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The Unified Medical Language System (UMLS): Integrating Biomedical Terminology
, 2004
"... The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900 000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million ..."
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Cited by 350 (37 self)
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The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900 000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP.
J: Answering clinical questions with knowledgebased and statistical techniques
- Computational Linguistics
"... The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical me ..."
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Cited by 60 (10 self)
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The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidencebased medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians ’ questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline. 1.
Getting to the (C)Ore of Knowledge: Mining Biomedical Literature
- Int J Med Inform
"... Literature mining is the process of extracting and combining facts from scientific publications. In recent years, many computer programs have been designed to extract various molecular biology findings from Medline abstracts or full-text articles. The present article describes the range of text mini ..."
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Cited by 52 (1 self)
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Literature mining is the process of extracting and combining facts from scientific publications. In recent years, many computer programs have been designed to extract various molecular biology findings from Medline abstracts or full-text articles. The present article describes the range of text mining techniques that have been applied to scientific documents. It divides ‘automated reading ’ into four general subtasks: text categorization, named entity tagging, fact extraction, and collection-wide analysis. Literature mining offers powerful methods to support knowledge discovery and the construction of topic maps and ontologies. An overview is given of recent developments in medical language processing. Special attention is given to the domain particularities of molecular biology, and the emerging synergy between literature mining and molecular databases accessible through Internet.
Analysis of Semantic Classes in Medical Text for Question Answering
, 2004
"... To answer questions from clinical-evidence texts, we identify occurrences of the semantic classes --- disease, medication, patient outcome --- that are candidate elements of the answer, and the relations among them. Additionally, we determine whether an outcome is positive or negative. ..."
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Cited by 32 (2 self)
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To answer questions from clinical-evidence texts, we identify occurrences of the semantic classes --- disease, medication, patient outcome --- that are candidate elements of the answer, and the relations among them. Additionally, we determine whether an outcome is positive or negative.
Knowledge-based query expansion to support scenario-specific retrieval of medical free text
- Information Retrieval
, 2005
"... In retrieving medical free text, users are often interested in answers pertinent to certain scenarios that correspond to common tasks performed in medical practice, e.g., treatment ordiagnosis of a disease. A major challenge in handling such queries is that scenario terms in the query (e.g. treatmen ..."
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Cited by 28 (0 self)
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In retrieving medical free text, users are often interested in answers pertinent to certain scenarios that correspond to common tasks performed in medical practice, e.g., treatment ordiagnosis of a disease. A major challenge in handling such queries is that scenario terms in the query (e.g. treatment) are often too general to match specialized terms in relevant documents (e.g. chemotherapy). In this paper, we propose a knowledge-based query expansion method that exploits the UMLS knowledge source to append the original query with additional terms that are specifically relevant to the query’s scenario(s). We compared the proposed method with traditional statistical expansion that expands terms which are statistically correlated but not necessarily scenario specific. Our study on two standard testbeds shows that the knowledge-based method, by providing scenario-specific expansion, yields notable improvements over the statistical method in terms of average precision-recall. On the OHSUMED testbed, for example, the improvement is more than 5 % averaging over all scenario-specific queries studied and about 10 % for queries that mention certain scenarios, such astreatment of a disease anddifferential diagnosis of a symptom/disease. 1
The open biomedical annotator
- Summit Trans Bioinformatics
, 2009
"... The range of publicly available biomedical data is enormous and is expanding fast. This expansion means that researchers now face a hurdle to extracting the data they need from the large numbers of data that are available. Biomedical researchers have turned to ontologies and terminologies to structu ..."
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Cited by 24 (9 self)
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The range of publicly available biomedical data is enormous and is expanding fast. This expansion means that researchers now face a hurdle to extracting the data they need from the large numbers of data that are available. Biomedical researchers have turned to ontologies and terminologies to structure and annotate their data with ontology concepts for better search and retrieval. However, this annotation process cannot be easily automated and often requires expert curators. Plus, there is a lack of easy-to-use systems that facilitate the use of ontologies for annotation. This paper presents the Open Biomedical Annotator (OBA), an ontologybased Web service that annotates public datasets
Answer Extraction, Semantic Clustering, and Extractive Summarization for Clinical Question Answering
- Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL
, 2006
"... This paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval. ..."
