Biomedical subjects

Christopher G Chute

Publications and source records attributed to Christopher G Chute.

At least 19 recordsLinked to original sources

PubMed2006

Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniques.

OBJECTIVE: Human classification of diagnoses is a labor intensive process that consumes significant resources. Most medical practices use specially trained medical coders to categorize diagnoses for billing and research purposes. METHODS: We have developed an automated coding system designed to assign codes to clinical diagnoses. The system uses the notion of certainty to recommend subsequent processing. Codes with the highest certainty are generated by matching the diagnostic text to frequent examples in a database of 22 million manually coded entries. These code assignments are not subject to subsequent manual review. Codes at a lower certainty level are assigned by matching to previously infrequently coded examples. The least certain codes are generated by a naïve Bayes classifier. The latter two types of codes are subsequently manually reviewed. MEASUREMENTS: Standard information retrieval accuracy measurements of precision, recall and f-measure were used. Micro- and macro-averaged results were computed. RESULTS At least 48% of all EMR problem list entries at the Mayo Clinic can be automatically classified with macro-averaged 98.0% precision, 98.3% recall and an f-score of 98.2%. An additional 34% of the entries are classified with macro-averaged 90.1% precision, 95.6% recall and 93.1% f-score. The remaining 18% of the entries are classified with macro-averaged 58.5%. CONCLUSION: Over two thirds of all diagnoses are coded automatically with high accuracy. The system has been successfully implemented at the Mayo Clinic, which resulted in a reduction of staff engaged in manual coding from thirty-four coders to seven verifiers.

Abstracting and Indexing↗

PubMed2006

Measures of semantic similarity and relatedness in the biomedical domain.

Measures of semantic similarity between concepts are widely used in Natural Language Processing. In this article, we show how six existing domain-independent measures can be adapted to the biomedical domain. These measures were originally based on WordNet, an English lexical database of concepts and relations. In this research, we adapt these measures to the SNOMED-CT ontology of medical concepts. The measures include two path-based measures, and three measures that augment path-based measures with information content statistics from corpora. We also derive a context vector measure based on medical corpora that can be used as a measure of semantic relatedness. These six measures are evaluated against a newly created test bed of 30 medical concept pairs scored by three physicians and nine medical coders. We find that the medical coders and physicians differ in their ratings, and that the context vector measure correlates most closely with the physicians, while the path-based measures and one of the information content measures correlates most closely with the medical coders. We conclude that there is a role both for more flexible measures of relatedness based on information derived from corpora, as well as for measures that rely on existing ontological structures.

Database Management Systems↗

PubMed2006

National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge.

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.

Biomedical Research↗

PubMed2006

Content coverage of SNOMED-CT toward the ICU nursing flowsheets and the acuity indicators.

Nursing flowsheets contain discrete and detailed patient data that can be reused in various clinical decisions including patient acuity estimation. To be reused, patient data should be represented consistently and unambiguously with standardized terminologies. This study investigated the domain completeness of SNOMED-CT toward the ICU nursing flowsheets and the acuity indicators used at Mayo Clinic. We first paraphrased the terms and the semantic relations appeared in the source materials into short clinical statement. Then, the entire prepared statement was mapped to SNOMED-CT and validated by expert reviewers. SNOMED-CT provided complete matches and partial matches to 78.13% and 14.1% of the flowsheets statements respectively. It provided complete matches and partial matches to 60.8% and 39.2% of the acuity statements respectively. The average observed agreement among the reviewers ran around 85%. Lack of appropriate terms was the major cause of the incomplete matches with the flowshee statements. Majority of the incomplete matches with the statements from the acuity indicators were caused by limitations in the concept models.

Documentation↗

PubMed2005

Developing a corpus of clinical notes manually annotated for part-of-speech.

PURPOSE: This paper presents a project whose main goal is to construct a corpus of clinical text manually annotated for part-of-speech (POS) information. We describe and discuss the process of training three domain experts to perform linguistic annotation. METHODS: Three domain experts were trained to perform manual annotation of a corpus of clinical notes. A part of this corpus was combined with the Penn Treebank corpus of general purpose English text and another part was set aside for testing. The corpora were then used for training and testing statistical part-of-speech taggers. We list some of the challenges as well as encouraging results pertaining to inter-rater agreement and consistency of annotation. RESULTS: We used the Trigrams'n'Tags (TnT) [T. Brants, TnT-a statistical part-of-speech tagger, In: Proceedings of NAACL/ANLP-2000 Symposium, 2000] tagger trained on general English data to achieve 89.79% correctness. The same tagger trained on a portion of the medical data annotated for this project improved the performance to 94.69%. Furthermore, we find that discriminating between different types of discourse represented by different sections of clinical text may be very beneficial to improve correctness of POS tagging. CONCLUSION: Our preliminary experimental results indicate the necessity for adapting state-of-the-art POS taggers to the sublanguage domain of clinical text.

Abstracting and Indexing↗

PubMed2005

Domain-specific language models and lexicons for tagging.

Accurate and reliable part-of-speech tagging is useful for many Natural Language Processing (NLP) tasks that form the foundation of NLP-based approaches to information retrieval and data mining. In general, large annotated corpora are necessary to achieve desired part-of-speech tagger accuracy. We show that a large annotated general-English corpus is not sufficient for building a part-of-speech tagger model adequate for tagging documents from the medical domain. However, adding a quite small domain-specific corpus to a large general-English one boosts performance to over 92% accuracy from 87% in our studies. We also suggest a number of characteristics to quantify the similarities between a training corpus and the test data. These results give guidance for creating an appropriate corpus for building a part-of-speech tagger model that gives satisfactory accuracy results on a new domain at a relatively small cost.

Humans↗

PubMed2005

Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier.

This paper addresses a very specific problem of identifying patients diagnosed with a specific condition for potential recruitment in a clinical trial or an epidemiological study. We present a simple machine learning method for identifying patients diagnosed with congestive heart failure and other related conditions by automatically classifying clinical notes dictated at Mayo Clinic. This method relies on an automatic classifier trained on comparable amounts of positive and negative samples of clinical notes previously categorized by human experts. The documents are represented as feature vectors, where features are a mix of demographic information as well as single words and concept mappings to MeSH and HICDA classification systems. We compare two simple and efficient classification algorithms (Naïve Bayes and Perceptron) and a baseline term spotting method with respect to their accuracy and recall on positive samples. Depending on the test set, we find that Naïve Bayes yields better recall on positive samples (95 vs. 86%) but worse accuracy than Perceptron (57 vs. 65%). Both algorithms perform better than the baseline with recall on positive samples of 71% and accuracy of 54%.

Artificial Intelligence↗

PubMed2005

Abbreviation and acronym disambiguation in clinical discourse.

Use of abbreviations and acronyms is pervasive in clinical reports despite many efforts to limit the use of ambiguous and unsanctioned abbreviations and acronyms. Due to the fact that many abbreviations and acronyms are ambiguous with respect to their sense, complete and accurate text analysis is impossible without identification of the sense that was intended for a given abbreviation or acronym. We present the results of an experiment where we used the contexts harvested from the Internet through Google API to collect contextual data for a set of 8 acronyms found in clinical notes at the Mayo Clinic. We then used the contexts to disambiguate the sense of abbreviations in a manually annotated corpus.

Abbreviations as Topic↗

PubMed2005

Representing lexical components of medical terminologies in OWL.

Medical Terminologies play a vital role in clinical data capture, reporting, information integration, indexing and retrieval. The Web Ontology language (OWL) provides an opportunity for the medical community to leverage the capabilities of OWL semantics and tools to build formal, sound and consistent medical terminologies, and to provide a standard web accessible medium for inter-operability,access and reuse. One of the tasks facing the medical community today is to represent the extensive terminology content that already exists into this new medium. This paper addresses one aspect of this challenge - how to incorporate multilingual, structured lexical information such as definitions, synonyms, usage notes, etc. into the OWL ontology model in a standardized, consistent and useful fashion.

Internet↗

PubMed2004

Modeling guidelines for integration into clinical workflow.

The success of clinical decision-support systems requires that they are seamlessly integrated into clinical workflow. In the SAGE project, which aims to create the technological infra-structure for implementing computable clinical practice guide-lines in enterprise settings, we created a deployment-driven methodology for developing guideline knowledge bases. It involves (1) identification of usage scenarios of guideline-based care in clinical workflow, (2) distillation and disambiguation of guideline knowledge relevant to these usage scenarios, (3) formalization of data elements and vocabulary used in the guideline, and (4) encoding of usage scenarios and guideline knowledge using an executable guideline model. This methodology makes explicit the points in the care process where guideline-based decision aids are appropriate and the roles of clinicians for whom the guideline-based assistance is intended. We have evaluated the methodology by simulating the deployment of an immunization guideline in a real clinical information system and by reconstructing the workflow context of a deployed decision-support system for guideline-based care. We discuss the implication of deployment-driven guideline encoding for sharability of executable guidelines.

Decision Making, Computer-Assisted↗

PubMed2004

Using compound codes for automatic classification of clinical diagnoses.

Classification of diagnoses (a.k.a. coding) is the central part of current concept based medical IR systems. Some classification systems contain over 30,000 distinct codes which makes classifying clinical documents a time consuming labor intensive and error prone process. This paper presents a simple methodology for cleaning up and reusing existing manually coded diagnostic statements mainly extracted from clinical notes to build predictive models using a sparse-feature implementation of a Naïve Bayes classifier. One of the problems addressed is that diagnostic statements often contain several diagnoses and are assigned several codes resulting in a multi-class classification problem. We investigate one possible way of addressing this problem by introducing compound (multiple code) categories. We present experimental results of classifying >16,000 randomly selected diagnostic strings into 19 top level categories. A small improvement (3%) with using compound categories over simple categories indicates that using multiple code categories is a promising solution, although clearly in need of further research and refinement.

