A Comparative Study of Clinical Risk Prediction Using Limited Patient Electronic Health Records

Abstract

Predictive modeling of clinical risk using patient electronic health records (EHRs) has the potential to enhance healthcare outcomes by enabling early detection and intervention for high-risk patients. However, dealing with sparse, irregular, and temporal EHR data presents significant challenges. This paper presents a comparative study of clinical risk prediction with limited patient electronic medical records. The related literature is categorized and compared based on research objectives, methods and experimental analysis. Additionally, potential research opportunities for future work in this area are discussed. Meta-learning-based algorithms have the ability to overcome data scarcity challenge by learning shared feature representations. Nevertheless, further research is necessary to address limitations such as the interpretability and generalizability of the model across different patient populations.