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In this paper we present the evaluation of a set of string similarity metrics used to resolve the mapping from strings to concepts in the UMLS MetaThesaurus. String similarity is conceived as a single component in a full Reference Resolution System that would resolve such a mapping. Given this qualification, we obtain positive results achieving 73.6 F-measure (76.1 precision and 71.4 recall) for the task of assigning the correct UMLS concept to a given string. Our results demonstrate that adaptive string similarity methods based on Conditional Random Fields outperform standard metrics in this domain.

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We propose a machine learning approach for semantic recognition and normalization of clinical term descriptions. Clinical terms considered here are noisy descriptions in Spanish language written by health care professionals in our electronic health record system. These description terms contain clinical findings, family history, suspected disease, among other categories of concepts. Descriptions are usually very short texts presenting high lexical variability containing synonymy, acronyms, abbreviations and typographical errors. Mapping description terms to normalized descriptions requires medical expertise which makes it difficult to develop a rule-based knowledge engineering approach. In order to build a training dataset we use those descriptions that have been previously matched by terminologists to the hospital thesaurus database. We generate a set of feature vectors based on pairs of descriptions involving their individual and joint characteristics. We propose an unsupervised learning approach to discover term equivalence classes including synonyms, abbreviations, acronyms and frequent typographical errors. We evaluate different combinations of features to train MaxEnt and XGBoost models. Our system achieves an F 1 score of 89% on the Hospital Italiano de Buenos Aires (HIBA) problem list.

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