An interpretable diagnostic approach for lung cancer: Combining maximal clique and improved BERT

The lung cancer incidence and mortality in China have always been high. Moreover, due to the limited level of professional technology, misdiagnosis and missed diagnosis of lung cancer often occur. To improve the accuracy of diagnosis, this paper proposes an interpretable diagnostic method for lung cancer based on Chinese electronic medical records (EMRs). First, to overcome the difficulty in word segmentation of clinical texts in Chinese EMRs, a dictionary construction method is proposed based on the idea of maximal clique, and 730 medical professional terms related to lung diseases are identified. Then, the ProbSparse self-attention mechanism and self-attention distilling operation in Informer are used to improve the Bidirectional Encoder Representations from Transformer (BERT) to realize the representation of long clinical texts with lower time complexity and memory consumption. Finally, the convolutional neural network with an attention mechanism is employed to process the representation results to realize the interpretable prediction of lung cancer. This method is applied to the lung cancer diagnosis of inpatients in a tertiary hospital in Hunan Province, obtaining excellent results of about 0.9 for area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, the results of the comparative analysis with existing dictionaries, word embedding methods and diagnostic methods further confirm the superiority of the proposed method. Specifically, the proposed method improves the precision by at least 6%, the recall by at least 2.6%, the F1 score by at least 5.2%, AUROC by at least 7.3% and AUPRC by at least 7.7% compared with all these state-of-the-art methods.