An Integrated Disease Progression Model to Analyze Electronic Health Records With Multimodal Datasets Using Deep Learning Techniques

In contemporary clinical environments, precisely simulating the emergence of disease is an intricate task owing to heterogeneously derived data and variability in the record of diagnoses. The research presents a stable classification model that integrates image data with formalized patient data to track patterns in disease through various stages. The suggested approach combines a multi-stage analytical methodology that includes systematic data preparation, transfer-based feature learning with a purpose-tuned InceptionResNetV2 architecture, and performance metrics evaluation under stringent criteria. Substantially, the system has been augmented by incorporating a fusion approach in which diagnostic images are combined with patient records, leading to enhanced classification validity. With a general accuracy level of 97.45%, the model indicates good generalizability and interpretability. Its use of domain-specific tuning and interpretive tools increases its applicability to real-world medical diagnosis, offering a sound solution for dealing with class imbalance and heterogeneous disease presentations. These figures indicate the model's best performance in dealing with data imbalance and misclassifications, typical in medical imaging and Electronic Health Record (EHR) analysis. Data imbalance, whereby certain disease categories are underrepresented, is likely to lead to skewed predictions and false generalizations. The findings point to the need for developing diagnostic tools that would be applicable in multimodal data integration.