Abstract
Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports. In this study, we designed an NLP pipeline for the direct extraction of clinically relevant features from Chinese radiology reports, which is the first key step in computer-aided radiologic diagnosis. The pipeline was comprised of named entity recognition, synonyms normalization, and relationship extraction to finally derive the radiological features composed of one or more terms. In named entity recognition, we incorporated lexicon into deep learning model bidirectional long short-term memory-conditional random field (BiLSTM-CRF), and the model finally achieved an F1 score of 93.00%. With the extracted radiological features, least absolute shrinkage and selection operator and machine learning methods (support vector machine, random forest, decision tree, and logistic regression) were used to build the classifiers for liver cancer prediction. For liver cancer diagnosis, random forest had the highest predictive performance in liver cancer diagnosis (F1 score 86.97%, precision 87.71%, and recall 86.25%). This work was a comprehensive NLP study focusing on Chinese radiology reports and the application of NLP in cancer risk prediction. The proposed NLP pipeline for the radiological feature extraction could be easily implemented in other kinds of Chinese clinical texts and other disease predictive tasks.
Highlights
Massive electronic medical records (EMRs) are potentially valuable clinical sources for research for improving clinical care and support [1], [2]
The lexicon features bring an improvement of 1.74% in precision, 2.72% in recall and 2.22% in F1 score for the basic bidirectional long short-term memory (BiLSTM)-Conditional Random Field (CRF) model without lexicon
This study described an natural language processing (NLP) pipeline of Chinese free-text radiology reports for liver cancer diagnosis
Summary
Massive electronic medical records (EMRs) are potentially valuable clinical sources for research for improving clinical care and support [1], [2]. In the current digital age, machine learning-based algorithms play a powerful role in data mining, which is useful in applications such as clinical decision-making, disease computer-aided diagnosis, and management [3], [4]. As an important EMRs component, the radiology report is a primary method of communication between radiologists who interpret the image and physicians who make the final diagnosis. With the rapid growth of clinical big data, applying machine learning methods to process medical texts becomes executable. Extracting clinically relevant information from radiology reports has great importance in terms of advancing radiological research and clinical practice [6], significant challenges still exist, mainly due to the free form of most reports [7].
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