Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data.
Journal:
International journal of medical informatics
PMID:
39068894
Abstract
BACKGROUND AND OBJECTIVE: Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models focus on single diseases, neglecting comorbidities prevalent in patients. Moreover, mortality is infrequent compared to illness, causing extreme class imbalance that impedes reliable prediction. We aim to develop a deep learning framework that accurately forecasts mortality risk from real-world data by addressing comorbidities and class imbalance.