Abstracting and Indexing↗

PubMed2004

A corpus driven approach applying the "frame semantic" method for modeling functional status terminology.

In an effort to unearth semantic models that could prove fruitful to functional-status terminology development we applied the "frame semantic" method, derived from the linguistic theory of thematic roles currently exemplified in the Berkeley "FrameNet" Project. Full descriptive sentences with functional-status conceptual meaning were derived from structured content within a corpus of questionnaire assessment instruments commonly used in clinical practice for functional-status assessment. Syntactic components in those sentences were delineated through manual annotation and mark-up. The annotated syntactic constituents were tagged as frame elements according to their semantic role within the context of the derived functional-status expression. Through this process generalizable "semantic frames" were elaborated with recurring "frame elements". The "frame semantic" method as an approach to rendering semantic models for functional-status terminology development and its use as a basis for machine recognition of functional status data in clinical narratives are discussed.

Information Science↗

PubMed2004

Terminology access methods leveraging LDAP resources.

Health terminologies have become more complex, more massive, and more ubiquitous in the modern healthcare enterprise. Present technology makes the use of these terminologies by humans, unaided by machines, virtually impossible. However, system and message interoperability can be severely compromised if the software services deploying terminology content and interfaces are themselves non-standard. We review some characteristics for good terminology services and introduce an open-source, robust, widely deployed and widely available software resource to underpin terminology service implementations. The Lightweight Directory Access Protocol, or LDAP, is compared with alternative technologies. We describe a reference implementation of terminology services built around the HL7 Common Terminology Services using LDAP methods. We propose that LDAP is well suited as a common platform for federated, synchronized, and algorithmically distributed terminology content from multiple sources.

Algorithms↗

PubMed2003

From clinical records to regulatory reporting: formal terminologies as foundation.

Achieving goals of healthy people and populations is dependent on available and relevant data for health care decisions. New technologies enable reuse of data for decisions, however it is clear that uniform data standards and in particular standards around terminological data will be required to achieve reuse. Terminological data related to functioning and disability presents unique challenges because of the conceptual ambiguity within the field. The International Classification of Functioning, Disability, and Health (ICF) provides a clarifying conceptual foundation for functioning and disability data, but is not structured as a formal terminology. The need for a concerted and coordinated effort is emphasized.

Activities of Daily Living↗

PubMed2003

A data-driven approach for extracting "the most specific term" for ontology development.

We present a data-driven approach to extract the "most specific" terms relevant to an ontology of functioning, disability and health. The algorithm is a combination of statistical and linguistic approaches. The statistical filter is based on the frequency of the content words in a given text string; the linguistic heuristic is an extension of existing algorithms but goes beyond noun phrases and is formulated as a "complete syntactic node". Thus, it can be applied to any syntactic node of interest in the particular domain. Two test sets were marked by three experts. Test set 1 is a well-constructed text from pain abstracts; test set 2 is actual medical reports. Results are reported as recall, precision, F-score and rate of valid terms in false positives. A limitation of the current research is the relatively small test set.

Algorithms↗

PubMed2003

The Open Terminology Services (OTS) project.

The Open Terminology Services (OTS) project provides a common, well-specified mechanism to access terminological content in a vendor and platform neutral fashion. The project includes a freely available API specification and an open source reference implementation. The API specification derives from the OMG Lexicon Query Services interface specification as a foundation and defines mechanisms for browsing, querying and import terminological content. The Java-based reference implementation uses the Lightweight Directory Access Protocol (LDAP) for a back end, and provides a mechanism to query and distribute heterogeneous terminological content using a common format. The project includes the CTS (Central Terminology Services) subset under HL7.

Information Systems↗

PubMed2002

Public health, data standards, and vocabulary: crucial infrastructure for reliable public health surveillance.

The present, rapid adoption of electronic records in clinical care is likely to shift public health surveillance from passive, human-mediated abstraction to active, computer-generated reports. However, the accuracy and efficiency of this process depends upon the adoption of consistent information standards from beside to population and the relevance of these data to public health. This article outlines the current status of data standards of relevance to public health and expands upon the ideal goal state in which health information would be collected once and then reused for multiple health-related purposes, including public health surveillance.

Computer Communication Networks↗

PubMed2002

An evaluation of unmediated versus mediated retrieval services.

To understand if unmediated services could serve the data retrieval needs for the Mayo research investigator, a study was conducted to determine researcher interest, ability, and outcome of using a clinical data retrieval system. The results indicate about 25% of the research investigators would use a self-service retrieval tool. However, there is clear evidence a majority of the research investigators are satisfied with and prefer the mediated service because of convenience, retrieval specialist knowledge, and lack of time to perform the search themselves. Approximately 61% of the non-participants indicated they would be willing to pay a fee for continued use of the mediated service. This study confirms the interest in self-service retrieval tools, but the actual interest is lower than anticipated. The recommendation is to continue the use of mediated services and to offer self-service methods as needed, allowing the most options to the research investigator.

Biomedical Research↗