Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks
This addresses misdiagnosis reduction in healthcare by applying advanced NLP to a large-scale Russian EHR dataset, though it is incremental as it builds on existing BERT methods.
The paper tackles predicting clinical diagnoses from Russian Electronic Health Records using a modified BERT model with a novel fully-connected layer and domain-specific pretraining, achieving improved performance over baselines and comparable results to medical experts on a dataset of 4 million patient visits.
In this paper we study the problem of predicting clinical diagnoses from textual Electronic Health Records (EHR) data. We show the importance of this problem in medical community and present comprehensive historical review of the problem and proposed methods. As the main scientific contributions we present a modification of Bidirectional Encoder Representations from Transformers (BERT) model for sequence classification that implements a novel way of Fully-Connected (FC) layer composition and a BERT model pretrained only on domain data. To empirically validate our model, we use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits. This is the largest such study for the Russian language and one of the largest globally. We performed a number of comparative experiments with other text representation models on the task of multiclass classification for 265 disease subset of ICD-10. The experiments demonstrate improved performance of our models compared to other baselines, including a fine-tuned Russian BERT (RuBERT) variant. We also show comparable performance of our model with a panel of experienced medical experts. This allows us to hope that implementation of this system will reduce misdiagnosis.