Term-BLAST-like alignment tool for concept recognition in noisy clinical texts

Motivation Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts.Results Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches.Availability and implementation Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

Automated summary

Motivation methods for concept recognition in clinical texts are often tested on abstracts or articles, but texts from electronic health records (EHRs) often contain errors and nonstandard representations. This study presents a method inspired by the BLAST algorithm that screens texts for matches based on k-mer counts and scores candidates based on typical patterns of spelling errors. Experimental results show a significant enhancement in entity linking task performance, supporting the use of this method alongside existing approaches.