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PubMed2001

Bidirectional incremental parsing for automatic pathway identification with combinatory categorial grammar.

As the importance of automatically extracting and analyzing various natural language assertions about protein-protein interactions in biomedical publications is recognized, many uses of natural language processing techniques are proposed in the literature. However, most proposals to date make rather simplifying assumptions about the syntactic aspects of natural language due to various reasons including efficiency. In this paper, we describe an implemented system that utilizes combinatory categorical grammar known to be competent in modeling natural language, with a controlled mechanism for the parser to operate bidirectionally and incrementally. We discuss the performance of the system on a large set of abstracts in Medline with quite encouraging results.

Data Interpretation, Statistical↗
PubMed1999

Galen: a third generation terminology tool to support a multipurpose national coding system for surgical procedures.

GALEN has developed a new generation of terminology tools based on a language independent concept reference model using a compositional formalism allowing computer processing and multiple reuses. During the 4th framework program project Galen-In-Use we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures (CCAM) in France. On one hand we contributed to a language independent knowledge repository for multicultural Europe. On the other hand we support the traditional process for creating a new coding system in medicine which is very much labour consuming by artificial intelligence tools using a medically oriented recursive ontology and natural language processing. We used an integrated software named CLAW to process French professional medical language rubrics produced by the national colleges of surgeons into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation on one hand we generate controlled French natural language to support the finalization of the linguistic labels in relation with the meanings of the conceptual system structure. On the other hand the classification manager of third generation proves to be very powerful to retrieve the initial professional rubrics with different categories of concepts within a semantic network.

Abstracting and Indexing↗
PubMed2002

Brain signatures of artificial language processing: evidence challenging the critical period hypothesis.

Adult second language learning seems to be more difficult and less efficient than first language acquisition during childhood. By using event-related brain potentials, we show that adults who learned a miniature artificial language display a similar real-time pattern of brain activation when processing this language as native speakers do when processing natural languages. Participants trained in the artificial language showed two event-related brain potential components taken to reflect early automatic and late controlled syntactic processes, whereas untrained participants did not. This result challenges the common view that late second language learners process language in a principally different way from native speakers. Our findings demonstrate that a small system of grammatical rules can be syntactically instantiated by the adult speaker in a way that strongly resembles native-speaker sentence processing.

Adult↗
PubMed1995

Word segmentation processing: a way to exponentially extend medical dictionaries.

One of the most critical problems of automatic natural language processing (NLP) is the size of the medical lexicons. The set of compound medical words and the continual creation of new terms renders medical lexicons exhaustive beyond question. The structure of such dictionaries usually consists of two parts: 1) the morphological and sometimes syntactical information necessary to identify, on a grapheme level, a given word in a sentence, and 2) the part often devoted to conceptual knowledge associated with the recognized word. It is only when these two prerequisites are fulfilled that an attempt to understand the meaning of a whole expression is possible. The approach developed in this paper is a pragmatic way to rapidly increase the lexico-semantic part of medical dictionaries. We developed a semi-automatic tool, as a prototype to demonstrate the feasibility of this approach. This tool is able to translate almost any diagnosis expressed in French into its equivalent in the ICD-9CM coding scheme.

Dictionaries, Medical as Topic↗
PubMed2002

The horizontal and vertical nature of patient phenotype retrieval: new directions for clinical text processing.

The author reviews the historical problem of identifying appropriate patients for retrieval from a clinical repository of patient records, compares the competing features of document classification and natural language processing, and proposes an alternative approach. The alternative approach 1) codes inquiries in an ontology to lend a vertical axis to retrieval knowledge instead of coding the target body of notes, 2) invokes natural language indexing and lexical normalizations on the corpus of notes that is scalable and tractable, and 3) leverages thesauri of word-level synonyms and near-synonyms to expand term searches "horizontally" around the concept spaces drawn from the ontology in which the queries were "coded."

Abstracting and Indexing↗
PubMed2000

Limited parsing of notational text visit notes: ad-hoc vs. NLP approaches.

This paper describes the extraction of structured data relevant to glaucoma diagnosis and progression from visit notes typed as "notational text" by ophthalmologists during patient encounters. We compared two text processing systems: a limited pattern matching system called GDP (Glaucoma Dedicated Parser) and MedLEE, a proven natural language processing system which is in routine use encoding findings from chest radiograph and mammogram reports at the New York-Presbyterian hospital's Columbia-Presbyterian Center. We also evaluated the use of GDP as a preprocessor program to transform notational text into constructions recognizable by MedLEE. These systems have been evaluated according to their recall and precision in the particular task of processing a corpus of "notational text" documents to extract information related to glaucoma disease.

