Biomedical subjects

Carol Friedman

Publications and source records attributed to Carol Friedman.

11 recordsLinked to original sources

PubMed2003

Mining terminological knowledge in large biomedical corpora.

Terminological knowledge of the biomedical domain is important for natural language processing (NLP) and information retrieval (IR) applications, and a number of terminological knowledge sources, such as LocusLink, GeneBank, and the UMLS, already exist. However, because of the tremendous amount of research activity in the field, new terms and symbols are continually being created, many of which are published in the literature, but are not available in any of the other resources. Therefore, effective mining of the literature for new terminology is critical for furthering NLP and IR applications. Abbreviations are widely used in the biomedical domain, and the understanding of abbreviations requires a terminological knowledge base that consists of abbreviations with their associated senses. In previous work, several methods have been developed for automatic construction of abbreviation knowledge bases from parenthetical expressions. However, these methods pair abbreviations and their expansions based on manually crafted patterns or rules. In this paper, we propose an automatic method, which is not based on patterns or rules but is based on the use of collocations, to extract a set of related terms from parenthetical expressions including abbreviations associated with their expansions and other types of related terms such as synonyms, or hyponyms etc. Our method is based on the observation that terms associated with parenthetical expressions i) are usually related, and ii) are often collocations because they tend to co-occur more often than expected by chance. Our method was applied to the collection of MEDLINE abstracts. The method and the results were evaluated using two collections: Berman's handcrafted abbreviation list and the LocusLink collection.

Abstracting and Indexing↗

PubMed2002

Two biomedical sublanguages: a description based on the theories of Zellig Harris.

Natural language processing (NLP) systems have been developed to provide access to the tremendous body of data and knowledge that is available in the biomedical domain in the form of natural language text. These NLP systems are valuable because they can encode and amass the information in the text so that it can be used by other automated processes to improve patient care and our understanding of disease processes and treatments. Zellig Harris proposed a theory of sublanguage that laid the foundation for natural language processing in specialized domains. He hypothesized that the informational content and structure form a specialized language that can be delineated in the form of a sublanguage grammar. The grammar can then be used by a language processor to capture and encode the salient information and relations in text. In this paper, we briefly summarize his language and sublanguage theories. In addition, we summarize our prior research, which is associated with the sublanguage grammars we developed for two different biomedical domains. These grammars illustrate how Harris' theories provide a basis for the development of language processing systems in the biomedical domain. The two domains and their associated sublanguages discussed are: the clinical domain, where the text consists of patient reports, and the biomolecular domain, where the text consists of complete journal articles.

Abstracting and Indexing↗

PubMed2002

Of truth and pathways: chasing bits of information through myriads of articles.

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

Algorithms↗

PubMed2002

Association between health insurance coverage of office visit and cancer screening among women.

BACKGROUND: Little is known regarding the nuances of insurance benefit design that may affect the receipt of clinical preventive services. OBJECTIVE: To evaluate whether differences in insurance coverage of physician office visits influences the receipt of cancer screening in women who have full coverage for the screening services. DESIGN: Cohort study of women enrolled in fee-for-service (FFS) or Preferred Provider Organization (PPO) health plans, where FFS plans have less generous office visit coverage, for the period 1995 to 1997. SETTINGS AND PARTICIPANTS: General Motors Corporation's employees and their dependents. MAIN OUTCOME MEASURES: Papanicolaou and mammography rates in women aged 21 to 64 years (n = 139,294) and 52 to 64 years (n = 56,554), respectively. RESULTS: Compared with FFS plans, enrollees in PPO plans were significantly more likely to obtain a Papanicolaou smear and mammogram (adjusted relative risk [RRa] = 1.22; 95% CI, 1.21-1.24; and RRa, 1.17; 95% CI, 1.15-1.18, respectively). The association was more pronounced among hourly individuals (RRa, 1.27; 95% CI, 1.26-1.29 for Papanicolaou smears; RRa, 1.17; 95% CI, 1.16-1.19 for mammograms) than among salaried individuals (RRa, 1.10; 95% CI, 1.08-1.12 for Papanicolaou smears and RRa, 1.10; 95% CI, 1.06-1.12 for mammograms), corresponding to a greater differential in office visit coverage among the hourly group. CONCLUSIONS: Benefit structure appears to have an important effect on receipt of cancer screening in women. The findings highlight the need to ensure that future reforms of the health care system do not adversely affect the use of preventive services.

