Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

Abstract

Medical entity extraction (EE) is a standard procedure used as a first stage in medical texts processing. Usually Medical EE is a two-step process: named entity recognition (NER) and named entity normalization (NEN). We propose a novel method of doing medical EE from electronic health records (EHR) as a single-step multi-label classification task by fine-tuning a transformer model pretrained on a large EHR dataset. Our model is trained end-to-end in an distantly supervised manner using targets automatically extracted from medical knowledge base. We show that our model learns to generalize for entities that are present frequently enough, achieving human-level classification quality for most frequent entities. Our work demonstrates that medical entity extraction can be done end-to-end without human supervision and with human quality given the availability of a large enough amount of unlabeled EHR and a medical knowledge base.

Research on named entity identification method based on medical field

“…At present, scholars have conducted a lot of research and innovation on existing problems of entity recognition in medical field, and medical named entity recognition technology has been developed. Nesterov [1] et al used electronic health records (EHR) as a single-step multi-label classification task by fine-tuning a Transformer model pre-trained on a large EHR dataset. The models were trained end-to-end in a remotely supervised manner using targets automatically extracted from a medical knowledge base.…”