Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

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 5,889 de-identified progress reports (2,439 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 (GRU), 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 five machine learning algorithms including Naive Bayes (NB), K-nearest Neighbor (KNN), Support Vector Machine for classification (SVC), Random Forest (RF), and Logistic Regression (LR).

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, pre-trained 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.