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iDASH: integrating data for analysis, anonymization, and sharing

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第2期19卷 196-201页

作者： Ohno-Machado, Lucila Bafna, Vineet Boxwala, Aziz A. Chapman, Brian E. Chapman, Wendy W. Chaudhuri, Kamalika Day, Michele E. Farcas, Claudiu Heintzman, Nathaniel D. Jiang, Xiaoqian Kim, Hyeoneui Kim, Jihoon Matheny, Michael E. Resnic, Frederic S. Vinterbo, Staal A. Univ Calif San Diego Div Biomed Informat La Jolla CA 92093 USA Univ Calif San Diego Dept Comp Sci La Jolla CA 92093 USA Univ Calif San Diego San Diego Supercomp Ctr La Jolla CA 92093 USA Univ Calif San Diego Calif Inst Telecommun & Informat Technol Calit2 La Jolla CA 92093 USA VA Tennessee Valley Healthcare Syst Res & Dev Serv Nashville TN USA Vanderbilt Univ Dept Med Nashville TN USA Brigham & Womens Hosp Dept Med Boston MA 02115 USA Harvard Univ Sch Med Boston MA USA

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

关键词： Privacy technology cloud computing electronic health records genomics natural language processing informatics machine learning predictive modeling statistical learning knowledge bases translational research—application of biological knowledge to clinical care developing/using clinical decision support (other than diagnostic) and guideline systems knowledge acquisition and knowledge management privacy technology cloud computing data mining detecting disease outbreaks and biological threats statistical analysis of large datasets discovery and text and data mining methods automated learning measuring/improving patient safety and reducing medical errors monitoring the health of populations

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Drug-drug interaction through molecular structure similarity analysis

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第6期19卷 1066-1074页

作者： Vilar, Santiago Harpaz, Rave Uriarte, Eugenio Santana, Lourdes Rabadan, Raul Friedman, Carol Columbia Univ Med Ctr Dept Biomed Informat New York NY 10032 USA Univ Santiago de Compostela Dept Organ Chem Santiago De Compostela Spain

Background Drug-drug interactions (DDIs) are responsible for many serious adverse events;their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs. methods We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. Drug Bank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in Drug Bank was computed to identify DDI candidates. Results The methodology was evaluated using as a gold standard the interactions retrieved from the initial Drug Bank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database. Conclusion The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.

关键词： Drug-drug interaction adverse drug event structure similarity molecular fingerprints QSAR molecular modeling drug design automated learning statistical analysis of large datasets discovery and text and data mining methods

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The National Center for Biomedical Ontology

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第2期19卷 190-195页

作者： Musen, Mark A. Noy, Natalya F. Shah, Nigam H. Whetzel, Patricia L. Chute, Christopher G. Story, Margaret-Anne Smith, Barry Stanford Univ Ctr Biomed Informat Res Stanford CA 94305 USA Mayo Clin Div Biomed Stat & Informat Rochester MN USA Univ Victoria Dept Comp Sci Victoria BC Canada SUNY Buffalo Dept Philosophy Buffalo NY 14260 USA

The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies;build tools and web services to enable the use of ontologies and terminologies in clinical and translational research;educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices;and collaborate with a variety of groups who develop and use ontologies and terminologies in biomedicine. The centerpiece of the National Center for Biomedical Ontology is a web-based resource known as BioPortal. BioPortal makes available for research in computationally useful forms more than 270 of the world's biomedical ontologies and terminologies, and supports a wide range of web services that enable investigators to use the ontologies to annotate and retrieve data, to generate value sets and special-purpose lexicons, and to perform advanced analytics on a wide range of biomedical data.

关键词： Collaborative technologies knowledge representations knowledge acquisition and knowledge management controlled terminologies and vocabularies ontologies knowledge bases applications that link biomedical knowledge from diverse primary sources (includes automated indexing) statistical analysis of large datasets methods for integration of information from disparate sources discovery and text and data mining methods automated learning information retrieval HIT data standards representing identifying and modeling biological structures developing and refining ehr data standards (including image standards)

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An informatics agenda for public health: summarized recommendations from the 2011 AMIA PHI Conference

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 688-695页

作者： Massoudi, Barbara L. Goodman, Kenneth W. Gotham, Ivan J. Holmes, John H. Lang, Lisa Miner, Kathleen Potenziani, David D. Richards, Janise Turner, Anne M. Fu, Paul C. RTI Int Ctr Adv Hlth IT Atlanta GA 30341 USA Univ Miami Eth Programs Miami FL USA New York State Dept Hlth Healthcom Network Syst Management Albany NY USA Univ Penn Perelman Sch Med Ctr Clin Epidemiol & Biostat Philadelphia PA 19104 USA NIH Natl Informat Ctr Hlth Serv Res & Hlth Care Techn Natl Lib Med Bethesda MD 20892 USA Emory Univ Rollins Sch Publ Hlth Dept Behav Sci & Hlth Educ Atlanta GA 30322 USA Univ N Carolina Sch Med Chapel Hill NC USA US Ctr Dis Control & Prevent Ctr Global Hlth Div Global HIV AIDS Atlanta GA USA Univ Washington Sch Med NW Ctr Publ Hlth Practice Dept Hlth Serv Seattle WA USA Univ Washington Sch Med NW Ctr Publ Hlth Practice Dept Med Educ Seattle WA USA Univ Washington Sch Med NW Ctr Publ Hlth Practice Dept Biomed Informat Seattle WA USA Univ Calif Los Angeles Sch Publ Hlth Los Angeles Biomed Res Inst Los Angeles Cty Harbor UCLA Med CtrDept Pediat Torrance CA USA Univ Calif Los Angeles Sch Publ Hlth Los Angeles Cty Harbor UCLA Med Ctr Los Angeles Biomed Res InstDept Hlth Serv Torrance CA USA