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Cited by 21 (3 self)
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This paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval.
MeSH Up: effective MeSH text classification for improved document retrieval
- Bioinformatics
, 2009
"... Motivation: Controlled vocabularies such as the Medical Subject Headings (MeSH) thesaurus and the Gene Ontology (GO) provide an efficient way of accessing and organizing biomedical information by reducing the ambiguity inherent to free-text data. Different methods of automating the assignment of MeS ..."
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Cited by 20 (2 self)
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Motivation: Controlled vocabularies such as the Medical Subject Headings (MeSH) thesaurus and the Gene Ontology (GO) provide an efficient way of accessing and organizing biomedical information by reducing the ambiguity inherent to free-text data. Different methods of automating the assignment of MeSH concepts have been proposed to replace manual annotation, but they are either limited to a small subset of MeSH or have only been compared to a limited number of other systems. Results: We compare the performance of 6 MeSH classification systems (MetaMap, EAGL, a language and a vector space model based approach, a K-Nearest Neighbor approach and MTI) in terms of reproducing and complementing manual MeSH annotations. A K-Nearest Neighbor system clearly outperforms the other published approaches and scales well with large amounts of text using the full MeSH thesaurus. Our measurements demonstrate to what extent manual MeSH annotations can be reproduced and how they can be complemented by automatic annotations. We also show that a statistically significant improvement can be obtained in information retrieval (IR) when the text of a user’s query is automatically annotated with MeSH concepts, compared to using the original textual query alone. Conclusions: The annotation of biomedical texts using controlled vocabularies such as MeSH can be automated to improve text-only IR. Furthermore, the automatic MeSH annotation system we propose is highly scalable and it generates improvements in IR comparable to those observed for manual annotations. Contact:
Word sense disambiguation by selecting the best semantic type based on Journal Descriptor Indexing: preliminary experiment
- J. Am. Soc. Inform. Sci. Tech
, 2006
"... An experiment was performed at the National Library of Medicine ® (NLM ® ) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM’s MetaMap system, which maps free text to terms correspondi ..."
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Cited by 19 (1 self)
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An experiment was performed at the National Library of Medicine ® (NLM ® ) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM’s MetaMap system, which maps free text to terms corresponding to concepts in NLM’s Unified Medical Language System ® (UMLS ® ) Metathesaurus ®. If the text maps to more than one Metathesaurus concept at the same high confidence score, MetaMap has no way of knowing which concept is the correct mapping. We describe the JDI methodology, which is ultimately based on statistical associations between words in a training set of MEDLINE ® citations and a small set of journal descriptors (assigned by humans to journals per se) assumed to be inherited by the citations. JDI is the
Generative Content Models for Structural Analysis of Medical Abstracts
"... The ability to accurately model the content structure of text is important for many natural language processing applications. This paper describes experiments with generative models for analyzing the discourse structure of medical abstracts, which generally follow the pattern of “introduction”, “met ..."
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Cited by 18 (1 self)
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The ability to accurately model the content structure of text is important for many natural language processing applications. This paper describes experiments with generative models for analyzing the discourse structure of medical abstracts, which generally follow the pattern of “introduction”, “methods”, “results”, and “conclusions”. We demonstrate that Hidden Markov Models are capable of accurately capturing the structure of such texts, and can achieve classification accuracy comparable to that of discriminative techniques. In addition, generative approaches provide advantages that may make them preferable to discriminative techniques such as Support Vector Machines under certain conditions. Our work makes two contributions: at the application level, we report good performance on an interesting task in an important domain; more generally, our results contribute to an ongoing discussion regarding the tradeoffs between generative and discriminative techniques.
