Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored various recurrent neural network frameworks and show that they significantly outperformed the CRF models.
Code (1)
Tasks
BIG-bench Machine LearningEvent DetectionPharmacovigilanceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bidirectional RNN for Medical Event Detection in Electronic Health Records
Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important …
Event ExtractionSentenceDipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks
Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of vis…
PredictionFalls Prediction in eldery people using Gated Recurrent Units
Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies. In this work, we present Gated Recurrent Unit-based neural networks models designed for predicting fa…
Highrisk Prediction from Electronic Medical Records via Deep Attention Networks
Predicting highrisk vascular diseases is a significant issue in the medical domain. Most predicting methods predict the prognosis of patients from pathological and radiological measurements, which are expensive and requi…
Deep AttentionPrognosis