Detection and prediction of clopidogrel treatment failures using
  longitudinal structured electronic health records
Samuel Kim In Gu Sean Lee Mijeong Irene Ban Jane Chiang
We propose machine learning algorithms to automatically detect and predict
clopidogrel treatment failure using longitudinal structured electronic health
records (EHR). By drawing analogies between natural language and structured
EHR, we introduce various machine learning algorithms used in natural language
processing (NLP) applications to build models for treatment failure detection
and prediction. In this regard, we generated a cohort of patients with
clopidogrel prescriptions from UK Biobank and annotated if the patients had
treatment failure events within one year of the first clopidogrel prescription;
out of 502,527 patients, 1,824 patients were identified as treatment failure
cases, and 6,859 patients were considered as control cases. From the dataset,
we gathered diagnoses, prescriptions, and procedure records together per
patient and organized them into visits with the same date to build models. The
models were built for two different tasks, i.e., detection and prediction, and
the experimental results showed that time series models outperform bag-of-words
approaches in both tasks. In particular, a Transformer-based model, namely
BERT, could reach 0.928 AUC in detection tasks and 0.729 AUC in prediction
tasks. BERT also showed competence over other time series models when there is
not enough training data, because it leverages the pre-training procedure using
large unlabeled data.