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Finding the findings: identification of findings in medical literature using restricted natural language processing.

The ability to search the biomedical literature based on findings would provide enhanced access to information. We describe a computer program called FINDX which relies on the UMLS Metathesuaurus and restricted natural language processing to identify findings in free text. Such identification can serve as a filtering mechanism while selecting relevant papers. After discussing the salient characteristics of findings on which FINDX depends, we report on the results of an experiment in which we tested the program on a set of MEDLINE abstracts pertaining to the diagnosis of Parkinson Disease.

Humans↗

Comparing natural language processing tools to extract medical problems from narrative text.

To help maintain a complete, accurate and timely Problem List, we are developing a system to automatically retrieve medical problems from free-text documents. This system uses Natural Language Processing to analyze all electronic narrative text documents in a patient's record. Here we evaluate and compare 3 different applications of NLP technology in our system: the first using MMTx (MetaMap Transfer) with a negation detection algorithm (NegEx), the second using an alpha version of a locally developed NLP application called MPLUS2, and the third using keyword searching. They were adapted and trained to extract medical problems from a set of 80 problems of diagnosis type. The version using MMTx and NegEx was improved by adding some disambiguation and modifying the negation detection algorithm, and these modifications significantly improved recall and precision. The different versions of the NLP module were compared, and showed the following recall / precision results: standard MMTx with NegEx version 0.775 / 0.398; improved MMTx with NegEx version 0.892 / 0.753; MPLUS2 version 0.693 / 0.402; and keyword searching version 0.575 / 0.807. Average results for the reviewers were a recall of 0.788 and a precision of 0.912.

Algorithms↗

Unlocking clinical data from narrative reports: a study of natural language processing.

OBJECTIVE: To evaluate the automated detection of clinical conditions described in narrative reports. DESIGN: Automated methods and human experts detected the presence or absence of six clinical conditions in 200 admission chest radiograph reports. STUDY SUBJECTS: A computerized, general-purpose natural language processor; 6 internists; 6 radiologists; 6 lay persons; and 3 other computer methods. MAIN OUTCOME MEASURES: Intersubject disagreement was quantified by "distance" (the average number of clinical conditions per report on which two subjects disagreed) and by sensitivity and specificity with respect to the physicians. RESULTS: Using a majority vote, physicians detected 101 conditions in the 200 reports (0.51 per report); the most common condition was acute bacterial pneumonia (prevalence, 0.14), and the least common was chronic obstructive pulmonary disease (prevalence, 0.03). Pairs of physicians disagreed on the presence of at least 1 condition for an average of 20% of reports. The average intersubject distance among physicians was 0.24 (95% Cl, 0.19 to 0.29) out of a maximum possible distance of 6. No physician had a significantly greater distance than the average. The average distance of the natural language processor from the physicians was 0.26 (Cl, 0.21 to 0.32; not significantly greater than the average among physicians). Lay persons and alternative computer methods had significantly greater distance from the physicians (all > 0.5). The natural language processor had a sensitivity of 81% (Cl, 73% to 87%) and a specificity of 98% (Cl, 97% to 99%); physicians had an average sensitivity of 85% and an average specificity of 98%. CONCLUSIONS: Physicians disagreed on the interpretation of narrative reports, but this was not caused by outlier physicians or a consistent difference in the way internists and radiologists read reports. The natural language processor was not distinguishable from the physicians and was superior to all other comparison subjects. Although the domain of this study was restricted (six clinical conditions in chest radiographs), natural language processing seems to have the potential to extract clinical information from narrative reports in a manner that will support automated decision-support and clinical research.

Humans↗

Natural language processing challenges in HIV/AIDS clinic notes.

In recent years, significant progress has been achieved toward increased structured data entry using standardized health care terminologies. Concurrently, the value of narrative as the clinician's rich description of the encounter and source of vital information has been reaffirmed. Natural language processing (NLP) offers a strategy for integrating these approaches to provide structured reports for further computer processing. As part of a larger project aimed at using narrative data to enrich the online medical record, we analyzed a small sample of documents in a corpus of progress notes to identify potential challenges associated with using NLP for HIV/AIDS clinic notes. We provide illustrative examples of five types of challenges.

Acquired Immunodeficiency Syndrome↗

CliniViewer: a tool for viewing electronic medical records based on natural language processing and XML.

With the evolving use of computers in healthcare, the electronic medical record (EMR) is becoming more and more popular. A tool is needed that would enable physicians to accurately and efficiently access clinical information in multiple medical records associated with a particular patient. Both natural language processing (NLP) and the eXtensible Markup Language (XML) have been used in the clinical domain for capturing, representing, and utilizing clinical information and both have shown great potential. In this paper, we demonstrate another use of XML and NLP through CliniViewer, a tool that organizes and presents the clinical information in multiple records. We also describe the flexibility and capability provided when combining XML and NLP to summarize, navigate, and conceptualize structured information. The tool has been fully implemented and tested using patients with multiple discharge summaries.

