Enhanced multimodal BERT-based deep learning method to predict prostate cancer from clinical data

Prostate cancer is a persistent worldwide healthcare issue that needs to have enhanced predictive models to assist in the early diagnosis and stratification of treatment. This study proposes a deep learning model that is founded on Bidirectional Encoder Representations of Transformers (BERT) to derive unstructured clinical notes to predict prostate cancer. By hyper parameter using BERT with a large-scale database of patient model in semantic and contextual in clinical set. To further gain predictive power, BERT embedding are fused with numerical clinical features using multimodal fusion, which integrates complementary streams of data to offer powerful classification. Its model is much better in prediction accuracy with high sensitivity and specificity, which is confirmed with rigorous cross-validation and SHapley Additive explanations (SHAP) interpretability analyses. This piece demonstrates the urgency of language models based on transformers in extracting practical implications out of clinical records as a large-scale intervention in supporting the diagnosis of prostate cancer in resource-constrained care settings.