  Since no effective therapies exist for Alzheimer's disease (AD), prevention
has become more critical through lifestyle factor changes and interventions.
Analyzing electronic health records (EHR) of patients with AD can help us
better understand lifestyle's effect on AD. However, lifestyle information is
typically stored in clinical narratives. Thus, the objective of the study was
to demonstrate the feasibility of natural language processing (NLP) models to
classify lifestyle factors (e.g., physical activity and excessive diet) from
clinical texts. We automatically generated labels for the training data by
using a rule-based NLP algorithm. We conducted weak supervision for pre-trained
Bidirectional Encoder Representations from Transformers (BERT) models on the
weakly labeled training corpus. These models include the BERT base model,
PubMedBERT(abstracts + full text), PubMedBERT(only abstracts), Unified Medical
Language System (UMLS) BERT, Bio BERT, and Bio-clinical BERT. We performed two
case studies: physical activity and excessive diet, in order to validate the
effectiveness of BERT models in classifying lifestyle factors for AD. These
models were compared on the developed Gold Standard Corpus (GSC) on the two
case studies. The PubmedBERT(Abs) model achieved the best performance for
physical activity, with its precision, recall, and F-1 scores of 0.96, 0.96,
and 0.96, respectively. Regarding classifying excessive diet, the Bio BERT
model showed the highest performance with perfect precision, recall, and F-1
scores. The proposed approach leveraging weak supervision could significantly
increase the sample size, which is required for training the deep learning
models. The study also demonstrates the effectiveness of BERT models for
extracting lifestyle factors for Alzheimer's disease from clinical notes.
