Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes. Issue 1 (3rd January 2019)

• Record Type: Journal Article
• Authors: Guan, Meijian; Cho, Samuel; Petro, Robin; Zhang, Wei; Pasche, Boris; Topaloglu, Umit
• Is Part Of: JAMIA open. Volume 2:Issue 1(2019)
• Page Start: 139
• Page End: 149
• Publication Date: 2019-01-03
• Subjects: machine learning -- natural language processing -- electronic health records -- cancer -- genomics; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooy061
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objectives: Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods: We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression. Results: Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion: NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.