Glaucoma↗
PubMed2001

Structure localization in brain images: application to relevant image selection.

Recent advances in imaging have lead to increases in the number of images/study. Automated methods to select relevant images are critical to effectively convey study results. The proposed method combines natural language processing (NLP) and automatic structure localization to identify relevant images of a MR brain study. NLP extracts relevant locations of findings. Two algorithms were implemented and evaluated for structure localization. The first method involves registration of patient dataset to a labeled atlas. The second method involves an eigenimage search using a training set of images. A prototype was developed and tested on MR brain studies of nine patients. With the registration method, slices containing the relevant structure agreed with expert selection in 98% of cases. Structure localization by eigenimage search was able to locate the lateral ventricles correctly in all the test cases. The proposed method provides an accurate method for identifying relevant slices of an imaging study.

Algorithms↗
PubMed1998

Extracting findings from narrative reports: software transferability and sources of physician disagreement.

While natural language processing systems are beginning to see clinical use, it remains unclear whether they can be disseminated effectively through the health care community. MedLEE, a general-purpose natural language processor developed for Columbia-Presbyterian Medical Center, was compared to physicians' ability to detect seven clinical conditions in 200 Brigham and Women's Hospital chest radiograph reports. Using the system on the new institution's reports resulted in a small but measurable drop in performance (it was distinguishable from physicians at p = 0.011). By making adjustments to the interpretation of the processor's coded output (without changing the processor itself), local behavior was better accommodated, and performance improved so that it was indistinguishable from the physicians. Pairs of physicians disagreed on at least one condition for 22% of reports; the source of disagreement appeared to be interpretation of findings, gauging likelihood and degree of disease, and coding errors.

Diagnosis, Computer-Assisted↗
PubMed2005

Extending a medical language processing system to the functional status domain.

The World Health Organization's International Classification of Functioning, Disability, and Health (ICF) provides a common framework for describing functional status information (FSI) in health records. Given the expense of manual coding, we are investigating the use of natural language processing (NLP) for automated FSI coding. We used an existing NLP system that was originally designed to encode clinical information. The system's lexicon and coding table were modified and preprocessing and postprocessing programs were created, allowing for automated assignment of selected ICF codes.

Activities of Daily Living↗
PubMed2001

Building a text corpus for representing the variety of medical language.

Medical language processing has focused until recently on a few types of textual documents. However, a much larger variety of document types are used in different settings. It has been showed that Natural Language Processing (NLP) tools can exhibit very different behavior on different types of texts. Without better informed knowledge about the differential performance of NLP tools on a variety of medical text types, it will be difficult to control the extension of their application to different medical documents. We endeavored to provide a basis for such informed assessment: the construction of a large corpus of medical text samples. We propose a framework for designing such a corpus: a set of descriptive dimensions and a standardized encoding of both meta-information (implementing these dimensions) and content. We present a proof of concept demonstration by encoding an initial corpus of text samples according to these principles.

Documentation↗
PubMed2005

Extracting diagnoses from discharge summaries.

We have developed a program for extracting the diagnoses and procedures from the past medical history and discharge diagnoses in the discharge summary of a case and coding these using SNOMED-CT in the UMLS. The program uses a limited amount of natural language processing. Rather, it makes use of the relatively standard structure of the discharge summary, a small dictionary to divide the text into phrases, and the extensive collection of phrases for concepts in the UMLS to do the coding. With this approach the program finds 240 of 250 desired concepts with 19 false positives in 23 discharge summaries.

Diagnosis↗
PubMed2001

A knowledge model for the interpretation and visualization of NLP-parsed discharged summaries.

At our institution, a Natural Language Processing (NLP) tool called MedLEE is used on a daily basis to parse medical texts including complete discharge summaries. MedLEE transforms written text into a generic structured format, which preserves the richness of the underlying natural language expressions by the use of concept modifiers (like change, certainty, degree and status). As a tradeoff, extraction of application-specific medical information is difficult without a clear understanding of how these modifiers combine. We report on a knowledge model for MedLEE modifiers that is helpful for a high level interpretation of NLP data and is used for the generation of two distinct views on NLP-parsed discharge summaries: A physician view offering a condensed overview of the severity of patient problems and a data mining view featuring binary problem states useful for machine learning.

Artificial Intelligence↗
PubMed2001

Mining free-text medical records.

Text mining projects can be characterized along four parameters: 1) the demands of the market in terms of target domain and specificity and depth of queries; 2) the volume and quality of text in the target domain; 3) the text mining process requirements; and 4) the quality assurance process that validates the extracted data. In this paper, we provide lessons learned and results from a large-scale commercial project using Natural Language Processing (NLP) for mining the transcriptions of dictated clinical records in a variety of medical specialties. We conclude that the current state-of-the-art in NLP is suitable for mining information of moderate content depth across a diverse collection of medical settings and specialties.

