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

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 ent

SUBMITTER: Groza T 

PROVIDER: S-EPMC10710372 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature