Dark Data in Digital Health: A Predictive Framework for Identifying and Utilizing Underreported Clinical Signals

Abstract

This study developed a predictive framework to uncover underreported clinical signals hidden within dark data in digital health systems, addressing the paradox of abundant data but limited insights. It highlighted the importance of dark data, unanalyzed clinical records and the potential of AI to reveal hidden patterns while maintaining ethical standards. The research reviewed homomorphic encryption and AI integration, identifying a lack of real-time analysis in telehealth. Using a CRISP-DM-based methodology, machine learning was applied to datasets such as MIMIC-IV and PhysioNet, with preprocessing techniques like KNN imputation and DBSCAN outlier detection. Results showed neural networks achieved a 94% AUC-ROC, detected 32 new clinical signals, and improved rare disease identification by 50%. Ethical anonymization maintained 97% data utility, though dependence on historical data was a limitation. The novelty of this framework lies in merging dark data analytics with secure AI to enhance healthcare decision-making, patient safety, and precision medicine. Future work recommends real-time data integration, explainable AI, and standardized ethical protocols for scalability.