Biomedical subjects

Lyudmila Shagina

Publications and source records attributed to Lyudmila Shagina.

4 recordsLinked to original sources

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↗

PubMed2005

Extracting information on pneumonia in infants using natural language processing of radiology reports.

Natural language processing (NLP) is critical for improvement of the healthcare process because it can encode clinical data in patient documents. Many clinical applications such as decision support require coded data to function appropriately. However, in order to be applicable for healthcare, performance must be adequate. A valuable automated application is the detection of infectious diseases, such as surveillance of pneumonia in newborns (e.g., neonates) because the disease produces significant rates of morbidity and mortality, and manual surveillance is challenging. Studies have demonstrated that automated surveillance using NLP is a useful adjunct to manual surveillance and an effective tool for infection control practitioners. This paper presents a study evaluating the feasibility of an NLP-based monitoring system to screen for healthcare-associated pneumonia in neonates. We estimated sensitivity, specificity, and positive predictive value by comparing results with clinicians' judgments. Sensitivity was 71% and specificity was 99%. Our results demonstrated that the automated method was feasible.

Database Management Systems↗

PubMed2004

Automated encoding of clinical documents based on natural language processing.

OBJECTIVE: The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method. METHODS: An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts. RESULTS: Recall of the system for UMLS coding of all terms was .77 (95% CI.72-.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79-.87). Recall of the system for extracting all terms was .84 (.81-.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87-.91), and precision of the experts ranged from .61 to .91. CONCLUSION: Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.

Abstracting and Indexing↗

PubMed2003

A vocabulary development and visualization tool based on natural language processing and the mining of textual patient reports.

Medical terminologies are critical for automated healthcare systems. Some terminologies, such as the UMLS and SNOMED are comprehensive, whereas others specialize in limited domains (i.e., BIRADS) or are developed for specific applications. An important feature of a terminology is comprehensive coverage of relevant clinical terms and ease of use by users, which include computerized applications. We have developed a method for facilitating vocabulary development and maintenance that is based on utilization of natural language processing to mine large collections of clinical reports in order to obtain information on terminology as expressed by physicians. Once the reports are processed and the terms structured and collected into an XML representational schema, it is possible to determine information about terms, such as frequency of occurrence, compositionality, relations to other terms (such as modifiers), and correspondence to a controlled vocabulary. This paper describes the method and discusses how it can be used as a tool to help vocabulary builders navigate through the terms physicians use, visualize their relations to other terms via a flexible viewer, and determine their correspondence to a controlled vocabulary.

Algorithms↗