Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach.

Erdenebileg Batbaatar,Keun Ho Ryu

doi:10.3390/ijerph16193628

Abstract

Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named Entity Recognition (HNER) task using healthcare-domain ontology that can recognize health-related entities from large numbers of user messages from Twitter. For this task, we employ a deep learning architecture which is based on a recurrent neural network (RNN) with little feature engineering. To achieve our goal, we collected a large number of Twitter messages containing health-related information, and detected biomedical entities from the Unified Medical Language System (UMLS). A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features. The conditional random field (CRF) model predicted a sequence of labels that corresponded to a sequence of inputs, and the Viterbi algorithm was used to detect health-related entities from Twitter messages. We provide comprehensive results giving valuable insights for identifying medical entities in Twitter for various applications. The BiLSTM-CRF model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome HNER; a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of 73.47%, and F1-score of 84.51% for pharmacologic substance named entities. The ontology-based manual annotation results show that it is possible to perform high-quality annotation despite the complexity of medical terminology and the lack of context in tweets.

Highlights

An overwhelming amount of health-related knowledge has been recorded in social media sites such as Twitter, with the number of tweets posted each year increasing exponentially [1,2,3]
The bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome Health-Related Named Entity Recognition (HNER); a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of
The disease or syndrome HNER performance of BiLSTM-CRF has a precision of 93.99%, recall of 73.31%, and F1 of 81.77% when evaluating on the presented dataset

Summary

Introduction

An overwhelming amount of health-related knowledge has been recorded in social media sites such as Twitter, with the number of tweets posted each year increasing exponentially [1,2,3]. Twitter is the most comprehensive social media site collecting and providing public health information: 500 million tweets are sent each day—5000 every second. We present an HNER task using healthcare-domain ontology. For the input of the HNER task, we created a healthcare Twitter corpus which overflow of HNER task. For the input of the HNER task, we created a healthcare Twitter corpus was collected from Twitter with the search term “healthcare” between 12 July 2018 and 12 July 2019

Objectives

Methods

Results

Conclusion