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The role of complementary bipartite visual analytical representations in the analysis of SNPs: a case study in ancestral informative markers

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E5-E12页

作者： Bhavnani, Suresh K. Bellala, Gowtham Victor, Sundar Bassler, Kevin E. Visweswaran, Shyam Univ Texas Med Branch Inst Translat Sci Galveston TX 77555 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Houston Dept Phys Houston TX USA Univ Houston Texas Ctr Superconduct Houston TX USA Univ Pittsburgh Dept Biomed Informat Pittsburgh PA USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA USA

Objective Several studies have shown how sets of single-nucleotide polymorphisms (SNPs) can help to classify subjects on the basis of their continental origins, with applications to case-control studies and population genetics. However, most of these studies use dimensionality-reduction methods, such as principal component analysis, or clustering methods that result in unipartite (either subjects or SNPs) representations of the data. Such analyses conceal important bipartite relationships, such as how subject and SNP clusters relate to each other, and the genotypes that determine their cluster memberships. methods To overcome the limitations of current methods of analyzing SNP data, the authors used three bipartite analytical representations (bipartite network, heat map with dendrograms, and Circos ideogram) that enable the simultaneous visualization and analysis of subjects, SNPs, and subject attributes. Results The results demonstrate (1) novel insights into SNP data that are difficult to derive from purely unipartite views of the data, (2) the strengths and limitations of each method, revealing the role that each play in revealing novel insights, and (3) implications for how the methods can be used for the analysis of SNPs in genomic studies associated with disease. Conclusion The results suggest that bipartite representations can reveal new patterns in SNP data compared with existing unipartite representations. However, the novel insights require multiple representations to discover, verify, and comprehend the complex relationships. The results therefore motivate the need for a complementary visual analytical framework that guides the use of multiple bipartite representations to analyze complex relationships in SNP data.

关键词： Visualization of data and knowledge human-computer interaction and human-centered computing networks systems to support and improve diagnostic accuracy linking the genotype and phenotype discovery text and data mining methods uncertain reasoning and decision theory

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Automatic discourse connective detection in biomedical text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 800-808页

作者： Ramesh, Balaji Polepalli Prasad, Rashmi Miller, Tim Harrington, Brian Yu, Hong Univ Wisconsin Dept Elect Engn & Comp Sci Milwaukee WI 53211 USA Univ Wisconsin Dept Hlth Informat & Adm Milwaukee WI 53211 USA

Objective Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text. Materials and methods Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (similar to 112 000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (similar to 1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores. Results and Conclusion Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance: 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at: http://***.

关键词： Analysis automated learning controlled terminologies and vocabularies discovery display image representation knowledge acquisition and knowledge management knowledge bases knowledge representations machine learning natural language processing NLP ontologies processing text and data mining methods

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MCORES: a system for noun phrase coreference resolution for clinical records

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 906-912页

作者： Bodnari, Andreea Szolovits, Peter Uzuner, Oezlem MIT CSAIL Stata Ctr Cambridge MA 02139 USA SUNY Albany Dept Informat Studies Albany NY 12222 USA

Objective Narratives of electronic medical records contain information that can be useful for clinical practice and multi-purpose research. This information needs to be put into a structured form before it can be used by automated systems. Coreference resolution is a step in the transformation of narratives into a structured form. methods This study presents a medical coreference resolution system (MCORES) for noun phrases in four frequently used clinical semantic categories: persons, problems, treatments, and tests. MCORES treats coreference resolution as a binary classification task. Given a pair of concepts from a semantic category, it determines coreferent pairs and clusters them into chains. MCORES uses an enhanced set of lexical, syntactic, and semantic features. Some MCORES features measure the distance between various representations of the concepts in a pair and can be asymmetric. Results and Conclusion MCORES was compared with an in-house baseline that uses only single-perspective 'token overlap' and 'number agreement' features. MCORES was shown to outperform the baseline;its enhanced features contribute significantly to performance. In addition to the baseline, MCORES was compared against two available third-party, open-domain systems, RECONCILEACL09 and the Beautiful Anaphora Resolution Toolkit (BART). MCORES was shown to outperform both of these systems on clinical records.

