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. 2023 Dec 1;39(12):btad716.
doi: 10.1093/bioinformatics/btad716.

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

Tudor Groza  1   2 , Honghan Wu  3 , Marcel E Dinger  4   5 , Daniel Danis  6 , Coleman Hilton  7 , Anita Bagley  8 , Jon R Davids  8 , Ling Luo  9 , Zhiyong Lu  9 , Peter N Robinson  6   10
Affiliations

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

Tudor Groza et al. Bioinformatics. .

Abstract

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.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
High-level overview of the proposed term-BLAST-like alignment approach.
Figure 2.
Figure 2.
Distribution of alignment scores for all tokens derived from the HPO. The box plots at 5%, 10%, 15%, …, 100% are shown (Note: odd numbers are omitted from the X-axis label). Each box plot represents the corresponding percentile scores for the tokens. For instance, the 5% alignment score for “abdomen” is 0.05497, and this score, as well as the corresponding score for other tokens, is used to generate the box plots.
See this image and copyright information in PMC

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