Biomedical text mining has been shown to be valuable for diverse applications in the domains of molecular biology, toxicogenomics, and medicine. For example, it has been used to functionally annotate gene lists from microarray experiments [1–4], create literature-based compound profiles [5], generate medical hypotheses [6, 7], find new uses for old drugs [8–10], and measure protein similarity [11, 12]. The identification of biomedical terms in natural language is essential for biomedical text mining. The process of term identification consists of three tasks: term recognition, term classification and term mapping [13, 14]. Approaches to term identification generally fall into three categories: lexicon-based systems, rule-based systems, and statistics-based systems making use of different machine learning techniques [15]. All approaches have their disadvantages: lexicon-based systems are dependent on fast updates and large coverage of the underlying lexicons; to craft the rules for a rule-based system is time consuming and requires a high level of domain knowledge, and statistics-based systems need annotated corpora to train the classifiers. Term mapping, in which terms are linked to reference data sources, is the last step in the term identification process. Term mapping is only possible using lexicon-based term identification and is the focus of this paper (for comprehensive reviews on term identification see for example [13–17]). In addition, the lexicon-based approach deals with general medical terms for which it is difficult to design general matching patterns that are used by rule-based systems. It provides information concerning the semantic relations between terms and supports synonym and referent data source mapping, which is not possible using rule-based or statistically-based term identification. Specifically, we use the Unified Medical Language System (UMLS) meta-thesaurus provided by the U.S National Library of Medicine (NLM) [18]. The 2007AA edition of the UMLS contains more than 1.3 million concepts and 6.4 million terms referring to these concepts from more than 100 different vocabularies. These vocabularies cover different aspects of the biomedical field and have been developed for such different purposes as disease and procedure coding, adverse event reporting, literature indexing, billing, and gene function identification. NLM checks terms from different vocabularies for synonymy, assigns a unique concept identifier (CUI) and assigns concepts to one or more semantic types from the UMLS Semantic Network.

Naturally, the usefulness of the lexicon-based approach depends on the coverage of terms in the vocabulary for the particular domain and how well the terms are suited for natural language processing. The UMLS is not primarily intended as a resource for text mining, so not all of its terms are suitable for this purpose. For example, terms for coding of concepts can include specialized syntax (e.g., brackets) that is not suitable for text mining solutions ("undesired terms"). The following definition of a term in the UMLS can be found in the UMLS Glossary [19]: "A word or collection of words comprising an expression. In the Metathesaurus, a term is the class of all strings that are lexical variants (made singular and normalized to case) of each other". his definition allows for expressions that are not terms according to certain theories of terminology, in which terms are expressions that are actually used in domain-specific communication [20–22]. In fact, the UMLS abounds of expressions that are not expected to occur in any written or oral communication but are intended to precisely paraphrase the exact meaning of a concept. This has been illustrated by, for example, Srinivasan et al. [23], who found that by using normalized matching (i.e. ignoring case variation, punctuation, possessive markers, inflectional variation and word order) only a total of 34.3% of the 1,451,824 terms in the January 2002 version of the UMLS (non-English terms and terms with a suppressible term type excluded) could be found in a 11.5 million MEDLINE abstract corpus. McCray et al [24] could only find 10% of the UMLS terms when using a smaller corpus of 439,741 MEDLINE abstracts (UMLS 2001 version, 1,397,429 English terms, string features retained except for case variation). The lower match result in comparison with Srinivasan et al. might be explained by the difference in the matching methods, the UMLS version, and by the smaller corpus used. McCray et al. [24, 25] also investigated the nature of the strings in the UMLS and evaluated them for their use in natural language processing. The investigation resulted in a number of properties that could be used to filter unwanted strings from the UMLS. Rogers and Aronson [26] identified a number of filtering rules and term types which help in filtering the UMLS for the update of the MetaMap program [27].

This paper is inspired by McCray et al. [24, 25] and Rogers and Aronson [26] in that we aim to make the UMLS more useable for text-mining purposes. We do this by removing and adding synonyms to the UMLS, which are supposed to increase the accuracy and efficiency of biomedical term identification using the UMLS. We manually evaluate the impact of the rules on a MEDLINE corpus.

The rewrite rules were implemented to increase the recall of UMLS concepts in text. The suppression rules on the other hand were implemented to rid the UMLS of terms that are undesired when it comes to term identification either because they affect the precision of the term identification, e.g. the synonym "2" for the term "clinical class", the synonym "EC 2.7.1.-" for the concept "human CDC7 protein", or because they affect the efficiency of the term identification, i.e. long and vague terms that are unlikely to be found in text such as the term "poisoning by other and unspecified drugs and medicinal substances" or terms that are useless for concept identification such as the concept with the single term "WHILE". We applied the rules to the 2007AA version of the UMLS in UTF8 coding and then indexed citations from the MEDLINE database (1965-2007) (we refer to this as "the corpus" in the rest of the paper). Finally, the identified rewritten terms were manually assessed for their correspondence to the original UMLS terms and the identified suppressed terms were manually assessed for their usefulness for automatic text mining purposes. A detailed description of the procedure follows.