关键词： Artificial intelligence clinical outcomes confidentiality decision support discovery information storage and retrieval (text and images) machine learning natural language processing other methods of information extraction text and data mining methods

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The EpiCanvas infectious disease weather map: an interactive visual exploration of temporal and spatial correlations

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第6期19卷 954-959页

作者： Gesteland, Per Hans Livnat, Yarden Galli, Nathan Samore, Matthew H. Gundlapalli, Adi V. Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT 84113 USA Intermt Healthcare Inst Hlth Care Delivery Res Salt Lake City UT USA Univ Utah Sci & Comp Imaging Inst Salt Lake City UT 84113 USA Univ Utah Sch Med Dept Internal Med Salt Lake City UT USA VA Salt Lake City Hlth Care Syst Salt Lake City UT USA Univ Utah Sch Med Dept Pediat Salt Lake City UT USA

Advances in surveillance science have supported public health agencies in tracking and responding to disease outbreaks. Increasingly, epidemiologists have been tasked with interpreting multiple streams of heterogeneous data arising from varied surveillance systems. As a result public health personnel have experienced an overload of plots and charts as information visualization techniques have not kept pace with the rapid expansion in data availability. This study sought to advance the science of public health surveillance data visualization by conceptualizing a visual paradigm that provides an 'epidemiological canvas' for detection, monitoring, exploration and discovery of regional infectious disease activity and developing a software prototype of an 'infectious disease weather map'. Design objectives were elucidated and the conceptual model was developed using cognitive task analysis with public health epidemiologists. The software prototype was pilot tested using retrospective data from a large, regional pediatric hospital, and gastrointestinal and respiratory disease outbreaks were re-created as a proof of concept.

关键词： Biosurveillance correlation of data data display detecting disease outbreaks and biological threats developing/using clinical decision support (other than diagnostic) and guideline systems discovery human–computer interaction and human-centered computing improving healthcare workflow and process efficiency infectious disease outbreaks measuring/improving outcomes in specific conditions and patient subgroups methods for integration of information from disparate sources modeling physiological and disease processes other methods of information extraction public health surveillance simulation of complex systems (at all levels: molecules to work groups to organizations) statistical data analysis systems to support and improve diagnostic accuracy text and data mining methods visualization of data and knowledge

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A translational engine at the national scale: informatics for integrating biology and the bedside

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第2期19卷 181-185页

作者： Kohane, Isaac S. Churchill, Susanne E. Murphy, Shawn N. Harvard Univ Sch Med Childrens Hosp Informat Program Boston MA 02115 USA Harvard Univ Sch Med Ctr Biomed Informat Boston MA 02115 USA Partners Healthcare Syst Res Comp Informat Technol Charlestown MA USA

Informatics for integrating biology and the bedside (i2b2) seeks to provide the instrumentation for using the informational by-products of health care and the biological materials accumulated through the delivery of health care to conduct discovery research and to study the healthcare system in vivo. This complements existing efforts such as prospective cohort studies or trials outside the delivery of routine health care. i2b2 has been used to generate genome-wide studies at less than one tenth the cost and one tenth the time of conventionally performed studies as well as to identify important risk from commonly used medications. i2b2 has been adopted by over 60 academic health centers internationally.

关键词： Analysis automated learning bioinformatics clinical informatics data models discovery display genomics image representation information storage and retrieval (text and images) knowledge representations linking the genotype and phenotype medical records natural-language processing processing text and data mining methods visualization of data and knowledge

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Towards spoken clinical-question answering: evaluating and adapting automatic speech-recognition systems for spoken clinical questions

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 625-630页

作者： Liu, Feifan Tur, Gokhan Hakkani-Tuer, Dilek Yu, Hong Univ Wisconsin Dept Hlth Sci Milwaukee WI 53201 USA SRI Int Speech Technol & Res Lab Informat & Comp Sci Div Menlo Pk CA 94025 USA Int Comp Sci Inst Speech Grp Berkeley CA 94704 USA Univ Wisconsin Dept Comp Sci Milwaukee WI 53201 USA

