Machine learning-based methods for phenotyping dementia patients from electronic health record data

PROJECT SUMMARY/ABSTRACT
Candidate: Dr. Roy Adams applies for this K25 Mentored Quantitative Research Career Development Award
with the goal of building a productive independent research career as a methodologist focused on developing
electronic health record (EHR)-based models and tools to improve our understanding of Alzheimer’s disease
and related dementias (ADRDs). Dr. Adams brings with him excellent training in computational methods for
observational health data but lacks expertise in ADRDs and the methods used to study them. “Big data” is
powerful but understanding the context surrounding the data is essential for knowing the limits of the data and
avoiding bias. The K25 training will support Dr. Adams in becoming an independent ADRD researcher by
allowing him to: (1) develop an understanding of dementia biology and care, (2) gain expertise in the methods
used to model psychiatric measurements, (3) gain exposure to the study of ADRDs from observational data,
and (4) form a network of collaborators in clinical ADRD research. These training aims will be accomplished
through in-person clinical exposure, didactic courses, directed readings and journal groups, and participation in
professional research networks.
Research and Environment: Phenotyping is an essential step of most EHR-based studies of ADRDs. Due to
common sources of error – such as fragmented care and selection bias – phenotyping ADRDs in EHR data
remains a challenge. Recent advances in machine learning present a potential way to account for these
sources of bias in high-dimensional EHR data by combining multiple proxies for the phenotype of interest,
while explicitly modeling the error and bias in each proxy. However, these methods remain limited and
methodological development is needed before they can be applied to ADRD data without risking substantial
bias. The proposed research focuses on developing these methods to extract two types of EHR-based
phenotypes of ADRD: a binary phenotype indicating whether a patient has dementia and a continuous
phenotype measuring the severity of that dementia. Dr. Adams will apply these methods to a large database of
Johns Hopkins EHRs and validate them using a combination of data from a memory center, data from a
parallel ongoing longitudinal study of ADRDs, and assessments of patient severity based on chart review. This
work will take advantage of a unique combination of resources available through the Johns Hopkins
Alzheimer’s Disease Research Center, the Richman Family Precision Medicine Center of Excellence in
Alzheimer’s Disease, and the Johns Hopkins inHealth Precision Medicine initiative. Further, this research will
provide Dr. Adams with valuable experience working with ADRD patient data, set the foundation for future
methodological work, and generate methods that can be directly applied to several planned and ongoing
ADRD precision medicine studies at Johns Hopkins.

Grant Number: 5K25AG083064-03
NIH Institute/Center: NIH
Principal Investigator: Roy Adams