Acute Disease↗
PubMed1998

Versatility of a multilingual and bi-directional approach for medical language processing.

At the dawn of the 21st century, we are experiencing an exponential growth of online information that is mostly textual, and that benefits from new electronic media, such as the World Wide Web (WWW), to be broadly diffused across borders. However, there is a gap to bridge between holding information and accessing in a relevant way the deep underlying knowledge. Multilingual natural language processing (NLP), once tuned, is certainly the best solution to cope with this era of textual information. This paper focuses on the lesson learned through the joint development of an analyzer and a generator of medical language, within a multilingual context. Concrete examples, derived from the efforts under way in the European GALEN-IN-USE project, illustrate the use of these linguistic tools for the handling of surgical procedures.

Multilingualism↗
PubMed1993

A conceptual model for clinical radiology reports.

The structural and informational content of clinical radiology reports was examined to develop a comprehensive representational schema of the concepts in the domain. The model involves several different conceptual levels, ranging from the high level description of the report to the lower level description of the clinical concepts contained in the reports and the specification of the terms used to express the concepts. The design of an adequate structured representation for the domain has important implications for the design of the electronic patient record, for the unification of different controlled vocabularies by enabling them to be mapped to one common representation, and for the facilitation of natural language processing of clinical reports so that coded data may be obtained.

Humans↗
PubMed2003

The role of domain knowledge in automating medical text report classification.

OBJECTIVE: To analyze the effect of expert knowledge on the inductive learning process in creating classifiers for medical text reports. DESIGN: The authors converted medical text reports to a structured form through natural language processing. They then inductively created classifiers for medical text reports using varying degrees and types of expert knowledge and different inductive learning algorithms. The authors measured performance of the different classifiers as well as the costs to induce classifiers and acquire expert knowledge. MEASUREMENTS: The measurements used were classifier performance, training-set size efficiency, and classifier creation cost. RESULTS: Expert knowledge was shown to be the most significant factor affecting inductive learning performance, outweighing differences in learning algorithms. The use of expert knowledge can affect comparisons between learning algorithms. This expert knowledge may be obtained and represented separately as knowledge about the clinical task or about the data representation used. The benefit of the expert knowledge is more than that of inductive learning itself, with less cost to obtain. CONCLUSION: For medical text report classification, expert knowledge acquisition is more significant to performance and more cost-effective to obtain than knowledge discovery. Building classifiers should therefore focus more on acquiring knowledge from experts than trying to learn this knowledge inductively.

Algorithms↗
PubMedDate not supplied

A schema for representing medical language applied to clinical radiology.

OBJECTIVE: Develop a representational schema for clinical concepts and apply it to the task of encoding radiology reports of the chest. DESIGN: The schema was developed following a manual analysis of sample reports from the domain. The schema has two main components: the Medical Entities Dictionary (MED), which specifies the formal representation of the concepts in the domain and of their structures, and the natural-language processor, which specifies the linguistic expressions of the concepts. The schema was evaluated by applying it to a test set of 7,500 reports. Two-hundred reports from the test set were manually analyzed by a medical expert to determine the accuracy and success rate of the system. RESULTS: 82% of the 7,500 reports that contained relevant clinical information were successfully structured automatically. For the smaller set of 200 reports, 80% were structured successfully with an accuracy rate of 97%. CONCLUSIONS: The schema is a formal representation for clinical concepts in radiology reports, and provides domain coverage that is particularly well-suited for natural-language processing of radiology for use in a decision support system.

Decision Support Systems, Management↗
PubMed2006

Human and automated coding of rehabilitation discharge summaries according to the International Classification of Functioning, Disability, and Health.

OBJECTIVE: The International Classification of Functioning, Disability, and Health (ICF) is designed to provide a common language and framework for describing health and health-related states. The goal of this research was to investigate human and automated coding of functional status information using the ICF framework. DESIGN: The authors extended an existing natural language processing (NLP) system to encode rehabilitation discharge summaries according to the ICF. MEASUREMENTS: The authors conducted a formal evaluation, comparing the coding performed by expert coders, non-expert coders, and the NLP system. RESULTS: Automated coding can be used to assign codes using the ICF, with results similar to those obtained by human coders, at least for the selection of ICF code and assignment of the performance qualifier. Coders achieved high agreement on ICF code assignment. CONCLUSION: This research is a key next step in the development of the ICF as a sensitive and universal classification of functional status information. It is worthwhile to continue to investigate automated ICF coding.

Activities of Daily Living↗