The AMIA Public Health Informatics 2011 Conference brought together members of the public health and health informatics communities to revisit the national agenda developed at the AMIA Spring Congress in 2001, assess the progress that has been made in the past decade, and develop recommendations to further guide the field. Participants met in five discussion tracks: technical framework;research and evaluation;ethics;education, professional training, and workforce development;and sustainability. Participants identified 62 recommendations, which clustered into three key themes related to the need to (1) enhance communication and information sharing within the public health informatics community, (2) improve the consistency of public health informatics through common public health terminologies, rigorous evaluation methodologies, and competency-based training, and (3) promote effective coordination and leadership that will champion and drive the field forward. The agenda and recommendations from the meeting will be disseminated and discussed throughout the public health and informatics communities. Both communities stand to gain much by working together to use these recommendations to further advance the application of information technology to improve health.

关键词： Conferences consensus development as topic informatics public health informatics public health practice public health ethics privacy confidentiality bioethics policy monitoring the health of populations knowledge representations statistical analysis of large datasets developing/using clinical decision support (other than diagnostic) and guideline systems discovery and text and data mining methods other methods of information extraction automated learning systems to support and improve diagnostic accuracy distributed systems agents software engineering: architecture health services research health reform health data standards qualitative/ethnographic field study improving the education and skills training of health professionals knowledge acquisition and knowledge management system implementation and management issues quality measures

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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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The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 370-375页

作者： Wei, Wei Visweswaran, Shyam Cooper, Gregory F. Univ Pittsburgh Dept Biomed Informat Pittsburgh PA 15260 USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA

Objective Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models. Design This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312 318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB). Measurement Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time. Results The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUG of 0.72, which is significantly better than the AUG of 0.59 by NB (p<0.00001), but not significantly different from the AUG of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study. Conclusion MANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.

关键词： Biomedical informatics systems to support and improve diagnostic accuracy uncertain reasoning and decision theory linking the genotype and phenotype discovery and text and data mining methods automated learning

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Computationally translating molecular discoveries into tools for medicine: translational bioinformatics articles now featured in JAMIA

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 352-353页

作者： Butte, Atul J. Shah, Nigam H. Stanford Univ Sch Med Dept Pediat Div Syst Med Stanford CA 94305 USA Lucile Packard Childrens Hosp Palo Alto CA USA Stanford Univ Sch Med Stanford Ctr Biomed Informat Res Stanford CA 94305 USA

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2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 552-556页

作者： Uzuner, Oezlem South, Brett R. Shen, Shuying DuVall, Scott L. SUNY Albany Coll Comp & Informat Dept Informat Studies Albany NY 12222 USA VA Salt Lake City Hlth Care Syst Salt Lake City UT USA Univ Utah Dept Internal Med Salt Lake City UT 84112 USA Univ Utah Dept Biomed Informat Salt Lake City UT USA

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports;an assertion classification task focused on assigning assertion types for medical problem concepts;and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.

关键词： Information storage and retrieval (text and images) discovery and text and data mining methods Other methods of information extraction Natural-language processing Automated learning visualization of data and knowledge uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery other methods of information extraction automated learning human-computer interaction and human-centered computing NLP machine learning Informatics

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Drug side effect extraction from clinical narratives of psychiatry and psychology patients

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第SUPPLEMENT_1期18卷 I144-I149页

作者： Sohn, Sunghwan Kocher, Jean-Pierre A. Chute, Christopher G. Savova, Guergana K. Mayo Clin Div Biomed Stat & Informat Dept Hlth Sci Res Rochester MN 55905 USA Childrens Hosp Boston MA 02115 USA Harvard Univ Sch Med Boston MA 02115 USA

Objective To extract physician-asserted drug side effects from electronic medical record clinical narratives. Materials and methods Pattern matching rules were manually developed through examining keywords and expression patterns of side effects to discover an individual side effect and causative drug relationship. A combination of machine learning (C4.5) using side effect keyword features and pattern matching rules was used to extract sentences that contain side effect and causative drug pairs, enabling the system to discover most side effect occurrences. Our system was implemented as a module within the clinical text Analysis and Knowledge Extraction System. Results The system was tested in the domain of psychiatry and psychology. The rule-based system extracting side effects and causative drugs produced an F score of 0.80 (0.55 excluding allergy section). The hybrid system identifying side effect sentences had an F score of 0.75 (0.56 excluding allergy section) but covered more side effect and causative drug pairs than individual side effect extraction. Discussion The rule-based system was able to identify most side effects expressed by clear indication words. More sophisticated semantic processing is required to handle complex side effect descriptions in the narrative. We demonstrated that our system can be trained to identify sentences with complex side effect descriptions that can be submitted to a human expert for further abstraction. Conclusion Our system was able to extract most physician-asserted drug side effects. It can be used in either an automated mode for side effect extraction or semi-automated mode to identify side effect sentences that can significantly simplify abstraction by a human expert.

关键词： Natural language processing machine learning information extraction electronic medical record Information storage and retrieval (text and images) discovery and text and data mining methods Other methods of information extraction Natural-language processing bioinformatics Ontologies Knowledge representations Controlled terminologies and vocabularies Information Retrieval HIT data Standards Human-computer interaction and human-centered computing Providing just-in-time access to the biomedical literature and other health information Applications that link biomedical knowledge from diverse primary sources (includes automated indexing) Linking the genotype and phenotype

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