Advanced AI Methods for Cancer Prediction: Integrating Longitudinal Electronic Health Records with Deep Learning

Abstract

A developing area in Artificial Intelligence (AI) driven healthcare is cancer prediction in developing Electronic Health Records (EHRs). When dealing with multi-modal data, missing values, and explainability, traditional methods may be somewhat problematic. This study proposes a cutting-edge AI-driven framework for cancer prediction by integrating structured EHRs, clinical notes, and genetic data. To analyze sequential data, this work uses deep learning (DL) models like Long short-term memory (LSTM) networks and Transformers; to glean insights from medical texts that aren't organized, also use Natural Language Processing (NLP). The use of Generative Adversarial Networks (GANs) is used to handle missing data, and to certify transparency, SHapley Additive exPlanations (SHAP) methods are engaged to explain predictions. The accuracy achieved by the proposed model is higher than that of conventional machine learning methods. It recovers clinical decision-making and early cancer diagnosis by integrating many data modalities. This study work emphasizes the importance of AI in practical healthcare settings.