These CUI strings were used to generate inflections and derivations using LVG (NLM’s Lexical Variant Generation library from the SPECIALIST NLP Tools collection). Lexical variants were generated using CUI strings from the MRCONSO table for the mapped CUIs. This table was constructed from the CUI and STR columns of the MRCONSO table, with all records for strings known to be irrelevant to our application removed.Īfter the matching concept CUI list was obtained, a related term list was built by reverse lookup in the same term-to-concept table. It was for this reason that we made our own term-to-concept table. This can happen in cases of long phrases, excessive numbers of candidate permutations, and phrase terms that appear in MetaMap’s stop word list.
MetaMap is effective at using variants to find matches, but can miss some exact matches. We also developed a secondary concept matching strategy after some queries did not produce any mappings from MetaMap. For synonym identification, query terms were first mapped to UMLS concept CUIs, if possible, using MetaMap ( 35). We identified synonyms and lexical variants using the UMLS. To the best of our knowledge, the topic model and SemRep predicate-based expansions are approaches that have not been previous explored, especially in the context of clinical text retrieval. The predication-based expansion made use of a large predication database extracted from medical literature by a natural language processing (NLP) system called SemRep.
In the topic model-based expansion, we added related terms based on a topic-model trained on 100,000 clinical documents. The synonym-based expansion used a few selected UMLS source vocabularies and included lexical variants in the expanded queries. In this paper, we describe our experiments with three different query expansion strategies: synonym-based, topic model-based, and predication-based. On clinical notes, several studies have investigated different query expansion methods such as synonym expansion and relevance feedback with mixed results ( 13– 17). In biomedical informatics there have been a number of applications that have developed query expansion techniques for searching literature ( 7– 12). The user may ignore or use the suggested terms to construct new queries. In interactive systems, related terms are often presented as suggestions to a user ( 6). Some systems automatically expand the original query. Query log data, which records the search behavior of previous users, has also become a source for expansion terms especially in Web search engines ( 4, 5). For instance, retrieval feedback methods analyze the “best” returned documents, as determined by a ranking algorithm or by the user. Typical sources of additional terms are thesauri or the retrieved documents themselves. Query expansion, i.e., adding additional terms to the original query ( 3), is a common information retrieval (IR) technique to improve the query performance. The same query also results in many false negatives because it does not capture related phrases such as “suicidal tendencies” or “plans to commit suicide.” For instance, a query for patients with “suicide ideation” in clinical notes returns many false positives because this phrase is often negated in the notes. Simple text search is often not effective. The size of clinical data warehouses and repositories are growing exponentially and even a single patient record may contain dozens to hundreds of notes. At the same time, finding meaningful information can be a daunting task.
For example, administrators may seek collection of performance measures while researchers may search the data for cohort identification ( 1, 2). They also contain a wealth of information far beyond the immediate clinical use.
#Inform synonyms free
Free text medical records play a critical role in clinical practice.
0 kommentar(er)
