A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Special track on AI for CompSust and Human well-being. Pages 4299-4305. https://doi.org/10.24963/ijcai.2020/593
The prediction of the risk profile related to the cardiopathy complication is a core research task that could support clinical decision making. However, the design and implementation of a clinical decision support system based on Electronic Health Record (EHR) temporal data comprise of several challenges. Several single task learning approaches consider the prediction of the risk profile related to a specific diabetes complication (i.e., cardiopathy) independent from other complications. Accordingly, the state-of-the-art multi-task learning (MTL) model encapsulates only the temporal relatedness among the EHR data. However, this assumption might be restricted in the clinical scenario where both spatio-temporal constraints should be taken into account. The aim of this study is the proposal of two different MTL procedures, called spatio-temporal lasso (STL-MTL) and spatio-temporal group lasso (STGL-MTL), which encode the spatio-temporal relatedness using a regularization term and a graph-based approach (i.e., encoding the task relatedness using the structure matrix). Experimental results on a real-world EHR dataset demonstrate the robust performance and the interpretability of the proposed approach.
Machine Learning: Transfer, Adaptation, Multi-task Learning
Machine Learning Applications: Bio/Medicine
Machine Learning: Classification
Machine Learning Applications: Applications of Supervised Learning