Objective To evaluate existing automatic speech-recognition (ASR) systems to measure their performance in interpreting spoken clinical questions and to adapt one ASR system to improve its performance on this task. Design and measurements The authors evaluated two well-known ASR systems on spoken clinical questions: Nuance Dragon (both generic and medical versions: Nuance Gen and Nuance Med) and the SRI Decipher (the generic version SRI Gen). The authors also explored language model adaptation using more than 4000 clinical questions to improve the SRI system's performance, and profile training to improve the performance of the Nuance Med system. The authors reported the results with the NIST standard word error rate (WER) and further analyzed error patterns at the semantic level. Results Nuance Gen and Med systems resulted in a WER of 68.1% and 67.4% respectively. The SRI Gen system performed better, attaining a WER of 41.5%. After domain adaptation with a language model, the performance of the SRI system improved 36% to a final WER of 26.7%. Conclusion Without modification, two well-known ASR systems do not perform well in interpreting spoken clinical questions. With a simple domain adaptation, one of the ASH systems improved significantly on the clinical question task, indicating the importance of developing domain/genre-specific ASR systems.

关键词： Automated learning discovery text and data mining methods other methods of information extraction natural-language processing knowledge bases knowledge representations knowledge acquisition and knowledge management discovery and text and data mining methods natural-language processing automated learning processing and display analysis image representation controlled terminologies and vocabularies ontologies machine learning spoken clinical question answering language model adaptation automatic speech recognition

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A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 601-606页

作者： Jiang, Min Chen, Yukun Liu, Mei Rosenbloom, S. Trent Mani, Subramani Denny, Joshua C. Xu, Hua Vanderbilt Univ Sch Med Dept Biomed Informat Nashville TN 37232 USA Vanderbilt Univ Sch Med Dept Med Nashville TN 37232 USA

Objective The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities including medical problems, tests, and treatments, as well as their asserted status from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge. Design The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes. Measurements Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge. Results and discussion Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically

关键词： Natural language processing machine learning named entity recognition clinical text processing NLP ethical study methods text generation from discrete data Classical experimental and quasi-experimental study methods (lab and field) Developing and refining EHR data standards (including image standards) Controlled terminologies and vocabularies Human–computer interaction and human-centered computing Surveys and needs analysis Qualitative/ethnographic field study policy Legal historical Automated learning Translational research—application of biological knowledge to clinical care Uncertain reasoning and decision theory Linking the genotype and phenotype languages computational methods Advanced algorithms discovery text and data mining methods Knowledge representations Knowledge bases

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Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第3期18卷 232-242页

作者： Wright, Adam Sittig, Dean F. Ash, Joan S. Feblowitz, Joshua Meltzer, Seth McMullen, Carmit Guappone, Ken Carpenter, Jim Richardson, Joshua Simonaitis, Linas Evans, R. Scott Nichol, W. Paul Middleton, Blackford Brigham & Womens Hosp Div Gen Med & Primary Care Boston MA 02115 USA Partners HealthCare Boston MA USA Harvard Univ Sch Med Boston MA USA Univ Texas Houston UT Mem Hermann Ctr Healthcare Qual & Safety Sch Biomed Informat Houston TX USA Oregon Hlth & Sci Univ Dept Med Informat & Clin Epidemiol Portland OR 97201 USA Kaiser Permanente Ctr Hlth Res Portland OR USA Providence Portland Med Ctr Reg Informat Serv Portland OR USA Weill Cornell Med Coll Ctr Healthcare Informat & Policy New York NY USA Indiana Univ Sch Med Regenstrief Inst Inc Indianapolis IN USA Intermt Hlth Care Dept Med Informat Salt Lake City UT USA Vet Hlth Adm Patient Care Serv Seattle WA USA

Background Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems. Objective To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs. Study design and methods We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4). Results Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common. Conclusion We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.

