Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

Abstract

Background Automatic recognition of medical concepts in unstructured text is an important component of many clinical and research applications, and its accuracy has a large impact on electronic health record analysis. The mining of medical concepts is complicated by the broad use of synonyms and nonstandard terms in medical documents. Objective We present a machine learning model for concept recognition in large unstructured text, which optimizes the use of ontological structures and can identify previously unobserved synonyms for concepts in the ontology. Methods We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space. It uses the hierarchical structure provided by the biomedical ontology as an implicit prior embedding to better learn embedding of various terms. We trained our model on two biomedical ontologies—the Human Phenotype Ontology (HPO) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT). Results We tested our model trained on HPO by using two different data sets: 288 annotated PubMed abstracts and 39 clinical reports. We achieved 1.7%-3% higher F1-scores than those for our strongest manually engineered rule-based baselines ( P =.003). We also tested our model trained on the SNOMED-CT by using 2000 Intensive Care Unit discharge summaries from MIMIC (Multiparameter Intelligent Monitoring in Intensive Care) and achieved 0.9%-1.3% higher F1-scores than those of our baseline. The results of our experiments show high accuracy of our model as well as the value of using the taxonomy structure of the ontology in concept recognition. Conclusion Most popular medical concept recognizers rely on rule-based models, which cannot generalize well to unseen synonyms. In addition, most machine learning methods typically require large corpora of annotated text that cover all classes of concepts, which can be extremely difficult to obtain for biomedical ontologies. Without relying on large-scale labeled training data or requiring any custom training, our model can be efficiently generalized to new synonyms and performs as well or better than state-of-the-art methods custom built for specific ontologies.

The Human Phenotype Ontology in 2021

“…To improve the available vocabulary and annotated diseases, a targeted expansion of IEI relevant HPO terms and re-annotation of currently known IEIs was launched by representatives of the ESID genetics working party and of ERN-RITA (European Network on Rare Primary Immunodeficiency, Autoinflammatory and Autoimmune diseases) with input from the International Society of Systemic Autoinflammatory Diseases (ISSAID) in 2018. The systematic review involved expert clinicians, geneticists, researchers (working on IEI) and bioinformaticians combining an ontology-guided machine-learning approach ( 37 ) with expert clinical immunologists’ reviews (M. Haimel, et al. , manuscript in preparation).…”

The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions

“…Applying such an architecture to the medical domain can help with improving and explaining automatic recognition of medical concepts in (un-)structured text (i.e., patient records), which is a complicated task due to the broad use of synonyms and non-standard terms in medical documents (Arbabi et al, 2019 ). In essence, better reproducibility of explanations can be achieved by reducing randomness during perturbation by the integration of semantic sampling that also allows to generate contextual explanations, which in turn can be interpreted by humans more intuitively.…”

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

“…In some cases, additional works are cited to provide context. A total of 15 articles were finally selected for inclusion [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

“…Another approach to ontology-guided graph embedding uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space. It uses the hierarchical structure provided by the HPO and Systematized Nomenclature of Medicine -Clinical Terms (SNOMED-CT) terms as an implicit prior embedding to better learn embedding of various terms [4]. Other new research shows the importance of wisely choosing text corpora for training.…”