Dataset Information

Automated UMLS-based comparison of medical forms.

ABSTRACT: Medical forms are very heterogeneous: on a European scale there are thousands of data items in several hundred different systems. To enable data exchange for clinical care and research purposes there is a need to develop interoperable documentation systems with harmonized forms for data capture. A prerequisite in this harmonization process is comparison of forms. So far--to our knowledge--an automated method for comparison of medical forms is not available. A form contains a list of data items with corresponding medical concepts. An automatic comparison needs data types, item names and especially item with these unique concept codes from medical terminologies. The scope of the proposed method is a comparison of these items by comparing their concept codes (coded in UMLS). Each data item is represented by item name, concept code and value domain. Two items are called identical, if item name, concept code and value domain are the same. Two items are called matching, if only concept code and value domain are the same. Two items are called similar, if their concept codes are the same, but the value domains are different. Based on these definitions an open-source implementation for automated comparison of medical forms in ODM format with UMLS-based semantic annotations was developed. It is available as package compareODM from http://cran.r-project.org. To evaluate this method, it was applied to a set of 7 real medical forms with 285 data items from a large public ODM repository with forms for different medical purposes (research, quality management, routine care). Comparison results were visualized with grid images and dendrograms. Automated comparison of semantically annotated medical forms is feasible. Dendrograms allow a view on clustered similar forms. The approach is scalable for a large set of real medical forms.

SUBMITTER: Dugas M

PROVIDER: S-EPMC3701617 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Automated UMLS-based comparison of medical forms.

Dugas Martin M Fritz Fleur F Krumm Rainer R Breil Bernhard B

PloS one 20130704 7

Medical forms are very heterogeneous: on a European scale there are thousands of data items in several hundred different systems. To enable data exchange for clinical care and research purposes there is a need to develop interoperable documentation systems with harmonized forms for data capture. A prerequisite in this harmonization process is comparison of forms. So far--to our knowledge--an automated method for comparison of medical forms is not available. A form contains a list of data items w ...[more]

PMID: 23861827

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Project description:BackgroundLarge biomedical data sets have become increasingly important resources for medical researchers. Modern biomedical data sets are annotated with standard terms to describe the data and to support data linking between databases. The largest curated listing of biomedical terms is the the National Library of Medicine's Unified Medical Language System (UMLS). The UMLS contains more than 2 million biomedical terms collected from nearly 100 medical vocabularies. Many of the vocabularies contained in the UMLS carry restrictions on their use, making it impossible to share or distribute UMLS-annotated research data. However, a subset of the UMLS vocabularies, designated Category 0 by UMLS, can be used to annotate and share data sets without violating the UMLS License Agreement.MethodsThe UMLS Category 0 vocabularies can be extracted from the parent UMLS metathesaurus using a Perl script supplied with this article. There are 43 Category 0 vocabularies that can be used freely for research purposes without violating the UMLS License Agreement. Among the Category 0 vocabularies are: MESH (Medical Subject Headings), NCBI (National Center for Bioinformatics) Taxonomy and ICD-9-CM (International Classification of Diseases-9-Clinical Modifiers).ResultsThe extraction file containing all Category 0 terms and concepts is 72,581,138 bytes in length and contains 1,029,161 terms. The UMLS Metathesaurus MRCON file (January, 2003) is 151,048,493 bytes in length and contains 2,146,899 terms. Therefore the Category 0 vocabularies, in aggregate, are about half the size of the UMLS metathesaurus.A large publicly available listing of 567,921 different medical phrases were automatically coded using the full UMLS metatathesaurus and the Category 0 vocabularies. There were 545,321 phrases with one or more matches against UMLS terms while 468,785 phrases had one or more matches against the Category 0 terms. This indicates that when the two vocabularies are evaluated by their fitness to find at least one term for a medical phrase, the Category 0 vocabularies performed 86% as well as the complete UMLS metathesaurus.ConclusionThe Category 0 vocabularies of UMLS constitute a large nomenclature that can be used by biomedical researchers to annotate biomedical data. These annotated data sets can be distributed for research purposes without violating the UMLS License Agreement. These vocabularies may be of particular importance for sharing heterogeneous data from diverse biomedical data sets. The software tools to extract the Category 0 vocabularies are freely available Perl scripts entered into the public domain and distributed with this article.

Project description:BackgroundThe Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI) from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS) concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP) pipeline for AEFI reports.MethodsWe developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1) error detection, (2) word list generation, (3) word list disambiguation and (4) error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction.ResultsWe used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV) for the spell checker were 74% (95% CI: 74-75), 100% (95% CI: 100-100), and 47% (95% CI: 46%-48%), respectively.ConclusionWe created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available tools, but the specificity was much superior. The slow processing speed may be improved by trimming it down to the most useful component algorithms. Other investigators may find the methods we developed useful for cleaning text using lexicons specific to their area of interest.

Project description:OBJECTIVES:To test the hypothesis that most instances of negated concepts in dictated medical documents can be detected by a strategy that relies on tools developed for the parsing of formal (computer) languages-specifically, a lexical scanner ("lexer") that uses regular expressions to generate a finite state machine, and a parser that relies on a restricted subset of context-free grammars, known as LALR(1) grammars. METHODS:A diverse training set of 40 medical documents from a variety of specialties was manually inspected and used to develop a program (Negfinder) that contained rules to recognize a large set of negated patterns occurring in the text. Negfinder's lexer and parser were developed using tools normally used to generate programming language compilers. The input to Negfinder consisted of medical narrative that was preprocessed to recognize UMLS concepts: the text of a recognized concept had been replaced with a coded representation that included its UMLS concept ID. The program generated an index with one entry per instance of a concept in the document, where the presence or absence of negation of that concept was recorded. This information was used to mark up the text of each document by color-coding it to make it easier to inspect. The parser was then evaluated in two ways: 1) a test set of 60 documents (30 discharge summaries, 30 surgical notes) marked-up by Negfinder was inspected visually to quantify false-positive and false-negative results; and 2) a different test set of 10 documents was independently examined for negatives by a human observer and by Negfinder, and the results were compared. RESULTS:In the first evaluation using marked-up documents, 8,358 instances of UMLS concepts were detected in the 60 documents, of which 544 were negations detected by the program and verified by human observation (true-positive results, or TPs). Thirteen instances were wrongly flagged as negated (false-positive results, or FPs), and the program missed 27 instances of negation (false-negative results, or FNs), yielding a sensitivity of 95.3 percent and a specificity of 97.7 percent. In the second evaluation using independent negation detection, 1,869 concepts were detected in 10 documents, with 135 TPs, 12 FPs, and 6 FNs, yielding a sensitivity of 95.7 percent and a specificity of 91.8 percent. One of the words "no," "denies/denied," "not," or "without" was present in 92.5 percent of all negations. CONCLUSIONS:Negation of most concepts in medical narrative can be reliably detected by a simple strategy. The reliability of detection depends on several factors, the most important being the accuracy of concept matching.

Dataset Information

Automated UMLS-based comparison of medical forms.

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Automated UMLS-based comparison of medical forms.

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