Abstract

This paper presents a novel approach to the task of automatically inferring the most probable diagnosis from a given clinical narrative. Structured Knowledge Bases (KBs) can be useful for such complex tasks but not sufficient. Hence, we leverage a vast amount of unstructured free text to integrate with structured KBs. The key innovative ideas include building a concept graph from both structured and unstructured knowledge sources and ranking the diagnosis concepts using the enhanced word embedding vectors learned from integrated sources. Experiments on the TREC CDS and HumanDx datasets showed that our methods improved the results of clinical diagnosis inference.

Highlights

  • Introduction and Related WorkClinical diagnosis inference is the problem of automatically inferring the most probable diagnosis from a given clinical narrative

  • In recent Text REtrieval Conference (TREC) Clinical Decision Support track (CDS1), diagnosis inference from medical narratives has improved the accuracy of retrieving relevant biomedical articles (Roberts et al, 2015; Hasan et al, 2015; Goodwin and Harabagiu, 2016)

  • Motivated by the superior power of the integration of structured Knowledge Bases (KBs) and unstructured free text, we propose a novel approach to clinical diagnosis inference

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Summary

Introduction and Related Work

Clinical diagnosis inference is the problem of automatically inferring the most probable diagnosis from a given clinical narrative. Solutions to the clinical diagnostic inferencing problem require a significant amount of inputs from domain experts and a variety of sources (Ferrucci et al, 2013; Lally et al, 2014). To address such complex inference tasks, researchers (Yao and Van Durme, 2014; Bao et al, 2014; Dong et al, 2015) have utilized structured KBs. that store relevant information about various entity types and relation triples. To the best of our knowledge, there is no work on diagnoses inference from clinical narratives conducted in an unsupervised way We build such baselines for this task

Overview of the Approach
Knowledge Sources of Evidence Concepts
Building Weighted Concept Graph
Result
Representing Clinical Case
Inferring Concepts for Diagnosis
Word Embedding Models
Results
Discussion
Method
Findings
Conclusion
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