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PubMedDate not supplied

Trends and pitfalls with nomenclatures and classifications in medicine.

This paper reflects some of the main points in the history and the trends of classifications, nomenclatures and knowledge representation models in medicine. The relations with philosophical background are briefly related. The contributions in this field during the Medical Informatics Europe (MIE) conference in 1997 are presented. International collaborations in the field of lexico-semantic resources and inter-disciplinary partnership in natural language processing technologies are necessary steps to achieve definitive results that will lead to applications in the daily medical practice.

Algorithms↗
PubMed1998

Automatic semantic interpretation of anatomic spatial relationships in clinical text.

A set of semantic interpretation rules to link the syntax and semantics of locative relationships among anatomic entities was developed and implemented in a natural language processing system. Two experiments assessed the ability of the system to identify and characterize physico-spatial relationships in coronary angiography reports. Branching relationships were by far the most common observed (75%), followed by PATH (20%) and PART/WHOLE relationships. Recall and precision scores were 0.78 and 0.67 overall, suggesting the viability of this approach in semantic processing of clinical text.

Anatomy↗
PubMedDate not supplied

Using nurses' natural language entries to build a concept-oriented terminology for patients' chief complaints in the emergency department.

Information about the chief complaint (CC), also known as the patient's reason for seeking emergency care, is critical for patient prioritization for treatment and determination of patient flow through the emergency department (ED). Triage nurses document the CC at the start of the ED visit, and the data are increasingly available in electronic form. Despite the clinical and operational significance of the CC to the ED, there is no standard CC terminology. We propose the construction of concept-oriented nursing terminologies from the actual language used by experts. We use text analysis to extract CC concepts from triage nurses' natural language entries. Our methodology for building the nursing terminology utilizes natural language processing techniques and the Unified Medical Language System.

Computational Biology↗
PubMed2001

Enabling clinicians, researchers, and educators to build custom web-based biomedical information systems.

We describe an open-source toolkit that enables clinicians, researchers, and educators to build their own web-based biomedical information systems. The Web Interfacing Repository Manager (Wirm) is a high-level application server aimed at medical professionals, allowing them to create individually tailored systems for managing their multimedia data and knowledge. We provide an overview of the features of Wirm, explaining how they meet the requirements for supporting biomedical information management, and describe four applications that are currently being developed with Wirm: MyPACS, a teaching file authoring system for radiologists, Fathom, an experiment management system for natural language processing, the Digital Anatomist Repository, an image archiving tool for medical schools, and Ontolog, a browser for medical vocabularies.

Anatomy↗
PubMed2004

Content-rich biological network constructed by mining PubMed abstracts.

BACKGROUND: The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public. RESULTS: We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. CONCLUSIONS: Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.

Biology↗
PubMed2006

Bio-Ontology and text: bridging the modeling gap.

MOTIVATION: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. RESULTS: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). AVAILABILITY: http://zellig.cpmc.columbia.edu/PGschema

Abstracting and Indexing↗
PubMed2004

Automated medical problem list generation: towards a patient timeline.

The problem-oriented electronic medical record has been investigated as an alternative to source-oriented organization of patient data. At the core of a problem-oriented view is the medical problem list. Maintenance of the medical problem list is often manual, making it highly user dependent. We detail the beginnings of an automated medical problem list generator based on ICD-9: given a set of ICD-9 codes associated with a patient record, the system maps the codes (and related data) to an anatomy-centric hierarchy. 1 million patient encounters from an outpatient setting were reviewed to generate a unique set of 7,890 ICD-9 codes. Natural language processing of the ICD-9 string descriptions identified 1,981 anatomical terms, which were subsequently mapped to one of 21 anatomical categories. The output of the medical problem list generator was then used to create a problem-oriented, gestalt view of a patient's medical record. Preliminary evaluation of the generator revealed 100% recall, but only 60% precision. This initial work has highlighted several issues in defining a medical problem list, including questions of granularity and performance trade-offs.

Electronic Data Processing↗
PubMed1998

Galen-In-Use: using artificial intelligence terminology tools to improve the linguistic coherence of a national coding system for surgical procedures.

GALEN has developed a language independent common reference model based on a medically oriented ontology and practical tools and techniques for managing healthcare terminology including natural language processing. GALEN-IN-USE is the current phase which applied the modelling and the tools to the development or the updating of coding systems for surgical procedures in different national coding centers co-operating within the European Federation of Coding Centre (EFCC) to create a language independent knowledge repository for multicultural Europe. We used an integrated set of artificial intelligence terminology tools named CLAssification Manager workbench to process French professional medical language rubrics into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation we generate controlled French natural language. The French national coding centre is then able to retrieve the initial professional rubrics with different categories of concepts, to compare the professional language proposed by expert clinicians to the French generated controlled vocabulary and to finalize the linguistic labels of the coding system in relation with the meanings of the conceptual system structure.

Artificial Intelligence↗
PubMed2005

Building medical ontologies based on terminology extraction from texts: an experimentation in pneumology.

Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri do not fit computer processing. We think the automation of the coding task requires a conceptual modelling of medical items: an ontology. Our objective is to help pneumologists code acts and diagnoses with a software that represents medical knowledge by an ontology of the concerned specialty. The main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. In this paper, our objective is twofold: we have to build the ontology of pneumology and we want to develop a methodology for the knowledge engineer to build various types of medical ontologies based on terminology extraction from texts.