Adult↗

PubMed2002

Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports.

PURPOSE: To evaluate translation of chest radiographic reports by using natural language processing and to compare the findings with those in the literature. MATERIALS AND METHODS: A natural language processor coded 10 years of narrative chest radiographic reports from an urban academic medical center. Coding for 150 reports was compared with manual coding. Frequencies and co-occurrences of 24 clinical conditions (diseases, abnormalities, and clinical states) were estimated. The ratio of right to left lung mass, association of pleural effusion with other conditions, and frequency of bullet and stab wounds were compared with independent observations. The sensitivity and specificity of the system's pneumothorax coding were compared with those of manual financial coding. RESULTS: The system coded 889,921 reports on 251,186 patients. On the basis of manual coding of 150 reports, the processor's sensitivity (0.81) and specificity (0.99) were comparable to those previously reported for natural language processing and for expert coders. The frequencies of the selected conditions ranged from 0.22 for pleural effusion to 0.0004 for tension pneumothorax. The database confirmed earlier observations that lung cancer occurs in a 3:2 right-to-left ratio. The association of pleural effusion with other conditions mirrored that in the literature. Bullet and stab wounds decreased during 10 years at a rate consistent with crime statistics. A review of pneumothorax cases showed that the database (sensitivity, 1.00; specificity, 0.996) was more accurate than financial discharge coding (sensitivity, 0.17; P =.002; specificity, 0.996; not significant). CONCLUSION: Internal and external validation in this study confirmed the accuracy of natural language processing for translating chest radiographic narrative reports into a large database of information.

Databases, Factual↗

PubMed2002

A comparison of the Charlson comorbidities derived from medical language processing and administrative data.

The objective of this study was to develop a medical language processing (MLP) system, which consisted of MedLEE and a set of inference rules, to identify 19 Charlson comorbidities from discharge summaries and chest x-ray reports. We used 233 cases to learn the patterns that were indicative of comorbidities for developing the inference rules. We then used an independent data set of 3,662 pneumonia patients to identify comorbidities by MLP compared with administrative data (ICD-9 codes). A stratified random sample of 190 records from disagreement cases was manually reviewed. The sensitivity, specificity, and accuracy for the MLP system/ICD-9 codes in this testing set were 0.84/0.16, 0.70/0.30, and 0.77/0.23 respectively. Thirteen of the 19 comorbidities studied were underreported in the administrative data. The kappa values ranged from 0.19 for peptic ulcer to 0.70 for lymphoma. We conclude that comorbidities derived from natural language processing of medical records can improve ICD-9-based approaches.

Adult↗

PubMed2002

Representing nested semantic information in a linear string of text using XML.

XML has been widely adopted as an important data interchange language. The structure of XML enables sharing of data elements with variable degrees of nesting as long as the elements are grouped in a strict tree-like fashion. This requirement potentially restricts the usefulness of XML for marking up written text, which often includes features that do not properly nest within other features. We encountered this problem while marking up medical text with structured semantic information from a Natural Language Processor. Traditional approaches to this problem separate the structured information from the actual text mark up. This paper introduces an alternative solution, which tightly integrates the semantic structure with the text. The resulting XML markup preserves the linearity of the medical texts and can therefore be easily expanded with additional types of information.

Programming Languages↗

PubMed2002

A study of abbreviations in MEDLINE abstracts.

Abbreviations are widely used in writing, and the understanding of abbreviations is important for natural language processing applications. Abbreviations are not always defined in a document and they are highly ambiguous. A knowledge base that consists of abbreviations with their associated senses and a method to resolve the ambiguities are needed. In this paper, we studied the UMLS coverage, textual variants of senses, and the ambiguity of abbreviations in MEDLINE abstracts. We restricted our study to three-letter abbreviations which were defined using parenthetical expressions. When grouping similar expansions together and representing senses using groups, we found that after ignoring senses where the total number of occurrences within the corresponding group was less than 100, 82.8% of the senses matched the UMLS, covered over 93% of occurrences that were considered, and had an average of 7.74 expansions for each sense. Abbreviations are highly ambiguous: 81.2% of the abbreviations were ambiguous, and had an average of 16.6 senses. However, after ignoring senses with occurrences of less than 5, 64.6% of the abbreviations were ambiguous, and had an average of 4.91 senses.