Humans↗

Emergency department applications of digital dictation and natural language processing.

The emergency physician has a number of challenges that must be addressed in providing high quality emergency health care delivery. One of these is the need to develop accurate documentation of the patient's health status and the patient's response to treatment in the ED. Digital dictation when combined with natural language processing is an evolving technology that is very user-friendly, fast, flexible, and cost-effective. Together, they can potentially improve risk management, reimbursement, and ultimately patient care. All the specialties of medicine are facing tremendous challenges of quality in the coming decade. In the past, an ED would wait for an incident or maloccurrence to happen. The director would analyze the cause, educate the staff, and write a new policy. Quality assurance meant searching for deviance and promoting compliance. The approach of "quality as a process of continuous improvement" suggests that the old ways don't work. We must have access to the detailed information about our clinical practice in order to assess outcomes, evaluate procedures, and undergo any meaningful change. It is said, "We cannot change what we cannot measure". Certainly tens of thousands of handwritten illegible ED reports represent an effective barrier to any substantive insight into our clinical process, and that constitutes an absolute barrier to any effective change. Digital dictation represents one gateway into the electronic record. Combined with NLP and appropriate information management, this technology holds tremendous potential for growth and provides for quality in the coming decade.

Efficiency↗

Natural language processing and its future in medicine.

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be structured or coded. Many online patient reports are not coded, but are recorded in natural-language text that cannot be reliably accessed. Natural language processing (NLP) can solve this problem by extracting and structuring text-based clinical information, making clinical data available for use. NLP systems are quite difficult to develop, as they require substantial amounts of knowledge, but progress has definitely been made. Some NLP systems have been developed and tested and have demonstrated promising performance in practical clinical applications; some of these systems have already been deployed. The authors provide background information about NLP, briefly describe some of the systems that have been recently developed, and discuss the future of NLP in medicine.

Forecasting↗

Facilitating cancer research using natural language processing of pathology reports.

Many ongoing clinical research projects, such as projects involving studies associated with cancer, involve manual capture of information in surgical pathology reports so that the information can be used to determine the eligibility of recruited patients for the study and to provide other information, such as cancer prognosis. Natural language processing (NLP) systems offer an alternative to automated coding, but pathology reports have certain features that are difficult for NLP systems. This paper describes how a preprocessor was integrated with an existing NLP system (MedLEE) in order to reduce modification to the NLP system and to improve performance. The work was done in conjunction with an ongoing clinical research project that assesses disparities and risks of developing breast cancer for minority women. An evaluation of the system was performed using manually coded data from the research project's database as a gold standard. The evaluation outcome showed that the extended NLP system had a sensitivity of 90.6% and a precision of 91.6%. Results indicated that this system performed satisfactorily for capturing information for the cancer research project.

Biomedical Research↗

Natural language processing and visualization in the molecular imaging domain.

Molecular imaging is at the crossroads of genomic sciences and medical imaging. Information within the molecular imaging literature could be used to link to genomic and imaging information resources and to organize and index images in a way that is potentially useful to researchers. A number of natural language processing (NLP) systems are available to automatically extract information from genomic literature. One existing NLP system, known as BioMedLEE, automatically extracts biological information consisting of biomolecular substances and phenotypic data. This paper focuses on the adaptation, evaluation, and application of BioMedLEE to the molecular imaging domain. In order to adapt BioMedLEE for this domain, we extend an existing molecular imaging terminology and incorporate it into BioMedLEE. BioMedLEE's performance is assessed with a formal evaluation study. The system's performance, measured as recall and precision, is 0.74 (95% CI: [.70-.76]) and 0.70 (95% CI [.63-.76]), respectively. We adapt a JAVA viewer known as PGviewer for the simultaneous visualization of images with NLP extracted information.

Animals↗

Terminology model discovery using natural language processing and visualization techniques.

Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.

Automation↗

Natural language processing in the electronic medical record: assessing clinician adherence to tobacco treatment guidelines.