Humans↗
PubMed2002

Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System.

Given the post September 11th climate of possible bioterrorist attacks and the high profile 2002 Winter Olympics in the Salt Lake City, Utah, we challenged ourselves to deploy a computer-based real-time automated biosurveillance system for Utah, the Utah Real-time Outbreak and Disease Surveillance system (Utah RODS), in six weeks using our existing Real-time Outbreak and Disease Surveillance (RODS) architecture. During the Olympics, Utah RODS received real-time HL-7 admission messages from 10 emergency departments and 20 walk-in clinics. It collected free-text chief complaints, categorized them into one of seven prodromes classes using natural language processing, and provided a web interface for real-time display of time series graphs, geographic information system output, outbreak algorithm alerts, and details of the cases. The system detected two possible outbreaks that were dismissed as the natural result of increasing rates of Influenza. Utah RODS allowed us to further understand the complexities underlying the rapid deployment of a RODS-like system.

Algorithms↗
PubMed1993

Automated linkage of free-text descriptions of patients with a practice guideline.

The process of applying a practice guideline to a patient requires a great deal of clinical data. AAPT (Appropriateness-Assessment Processing from Text) is an experimental computer program that can assess the appropriateness of coronary-artery bypass grafting surgery (CABG) in patients with coronary-artery disease (CAD) and chronic stable angina from the admission summaries of those patients. The AAPT architecture combines natural-language processing (NLP) and probabilistic inference. The NLP module identifies single clinical concepts of interest in the free-text document. The probabilistic inference module, a Bayesian belief network, estimates values for variables not specifically mentioned. AAPT produces a patient's summary of CAD that is similar to a manually generated clinical summary. Work is ongoing to improve AAPT and evaluate it as a tool to assist in the dissemination of guidelines and as a tool to encourage adherence to practice guidelines.

Angina Pectoris↗
PubMed1999

Mining molecular binding terminology from biomedical text.

Automatic access to information regarding macromolecular binding relationships would provide a valuable resource to the biomedical community. We report on a pilot project to mine such information from the molecular biology literature. The program being developed takes advantage of natural language processing techniques and is supported by two repositories of biomolecular knowledge. A formative evaluation has been conducted on a subset of MEDLINE abstracts.

Algorithms↗
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↗
PubMed2005

System architecture for temporal information extraction, representation and reasoning in clinical narrative reports.

Exploring temporal information in narrative Electronic Medical Records (EMRs) is essential and challenging. We propose an architecture for an integrated approach to process temporal information in clinical narrative reports. The goal is to initiate and build a foundation that supports applications which assist healthcare practice and research by including the ability to determine the time of clinical events (e.g., past vs. present). Key components include: (1) an annotation schema for temporal expressions and the development of an associated tagger; (2) a natural language processing (NLP) system for encoding and extracting medical events and associating them with formalized temporal data; (3) a post-processor, with a knowledge-based subsystem to help discover implicit information, that resolves temporal expressions and deals with issues such as granularity and vagueness; and (4) a reasoning mechanism which models clinical reports as Simple Temporal Problems (STPs).

Humans↗
PubMed2005

Using NLP to extract concepts from chief complaints.

The NLM's MMTx natural language processing (NLP) engine was used to extract concepts from chief complaints entered into an ambulatory electronic medical record (EMR). Of the over 600,000 strings submitted to the process, approximately 25% were assigned at least one concept, with a rate of 2% for incorrect assignments.

Abstracting and Indexing↗
PubMed1998

Knowledge discovery and data mining to assist natural language understanding.

As natural language processing systems become more frequent in clinical use, methods for interpreting the output of these programs become increasingly important. These methods require the effort of a domain expert, who must build specific queries and rules for interpreting the processor output. Knowledge discovery and data mining tools can be used instead of a domain expert to automatically generate these queries and rules. C5.0, a decision tree generator, was used to create a rule base for a natural language understanding system. A general-purpose natural language processor using this rule base was tested on a set of 200 chest radiograph reports. When a small set of reports, classified by physicians, was used as the training set, the generated rule base performed as well as lay persons, but worse than physicians. When a larger set of reports, using ICD9 coding to classify the set, was used for training the system, the rule base performed worse than the physicians and lay persons. It appears that a larger, more accurate training set is needed to increase performance of the method.

Artificial Intelligence↗
PubMed1996

Recognizing noun phrases in medical discharge summaries: an evaluation of two natural language parsers.

We evaluated the ability of two natural language parsers, CLARIT and the Xerox Tagger, to identify simple, noun phrases in medical discharge summaries. In twenty randomly selected discharge summaries, there were 1909 unique simple noun phrases. CLARIT and the Xerox Tagger exactly identified 77.0% and 68.7% of the phrases, respectively, and partially identified 85.7% and 80.8% of the phrases. Neither system had been specially modified or tuned to the medical domain. These results suggest that it is possible to apply existing natural language processing (NLP) techniques to large bodies of medical text, in order to empirically identify the terminology used in medicine. Virtually all the noun phrases could be regarded as having special medical connotation and would be candidates for entry into a controlled medical vocabulary.

Medical Records↗
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↗