To make the UMLS more suitable for biomedical text mining we implemented and evaluated nine term rewrite and eight term suppression rules. In the creation of these rules, we used and refined many of the UMLS string properties identified by McCray et al. [24, 25] and Rogers and Aronson [26], together with an additional set of new properties discovered by our group during our work with the UMLS. Our work complements the work by McCray et al. and Rogers and Aronson in that we measured the impact on the number of terms identified by the different rules on all of MEDLINE (1965-2007), whereas the others only reported the number of strings in the UMLS that were affected by the specific string properties, and in that we also performed a manual analysis of the rewritten terms retrieved from the corpus and of the terms that were suppressed in the corpus. This was done in order to establish that the rewritten terms indeed correspond to the original terms and that only undesired terms were suppressed by the suppression rules.

The goal set for the rewrite rules was to increase the number of synonyms for a UMLS concept and thereby also increase the number of times a concept will be correctly identified in text, thus increasing the recall of biomedical term identification. Good rewrite rules should not generate terms that do not correspond to the original term. This holds true for the rules syntactic inversion, possessives and semantic type. The angular brackets rule and the short/long form rule generated a few incorrect cases. The incorrect cases from the angular brackets rule represent repeat patterns used in coding meal-related timings in patient records. These terms are in fact compositional grammar representing a class of "terms" in which various parts of a complex term are separated to their primitive codes and then put together through, for example, qualifiers. Hypothetically, such template terms could yield instance terms that have matches in the text. Rewriting these terms alters the template pattern and therefore the meaning of the term. Despite the incorrect cases generated by the angular brackets rule we recommend it to be used, but with a manual check of the results. We argue that this is feasible considering the small number of incorrect cases. We also recommend the use of the short/long form rule together with a manual check of the results. We find this advisable since the number of terms generated by the rule is relatively small (288 terms) but significant: it for example adds the commonly used abbreviation "SSRIs" to the term "Selective Serotonin Reuptake Inhibitors". It can also be noted that about half of the terms generated by this rule were homonyms. This indicates that the rule gives rise to quite ambiguous terms, which is another reason why we recommend a manual check of the results of this rule. Our analysis revealed that even though the different rules for rewriting terms with parentheses or brackets had an impact on the number of rewritten terms found, the quality of the rewritten terms was not perfect. The terms giving most problems were the names of biological entities, such as a genetic locus or the activity of an enzyme. These problems might be solved by introducing the criteria that parentheticals at the beginning (end) of a term should only be removed if they are followed (preceded) by a white space. This however would cause the rules to miss obvious cases without a white space where rewriting is necessary, such as " [M]Lymphoid leukaemias" (where the [M] is specific for the Read Codes vocabulary). A more promising way to tackle this problem is to analyze what kind of strings are found between parentheticals in the UMLS and based on these findings try to rewrite the terms. Our group has in this manner found that in 7,231 cases in the UMLS, the string between parentheses at the end of a term actually corresponds to the semantic type to which the term belongs. The implementation of this semantic type rule caused 259 new distinct terms to be found 218,838 times in our corpus and thus improves the recall of the UMLS-based term matching. Using the rewrite rules that passed our evaluation (rules 1-5) we were able to identify 1,117,772 new occurrences of 14,784 rewritten terms in the corpus. Projecting the results from the manual evaluation of 100 random rewritten terms per rule, 26 of these 14,784 terms would be incorrect.

Removal of erroneous synonyms improves the precision of the UMLS terms and removal of unnecessary terms and synonyms reduces the size of the UMLS, thus improving its efficiency. The evaluation of the suppression rules showed that all except one, namely the words > 5 rule, are safe to apply when the UMLS is to be used for concept identification in text.

We recommend the usage of the five rewrite rules and seven suppression rules that passed our evaluation when the UMLS is to be used for term identification in free text. Using these five rewrite rules we were able to identify 1,117,772 new occurrences of 14,784 rewritten terms in MEDLINE. Without the rewriting, we recognized 651,268 terms belonging to 397,414 concepts; with rewriting, we recognized 666,053 terms belonging to 410,823 concepts, which is an increase of 2.8% in the number of terms and an increase of 3.4% in the number of concepts recognized. Using the seven suppression rules, a total of 257,118 undesired terms were suppressed in the UMLS, thereby decreasing its size in megabyte by 25%, and 7,397 terms were suppressed in the corpus. By rewriting and suppressing the UMLS (and thereby increasing its recall and precision) it becomes more suitable for biomedical text mining purposes, such as information retrieval and knowledge discovery.

All the rules evaluated in this paper can be applied to UMLS data by using the software program Casper, which is available online at http://www.biosemantics.org. Casper takes a UMLS data file as input and gives a rewritten and suppressed UMLS data file as output. This UMLS data file can then be used together with any concept recognition software of choice. Please note that Casper operates on UMLS data, for which a license is needed.