Abstract
Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data. The proposed model has been evaluated using micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in the case of discharge datasets. For instance, for the case of discharge summary, the micro average F-score is improved by 2.55% and the overall accuracy is improved by 7.53%. For the case of progress notes, the micro average F-score and the overall accuracy are improved by 1.63% and 5.63%, respectively.
Highlights
Electronic Medical Record (EMR) [1], a digital version of storing patients’ medical history in textual format, has shaped our medical domain in such a promising way that we can gather all information into one place for healthcare providers
We evaluate the proposed model with different metrics namely micro average, macro average and accuracy by comparing with classifiers, namely Naive Bayes (NB), Maximum Entropy (ME), Support Vector Machine (SVM), Conditional Random Field (CRF) [24], and deep learning models including Convolutional Neural Network (CNN) [24], single task bidirectional Recurrent Neural Network (RNN) (BRNN), transfer bi-directional RNN (TBRNN) [20], and multitask bidirectional RNN (MBRNN) (Multitask model) [21], where we build multiclass classifiers with these classifiers to resolve Named Entity Recognition (NER) [24]
Bi-RNN model (BRNN) model is selected as the base line model and MBRNN is employed as the state-of-the-art
Summary
Electronic Medical Record (EMR) [1], a digital version of storing patients’ medical history in textual format, has shaped our medical domain in such a promising way that we can gather all information into one place for healthcare providers. To construct a comprehensive system to process EMR, we need different modules such as word-level modules including Part-of-Speech (POS) and Named Entity Recognition (NER), sentence-level modules like dependency parsing and semantic role labeling, and document-level modules, for example, classification and summarization. For the EMR summarization, the EMR is summarized from two dimensions: extractive summaries and abstractive summaries [2] Modules such as CliniViewer [3] and IHC Patient Worksheet [4] were built.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