关键词： Developing/using computerized provider order entry knowledge representations classical experimental and quasi-experimental study methods (lab and field) designing usable (responsive) resources and systems statistical analysis of large datasets discovery text and data mining methods automated learning human-computer interaction and human-centered computing qualitative/ethnographic field study clinical decision support manning maddux decision support biomedical informatics developing and refining EHR data standards (including image standards) controlled terminologies and vocabularies measuring/improving patient safety and reducing medical errors machine learning electronic health records meaningful use

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Recommending MeSH terms for annotating biomedical articles

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 660-667页

作者： Huang, Minlie Neveol, Aurelie Lu, Zhiyong NIH NCBI US Natl Lib Med Bethesda MD 20894 USA Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

Background Due to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired. Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval. methods Unlike previous methods for automatic MeSH term assignment, we reformulate the indexing task as a ranking problem such that relevant MeSH headings are ranked higher than those irrelevant ones. Specifically, for each document we retrieve 20 neighbor documents, obtain a list of MeSH main headings from neighbors, and rank the MeSH main headings using List Net a learning-to-rank algorithm. We trained our algorithm on 200 documents and tested on a previously used benchmark set of 200 documents and a larger dataset of 1000 documents. Results Tested on the benchmark dataset, our method achieved a precision of 0.390, recall of 0.712, and mean average precision (MAP) of 0.626. In comparison to the state of the art, we observe statistically significant improvements as large as 39% in MAP (p-value <0.001). Similar significant improvements were also obtained on the larger document set. Conclusion Experimental results show that our approach makes the most accurate MeSH predictions to date, which suggests its great potential in making a practical impact on MeSH indexing. Furthermore, as discussed the proposed learning framework is robust and can be adapted to many other similar tasks beyond MeSH indexing in the biomedical domain. All data sets are available at: http://***/CBBresearch/Lu/indexing.

关键词： Information storage and retrieval translational research—application of biological knowledge to clinical care discovery text and data mining methods natural-language processing Automated learning Delivering health information and knowledge to the public algorithms text mining data mining machine learning mesh MEDLINE indexing learning to rank algorithm

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A knowledge discovery and reuse pipeline for information extraction in clinical notes

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 574-579页

作者： Patrick, Jon D. Nguyen, Dung H. M. Wang, Yefeng Li, Min Univ Sydney Sch Informat Technol Fac Engn & IT Sydney NSW 2006 Australia

Objective Information extraction and classification of clinical data are current challenges in natural language processing. This paper presents a cascaded method to deal with three different extractions and classifications in clinical data: concept annotation, assertion classification and relation classification. Materials and methods A pipeline system was developed for clinical natural language processing that includes a proofreading process, with gold-standard reflexive validation and correction. The information extraction system is a combination of a machine learning approach and a rule-based approach. The outputs of this system are used for evaluation in all three tiers of the fourth i2b2/VA shared-task and workshop challenge. Results Overall concept classification attained an F-score of 83.3% against a baseline of 77.0%, the optimal F-score for assertions about the concepts was 92.4% and relation classifier attained 72.6% for relationships between clinical concepts against a baseline of 71.0%. Micro-average results for the challenge test set were 81.79%, 91.90% and 70.18%, respectively. Discussion The challenge in the multi-task test requires a distribution of time and work load for each individual task so that the overall performance evaluation on all three tasks would be more informative rather than treating each task assessment as independent. The simplicity of the model developed in this work should be contrasted with the very large feature space of other participants in the challenge who only achieved slightly better performance. There is a need to charge a penalty against the complexity of a model as defined in message minimalisation theory when comparing results. Conclusion A complete pipeline system for constructing language processing models that can be used to process multiple practical detection tasks of language structures of clinical records is presented.

关键词： agents automated learning classification clinical controlled terminologies and vocabularies designing usable (responsive) resources and systems discovery distributed systems information classification information extraction i2b2 challenge knowledge bases natural language processing ontologies software engineering: architecture text and data mining methods 2010 i2b2 challenge

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