Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMICIII dataset. Quantitative and qualitative analysis have shown the proposed framework achieves stateoftheart annotation performance and computational efficiency compared with other methods.

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.