Dark Data in Digital Health: A Predictive Framework for Identifying and Utilizing Underreported Clinical Signals
Emonena Patrick Obrik-Uloho
*
Prairie View A& M University, 100 University Dr, Prairie View, TX77446, United States.
Oluwaseun Oladeji Olaniyi
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
Olubukola Omolara Adebiyi
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
Rukayat Oluwabukola Olasege
Ottawa University, 1001 South Cedar Street, Ottawa, KS 66067, United States.
Seun Michael Oyekunle
Ekiti State University, Ado-Iworoko Road, P.M.B. 5363, Ado-Ekiti, Ekiti State, Nigeria.
*Author to whom correspondence should be addressed.
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.
Keywords: Dark data, digital health, predictive framework, underreported signals, machine learning