Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction

TL;DR

This paper presents a hierarchical embedding framework that aligns unstructured clinical notes with structured semantic frames to improve post-discharge ICU patient mortality prediction.

Contribution

It introduces a novel hierarchical semantic correspondence learning model that integrates text and structured knowledge for better patient risk assessment.

Findings

Outperforms baseline models on multiple mortality prediction benchmarks.

Effectively captures semantic relations between clinical text and structured knowledge.

Enhances local coherence in clinical note embeddings.

Abstract

Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients. Electronic health notes serve as a rich source for learning patient representations, that can facilitate effective risk assessment. However, a large portion of clinical notes are unstructured and also contain domain specific terminologies, from which we need to extract structured information. In this paper, we introduce an embedding framework to learn semantically-plausible distributed representations of clinical notes that exploits the semantic correspondence between the unstructured texts and their corresponding structured knowledge, known as semantic frame, in a hierarchical fashion. Our approach integrates text modeling and semantic correspondence learning into a single model that comprises 1) an unstructured embedding module that makes use of self-similarity matrix representations in order to inject structural regularities of different segments inherent in clinical texts to promote local coherence, 2) a structured embedding module to embed the semantic frames (e.g., UMLS semantic types) with deep ConvNet and 3) a hierarchical semantic correspondence module that embeds by enhancing the interactions between text-semantic frame embedding pairs at multiple levels (i.e., words, sentence, note). Evaluations on multiple embedding benchmarks on post discharge intensive care patient mortality prediction tasks demonstrate its effectiveness compared to approaches that do not exploit the semantic interactions between structured and unstructured information present in clinical notes.