Early Prediction of Alzheimers Disease Leveraging Symptom Occurrences
  from Longitudinal Electronic Health Records of US Military Veterans
Rumeng Li Xun Wang Dan Berlowitz Brian Silver Wen Hu Heather Keating Raelene Goodwin Weisong Liu Honghuang Lin Hong Yu
Early prediction of Alzheimer's disease (AD) is crucial for timely
intervention and treatment. This study aims to use machine learning approaches
to analyze longitudinal electronic health records (EHRs) of patients with AD
and identify signs and symptoms that can predict AD onset earlier. We used a
case-control design with longitudinal EHRs from the U.S. Department of Veterans
Affairs Veterans Health Administration (VHA) from 2004 to 2021. Cases were VHA
patients with AD diagnosed after 1/1/2016 based on ICD-10-CM codes, matched 1:9
with controls by age, sex and clinical utilization with replacement. We used a
panel of AD-related keywords and their occurrences over time in a patient's
longitudinal EHRs as predictors for AD prediction with four machine learning
models. We performed subgroup analyses by age, sex, and race/ethnicity, and
validated the model in a hold-out and "unseen" VHA stations group. Model
discrimination, calibration, and other relevant metrics were reported for
predictions up to ten years before ICD-based diagnosis. The study population
included 16,701 cases and 39,097 matched controls. The average number of
AD-related keywords (e.g., "concentration", "speaking") per year increased
rapidly for cases as diagnosis approached, from around 10 to over 40, while
remaining flat at 10 for controls. The best model achieved high discriminative
accuracy (ROCAUC 0.997) for predictions using data from at least ten years
before ICD-based diagnoses. The model was well-calibrated (Hosmer-Lemeshow
goodness-of-fit p-value = 0.99) and consistent across subgroups of age, sex and
race/ethnicity, except for patients younger than 65 (ROCAUC 0.746). Machine
learning models using AD-related keywords identified from EHR notes can predict
future AD diagnoses, suggesting its potential use for identifying AD risk using
EHR notes, offering an affordable way for early screening on large population.