Temporal Deep Learning Model for Longitudinal Patient Data Analysis and Outcome Prediction

Abstract

The development of large-scale longitudinal healthcare data is a result of the rapid development of electronic health records and the continuous patient monitoring system. Such data is important to analyze well in order to predict patient outcomes and make a timely clinical decision. Nonetheless, classical machine learning and statistical models do not always reflect convoluted temporal dependences and anomaly trends in longitudinal patient histories. In order to overcome these obstacles, this paper gives a timed deep learning architecture that should represent the sequential patient data and enhance the prediction outcomes. The proposed model capitalizes on temporal feature production mechanisms of learning temporal patient paths as time progresses with strategies to address the absence of information and irregularly sampled data. The proposed approach is experimentally evaluated on a real-world healthcare dataset, comparing the proposed approach to the conventional machine learning and baseline deep learning models. The findings show that the proposed model outperforms other models in accuracy, F1-score, and AUC, showing that it can be useful in identifying temporal relationships in patient data. The results illustrate how temporal deep learning models can be used to improve outcome prediction and aid information-based clinical decision systems.