BACKGROUND: Comprehensively assessing care quality with electronic medical records (EMRs) is not currently possible because much data reside in clinicians' free-text notes. METHODS: We evaluated the accuracy of MediClass, an automated, rule-based classifier of the EMR that incorporates natural language processing, in assessing whether clinicians: (1) asked if the patient smoked; (2) advised them to stop; (3) assessed their readiness to quit; (4) assisted them in quitting by providing information or medications; and (5) arranged for appropriate follow-up care (i.e., the 5A's of smoking-cessation care). DESIGN: We analyzed 125 medical records of known smokers at each of four HMOs in 2003 and 2004. One trained abstractor at each HMO manually coded all 500 records according to whether or not each of the 5A's of smoking cessation care was addressed during routine outpatient visits. MEASUREMENTS: For each patient's record, we compared the presence or absence of each of the 5A's as assessed by each human coder and by MediClass. We measured the chance-corrected agreement between the human raters and MediClass using the kappa statistic. RESULTS: For "ask" and "assist," agreement among human coders was indistinguishable from agreement between humans and MediClass (p>0.05). For "assess" and "advise," the human coders agreed more with each other than they did with MediClass (p<0.01); however, MediClass performance was sufficient to assess quality in these areas. The frequency of "arrange" was too low to be analyzed. CONCLUSIONS: MediClass performance appears adequate to replace human coders of the 5A's of smoking-cessation care, allowing for automated assessment of clinician adherence to one of the most important, evidence-based guidelines in preventive health care.

Counseling↗

Natural language processing in the molecular imaging domain.

Molecular imaging represents the intersection between imaging and genomic sciences. There has been a surge in research literature and information in both sciences. Information contained within molecular imaging literature could be used to 1) link to genomic and imaging information resources and 2) to organize and index images. This research focuses on the adaptation, evaluation, and application of BioMedLEE, a natural language processing system (NLP), in the automated extraction of information from molecular imaging abstracts.

Cell Line↗

Empirical, automated vocabulary discovery using large text corpora and advanced natural language processing tools.

A major impediment to the full benefit of electronic medical records is the lack of a comprehensive clinical vocabulary. Most existing vocabularies do not allow the full expressiveness of clinical diagnoses and findings that are often qualified by modifiers relating to severity, acuity, and temporal factors. One reason for the lack of expressivity is the inability of traditional manual construction techniques to identify the diversity of language used by clinicians. This study used advanced natural language processing tools to identify terminology in a clinical findings domain, compare its coverage with the UMLS Metathesaurus, and quantify the effort required to discover the additional terminology. It was found that substantial amounts of phrases and individual modifiers were not present in the UMLS Metathesaurus and that modest effort in human time and computer processing were needed to obtain the larger quantity of terms.

Medical Records↗

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↗

Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction.

MOTIVATION: Wnt signaling is a very active area of research with highly relevant publications appearing at a rate of more than one per day. Building and maintaining databases describing signal transduction networks is a time-consuming and demanding task that requires careful literature analysis and extensive domain-specific knowledge. For instance, more than 50 factors involved in Wnt signal transduction have been identified as of late 2003. In this work we describe a natural language processing (NLP) system that is able to identify references to biological interaction networks in free text and automatically assembles a protein association and interaction map. RESULTS: A 'gold standard' set of names and assertions was derived by manual scanning of the Wnt genes website (http://www.stanford.edu/~rnusse/wntwindow.html) including 53 interactions involved in Wnt signaling. This system was used to analyze a corpus of peer-reviewed articles related to Wnt signaling including 3369 Pubmed and 1230 full text papers. Names for key Wnt-pathway associated proteins and biological entities are identified using a chi-squared analysis of noun phrases over-represented in the Wnt literature as compared to the general signal transduction literature. Interestingly, we identified several instances where generic terms were used on the website when more specific terms occur in the literature, and one typographic error on the Wnt canonical pathway. Using the named entity list and performing an exhaustive assertion extraction of the corpus, 34 of the 53 interactions in the 'gold standard' Wnt signaling set were successfully identified (64% recall). In addition, the automated extraction found several interactions involving key Wnt-related molecules which were missing or different from those in the canonical diagram, and these were confirmed by manual review of the text. These results suggest that a combination of NLP techniques for information extraction can form a useful first-pass tool for assisting human annotation and maintenance of signal pathway databases. AVAILABILITY: The pipeline software components are freely available on request to the authors. CONTACT: dstates@umich.edu SUPPLEMENTARY INFORMATION: http://stateslab.bioinformatics.med.umich.edu/software.html.

Animals↗

Automation of a problem list using natural language processing.

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information.

Adult↗

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.

Abstracting and Indexing↗

Natural language processing, lexicon and semantics.

While the design of a fully general procedure for semantico-pragmatic interpretation of natural language texts does not seem to be feasible with current scientific knowledge and technology, the more practical microworld based approaches lack generality and portability. A compromise between generality and practicality might lie in the use of an intermediate level of presentation ("pseudo-semantics"), which can be derived from syntactic representations and lexical information by means of a general procedure. Domain-dependent rules for semantico-pragmatic interpretation can then be applied to these representations, insulating syntactic processing from details of the application domain.

Humans↗