MCN: A comprehensive corpus for medical concept normalization.

Normalization of clinical text involves linking different ways of talking about the same clinical concept to the same term in the standardized vocabulary. To date, very few annotated corpora for normalization have been available, and existing corpora so far have been limited in scope and only dealt with the normalization of diseases and disorders. In this paper, we describe the annotation methodology we developed in order to create a new manually annotated wide-coverage corpus for clinical concept normalization, the Medical Concept Normalization (MCN) corpus. In order to ensure wider coverage, we applied normalization to the text spans corresponding to the medical problems, treatments, and tests in the named entity corpus released for the fourth i2b2/VA shared task. In contrast to previous annotation efforts, we do not assign multiple concept labels to the named entities that do not map to a unique concept in the controlled vocabulary. Nor do we leave that named entity without a concept label. Instead, our normalization method that splits such named entities, resolving some of the core ambiguity issues. Lastly, we supply a sieve-based normalization baseline for MCN which combines MetaMap with multiple exact match components. The resulting corpus consists of 100 discharge summaries and provides normalization for the total of 10,919 concept mentions, using 3792 unique concepts from two controlled vocabularies. Our inter-annotator agreement is 67.69% pre-adjudication and 74.20% post-adjudication. Our sieve-based normalization baseline for MCN achieves 77% accuracy in cross-validation. We also detail the challenges of creating a normalization corpus, including the limitations deriving from both the mention span selection and the ambiguity and inconsistency within the current standardized terminologies. In order to facilitate the development of improved concept normalization methods, the MCN corpus will be publicly released to the research community in a shared task in 2019.

A Hybrid Normalization Method for Medical Concepts in Clinical Narrative using Semantic Matching.

Normalization maps clinical terms in medical notes to standardized medical vocabularies. In order to capture semantic similarity between different surface expressions of the same clinical concept, we develop a hybrid normalization system that incorporates a deep learning model to complement the traditional dictionary lookup ap- proach. We evaluate our system against the ShARe/CLEF 2013 challenge data in which 30% of the mentions have no concept mapping. When evaluating against the mentions which may be normalized to existing concepts, our hybrid system achieves 90.6% accuracy, obtaining a statistically significant improvement of 2.6% over a strong edit-distance and dictionary lookup combined baseline. Our analysis of semantic similarity between concepts and mentions reveals existing inconsistencies in ShARe/CLEF data, as well as problematic ambiguities in the UMLS. Our results suggest the potential of the proposed deep learning approach to further improve the performance of normalization by utilizing semantic similarity.

Interpretable Topic Features for Post-ICU Mortality Prediction.

Electronic health records provide valuable resources for understanding the correlation between various diseases and mortality. The analysis of post-discharge mortality is critical for healthcare professionals to follow up potential causes of death after a patient is discharged from the hospital and give prompt treatment. Moreover, it may reduce the cost derived from readmissions and improve the quality of healthcare. Our work focused on post-discharge ICU mortality prediction. In addition to features derived from physiological measurements, we incorporated ICD-9-CM hierarchy into Bayesian topic model learning and extracted topic features from medical notes. We achieved highest AUCs of 0.835 and 0.829 for 30-day and 6-month post-discharge mortality prediction using baseline and topic proportions derived from Labeled-LDA. Moreover, our work emphasized the interpretability of topic features derived from topic model which may facilitates the understanding and investigation of the complexity between mortality and diseases.