Intelligent MDT treatment decision making for stage III NSCLC using dual level embedding and three level explanation.

Stage III non-small cell lung cancer (NSCLC) presents complex challenges in treatment decisions due to extensive disease characteristics and patient heterogeneity. Multidisciplinary teams (MDT) offer personalized treatments by integrating diverse expertise. However, resource and time constraints limit MDT practicality in healthcare. Addressing this, we propose an interpretable approach for intelligent MDT treatment recommendations. Our approach focuses on enhancing clinical text representation and the interpretability of recommendations. Using a dual-level embedding technique, the local and global textual information can be captured. By delving into intricate factors at word, phrase, and sentence levels, we explain treatment recommendations for heterogeneous patients. Specially, attention flow is employed to consider the relationship between attentions across multiple layers, and attention mechanisms screen out important words and sentences. Our method achieves over 85% across accuracy, precision, recall, and F1 score in stage III NSCLC treatment recommendations. Furthermore, we assess the association between our proposed model and patient survival outcomes by analyzing patients who did not undergo MDT consultation. The results demonstrate that patients receiving model-concordant treatments exhibited significantly higher survival rates at 1, 3, and 5 years compared to those receiving model-nonconcordant treatments. Moreover, Kaplan-Meier survival curves confirm the improvement in survival outcomes associated with model-concordant treatments. Additionally, ablation analysis validates the rationality of the model structure.