An ensemble framework with l_21 norm regularized hypergraph laplacian multilabel learning for clinical data prediction Previous work has shown that machine learning algorithms lend themselves to clinical decisionmaking and are a valuable tool for physicians. For clinical data, it is often necessary to assign multiple labels to a patient record by choosing from a large number of potential labels. A key problem in learning from multilabelled data is how to exploit the information contained in the correlations between labels. The hypergraphbased multilabel learning method learns from data by exploiting the spectral property of the hypergraph that encodes the correlation structure of labels. However, the problem with this method is the difficulty with which interpretations can be made. This is mainly due to its inability to recognize the importance of key features in the original feature space. Moreover, it is hard to comprehensively capture the complex structure of the correlations between labels. To overcome these difficulties and improve interpretability, we propose an  l_21 norm regularized Graph Laplacian multilabel learning to perform feature selection and label embedding simultaneously. Indepth experimental studies, using the publicly available Medical Information Mart for Intensive Care (MIMICIII) database, validate the effectiveness of our approach.

Hierarchical TargetAttentive Diagnosis Prediction in Heterogeneous Information Networks We introduce HTAD, a novel model for diagnosis prediction using Electronic Health Records (EHR) represented as Heterogeneous Information Networks. Recent studies on modeling EHR have shown success in automatically learning representations of the clinical records in order to avoid the need for manual feature selection. However, these representations are often learned and aggregated without specificity for the different possible targets being predicted. Our model introduces a targetaware hierarchical attention mechanism that allows it to learn to attend to the most important clinical records when aggregating their representations for prediction of a diagnosis. We evaluate our model using a publicly available benchmark dataset and demonstrate that the use of targetaware attention significantly improves performance compared to the current state of the art. Additionally, we propose a method for incorporating noncategorical data into our predictions and demonstrate that this technique leads to further performance improvements. Lastly, we demonstrate that the predictions made by our proposed model are easily interpretable.