Bannon, PhD will present findings from a secondary data analysis of electronic medical records examining structured data elements as indicators of psychological distress among individuals with recent ADRD diagnoses.

Ayushi Divecha, MPH MPT will describe the use of random forest models to identify individuals at high risk for developing dementia from a harmonized database of 5 of the largest NIH-funded studies of aging across the US.

Learning Objectives:

  • Participants will be able to describe the gaps in clinical practice that suggest the need for novel assessment tools for cognitive decline and distress in the context of decline.
  • Participants will gain an appreciation for specific considerations needed for data mining of structured and unstructured data elements available in electronic medical records prior to building automated models.
  • Participants will be able to define specific machine learning techniques for identifying high-risk groups (e.g., random forest models).

Presentations:

  • 10:00 AM – 11:30 AM PST
    Pilot Feasibility Study of EMR-Based Identification of Psychological Distress Around Recent Dementia Diagnoses

    Individual Symposium Abstract First Author: Sarah M. Bannon, PhD (she/her/hers) – Icahn School of Medicine at Mount Sinai

  • 10:00 AM – 11:30 AM PST
    Using Supervised Machine Learning Methods to Identify Individuals at Risk of Dementia

    Individual Symposium Abstract First Author: Ayushi Divecha, MPH, MPT (she/her/hers) – Icahn School of Medicine at Mount Sinai

  • 10:00 AM – 11:30 AM PST
    Overcoming Barriers in Detecting Subjective Cognitive Decline: A Qualitative Study With Health Care Professionals

    Individual Symposium Abstract First Author: Ryan A. Mace, PhD (he/him/his) – Massachusetts General Hospital/Harvard Medical School