Pathologic Complete Response Prediction with Machine Learning Using Hierarchical Attention Feature Extraction

Predicting pathologic complete response in non-small cell lung cancer is crucial for tailoring effective treatment strategies and to improve patient outcomes. With the increasing application of artificial intelligence in cancer research, machine learning is poised to play a significant role in prognostication and decision-making. This paper presents a novel approach that utilizes named entity recognition and attention mechanisms applied to electronic health records to predict the pathologic complete response. We first employ named entity recognition to extract relevant biomedical entities from unstructured clinical notes within reports. These entities, combined with structured data, are then processed using a hierarchical attention mechanism to generate comprehensive patient representations. This approach captures complex relationships and contextual information within electronic health records compared to traditional methods. The results highlight the potential of advanced natural language processing techniques to enhance clinical decision-making and support personalized treatment planning in oncology.