Abbreviations as Topic↗

PubMed2002

Automatic extraction of gene and protein synonyms from MEDLINE and journal articles.

Genes and proteins are often associated with multiple names, and more names are added as new functional or structural information is discovered. Because authors often alternate between these synonyms, information retrieval and extraction benefits from identifying these synonymous names. We have developed a method to extract automatically synonymous gene and protein names from MEDLINE and journal articles. We first identified patterns authors use to list synonymous gene and protein names. We developed SGPE (for synonym extraction of gene and protein names), a software program that recognizes the patterns and extracts from MEDLINE abstracts and full-text journal articles candidate synonymous terms. SGPE then applies a sequence of filters that automatically screen out those terms that are not gene and protein names. We evaluated our method to have an overall precision of 71% on both MEDLINE and journal articles, and 90% precision on the more suitable full-text articles alone

Electronic Data Processing↗

PubMedDate not supplied

Mapping abbreviations to full forms in biomedical articles.

OBJECTIVE: To develop methods that automatically map abbreviations to their full forms in biomedical articles. METHODS: The authors developed two methods of mapping defined and undefined abbreviations (defined abbreviations are paired with their full forms in the articles, whereas undefined ones are not). For defined abbreviations, they developed a set of pattern-matching rules to map an abbreviation to its full form and implemented the rules into a software program, AbbRE (for "abbreviation recognition and extraction"). Using the opinions of domain experts as a reference standard, they evaluated the recall and precision of AbbRE for defined abbreviations in ten biomedical articles randomly selected from the ten most frequently cited medical and biological journals. They also measured the percentage of undefined abbreviations in the same set of articles, and they investigated whether they could map undefined abbreviations to any of four public abbreviation databases (GenBank LocusLink, SWISSPROT, LRABR of the UMLS Specialist Lexicon, and BioABACUS). RESULTS: AbbRE had an average 0.70 recall and 0.95 precision for the defined abbreviations. The authors found that an average of 25 percent of abbreviations were defined in biomedical articles and that of a randomly selected subset of undefined abbreviations, 68 percent could be mapped to any of four abbreviation databases. They also found that many abbreviations are ambiguous (i.e., they map to more than one full form in abbreviation databases). CONCLUSION: AbbRE is efficient for mapping defined abbreviations. To couple AbbRE with abbreviation databases for the mapping of undefined abbreviations, not only exhaustive abbreviation databases but also a method to resolve the ambiguity of abbreviations in the databases are needed.

Abbreviations as Topic↗

PubMedDate not supplied

Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the UMLS.

UNLABELLED: Motivation. The UMLS has been used in natural language processing applications such as information retrieval and information extraction systems. The mapping of free-text to UMLS concepts is important for these applications. To improve the mapping, we need a method to disambiguate terms that possess multiple UMLS concepts. In the general English domain, machine-learning techniques have been applied to sense-tagged corpora, in which senses (or concepts) of ambiguous terms have been annotated (mostly manually). Sense disambiguation classifiers are then derived to determine senses (or concepts) of those ambiguous terms automatically. However, manual annotation of a corpus is an expensive task. We propose an automatic method that constructs sense-tagged corpora for ambiguous terms in the UMLS using MEDLINE abstracts. METHODS: For a term W that represents multiple UMLS concepts, a collection of MEDLINE abstracts that contain W is extracted. For each abstract in the collection, occurrences of concepts that have relations with W as defined in the UMLS are automatically identified. A sense-tagged corpus, in which senses of W are annotated, is then derived based on those identified concepts. The method was evaluated on a set of 35 frequently occurring ambiguous biomedical abbreviations using a gold standard set that was automatically derived. The quality of the derived sense-tagged corpus was measured using precision and recall. RESULTS: The derived sense-tagged corpus had an overall precision of 92.9% and an overall recall of 47.4%. After removing rare senses and ignoring abbreviations with closely related senses, the overall precision was 96.8% and the overall recall was 50.6%. CONCLUSIONS: UMLS conceptual relations and MEDLINE abstracts can be used to automatically acquire knowledge needed for resolving ambiguity when mapping free-text to UMLS concepts.

Abbreviations as Topic↗