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.

Keywords: biomedical ontologies; concept recognition; human phenotype ontology; machine learning; medical text mining; phenotyping.

Figure 1

Architecture of the neural dictionary model. The encoder is shown at the top, and the procedure for computing the embedding for a concept is illustrated at the bottom. Encoder: a query phrase is first represented by its word vectors, which are then projected by a convolution layer into a new space. Then, a max-over-time pooling layer is used to aggregate the set of vectors into a single one. Thereafter, a fully connected layer maps this vector into the final representation of the phrase. Concept embedding: a matrix of raw embeddings is learned, where each row represents one concept. The final embedding of a concept is retrieved by summing the raw embeddings for that concept and all of its ancestors in the ontology. FC: fully connected.

Figure 2

Visualization of the representations learned for Human Phenotype Ontology concepts. The representations are embedded into two dimensions using t-SNE. The colors denote the high-level ancestors of the concepts. The plot on the left shows the representations learned in NCR-N, where the taxonomy information was used in training, and the plot on the right shows representations learned for NCR-HN, where the taxonomy was ignored. NCR-HN: variation of the NCR model that ignores the taxonomy and has not been trained on negative examples; NCR-N: variation of the NCR model that has not been trained on negative samples; t-SNE: t-distributed stochastic neighbor embedding.