Marcelo Fiszman

The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text

Interpretation of semantic propositions in free-text documents such as MEDLINE citations would provide valuable support for biomedical applications, and several approaches to semantic interpretation are being pursued in the biomedical informatics community. In this paper, we describe a methodology for interpreting linguistic structures that encode hypernymic propositions, in which a more specific concept is in a taxonomic relationship with a more general concept. In order to effectively process these constructions, we exploit underspecified syntactic analysis and structured domain knowledge from the Unified Medical Language System (UMLS). After introducing the syntactic processing on which our system depends, we focus on the UMLS knowledge that supports interpretation of hypernymic propositions. We first use semantic groups from the Semantic Network to ensure that the two concepts involved are compatible; hierarchical information in the Metathesaurus then determines which concept is more general and which more specific. A preliminary evaluation of a sample based on the semantic group Chemicals and Drugs provides 83% precision. An error analysis was conducted and potential solutions to the problems encountered are presented. The research discussed here serves as a paradigm for investigating the interaction between domain knowledge and linguistic structure in natural language processing, and could also make a contribution to research on automatic processing of discourse structure. Additional implications of the system we present include its integration in advanced semantic interpretation processors for biomedical text and its use for information extraction in specific domains. The approach has the potential to support a range of applications, including information retrieval and ontology engineering.

Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports

OBJECTIVE To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports. METHODS DESIGN Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured. OUTCOME MEASURES Recall, precision, specificity, and agreement (using Finns R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia. RESULTS Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference. CONCLUSION In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.

SemMedDB: a PubMed-scale repository of biomedical semantic predications

SUMMARY Effective access to the vast biomedical knowledge present in the scientific literature is challenging. Semantic relations are increasingly used in knowledge management applications supporting biomedical research to help address this challenge. We describe SemMedDB, a repository of semantic predications (subject-predicate-object triples) extracted from the entire set of PubMed citations. We propose the repository as a knowledge resource that can assist in hypothesis generation and literature-based discovery in biomedicine as well as in clinical decision-making support. AVAILABILITY AND IMPLEMENTATION The SemMedDB repository is available as a MySQL database for non-commercial use at http://skr3.nlm.nih.gov/SemMedDB. An UMLS Metathesaurus license is required. CONTACT kilicogluh@mail.nih.gov.

Abstraction summarization for managing the biomedical research literature

We explore a semantic abstraction approach to automatic summarization in the biomedical domain. The approach relies on a semantic processor that functions as the source interpreter and produces a list of predications. A transformation stage then generalizes and condenses this list, ultimately generating a conceptual condensate for a disorder input topic. The final condensate is displayed in graphical form. We provide a set of principles for the transformation stage and describe the application of this approach to multidocument input. Finally, we examine the characteristics and quality of the condensates produced.

MPLUS: a probabilistic medical language understanding system

This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+.

Extracting semantic predications from Medline citations for pharmacogenomics.

We describe a natural language processing system (Enhanced SemRep) to identify core assertions on pharmacogenomics in Medline citations. Extracted information is represented as semantic predications covering a range of relations relevant to this domain. The specific relations addressed by the system provide greater precision than that achievable with methods that rely on entity co-occurrence. The development of Enhanced SemRep is based on the adaptation of an existing system and crucially depends on domain knowledge in the Unified Medical Language System. We provide a preliminary evaluation (55% recall and 73% precision) and discuss the potential of this system in assisting both clinical practice and scientific investigation.

Automatic summarization of MEDLINE citations for evidence-based medical treatment: A topic-oriented evaluation

As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p<0.01) and the increase in the overall score of clinical usefulness was 0.39 (p<0.05).

Semantic MEDLINE: An advanced information management application for biomedicine

To support more effective biomedical information management, Semantic MEDLINE integrates document retrieval, advanced natural language processing, automatic summarization and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected graph of semantic relations, with links to the original MEDLINE citations. The ability to connect salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Semantic MEDLINE can make an impact on biomedicine by supporting scientific discovery and the timely translation of insights from basic research into advances in clinical practice and patient care. Semantic MEDLINE is illustrated here with recent research on the clock genes.

Semantic Interpretation for the Biomedical Research Literature

Natural language processing is increasingly used to support biomedical applications that manipulate information rather than documents. Examples include automatic summarization, question answering, and literature-based scientific discovery. Semantic processing is a method of automatic language analysis that identifies concepts and relationships to represent document content. The identification of this information depends on structured knowledge, and in the biomedical domain, one such resource is the Unified Medical Language System. After providing some linguistic background, we discuss several semantic interpretation systems being developed in biomedicine. Finally, we briefly investigate two applications that exploit semantic information in MEDLINE citations; one focuses on automatic summarization and the other is directed at information extraction for molecular biology research.

Text summarization in the biomedical domain

OBJECTIVE The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. MATERIALS AND METHODS MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation. RESULTS Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural language processing (17; 50%) and a hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. DISCUSSION This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization. CONCLUSION Recent research has focused on a hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.