Journal of Biomedical Informatics (J Biomed Informat)

Publications in this journal

• Article: Leveraging Concept-based Approaches to Identify Potential Phyto-therapies.

  Vivekanand Sharma, Indra Neil Sarkar
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  ABSTRACT: The potential of plant-based remedies has been documented in both traditional and contemporary biomedical literature. Such types of text sources may thus be sources from which one might identify potential plant-based therapies ("phyto-therapies"). Concept-based analytic approaches have been shown to uncover knowledge embedded within biomedical literature. However, to date there has been limited attention towards leveraging such techniques for the identification of potential phyto-therapies. This study presents concept-based analytic approaches for the retrieval and ranking of associations between plants and human diseases. Focusing on identification of phyto-therapies described in MEDLINE, both MeSH descriptors used for indexing and MetaMap inferred UMLS concepts are considered. Furthermore, the identification and ranking consider both direct (i.e., plant concepts directly correlated with disease concepts) and inferred (i.e., plant concepts associated with disease concepts based on shared signs and symptoms) relationships. Based on the two scoring methodologies used in this study, it was found that a vector space model approach outperformed probabilistic reliability based inferences. An evaluation of the approach is provided based on therapeutic interventions catalogued in both ClinicalTrials.gov and NDF-RT. The promising findings from this feasibility study highlight the challenges and applicability of concept-based analytic strategies for distilling phyto-therapeutic knowledge from text based knowledge sources like MEDLINE.
  Journal of Biomedical Informatics 05/2013;
• Article: TRAK ontology: Defining standard care for the rehabilitation of knee conditions.

  Kate Button, Robert W van Deursen, Larisa Soldatova, Irena Spasić
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  ABSTRACT: In this paper we discuss the design and development of TRAK (Taxonomy for RehAbilitation of Knee conditions), an ontology that formally models information relevant for the rehabilitation of knee conditions. TRAK provides the framework that can be used to collect coded data in sufficient detail to support epidemiologic studies so that the most effective treatment components can be identified, new interventions developed and the quality of future randomized control trials improved to incorporate a control intervention that is well defined and reflects clinical practice. TRAK follows design principles recommended by the Open Biomedical Ontologies (OBO) Foundry. TRAK uses the Basic Formal Ontology (BFO) as the upper-level ontology and refers to other relevant ontologies such as Information Artifact Ontology (IAO), Ontology for General Medical Science (OGMS), Phenotype And Trait Ontology (PATO), etc. TRAK is orthogonal to other bio-ontologies and represents domain-specific knowledge about treatments and modalities used in rehabilitation of knee conditions. Definitions of typical exercises used as treatment modalities are supported with appropriate illustrations, which can be viewed in the OBO-Edit ontology editor. The vast majority of other classes in TRAK are cross-referenced to the Unified Medical Language System (UMLS) to facilitate future integration with other terminological sources. TRAK is implemented in OBO, a format widely used by the OBO community. TRAK is available for download from http://www.cs.cf.ac.uk/trak. In addition, its public release can be accessed through BioPortal, where it can be browsed, searched and visualized.
  Journal of Biomedical Informatics 05/2013;
• Article: Scenarios, personas and user stories: User-centered Evidence-based Design Representations of Communicable Disease Investigations.

  Anne M Turner, Blaine Reeder, Judith Ramey
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  ABSTRACT: PURPOSE: Despite years of effort and millions of dollars spent to create a unified electronic communicable disease reporting systems, the goal remains elusive. A major barrier has been a lack of understanding by system designers of communicable disease (CD) work and the public health workers who perform this work. This study reports on the application of User Center Design representations, traditionally used for improving interface design, to translate the complex CD work identified through ethnographic studies to guide designers and developers of CD systems. The purpose of this work is to: (1) better understand public health practitioners and their information workflow with respect to communicable disease (CD) monitoring and control at a local health department, and (2) to develop evidence-based design representations that model this CD work to inform the design of future disease surveillance systems. METHODS: We performed extensive onsite semi-structured interviews, targeted work shadowing and a focus group to characterize local health department communicable disease workflow. Informed by principles of design ethnography and user-centered design (UCD) we created persona, scenarios and user stories to accurately represent the user to system designers. RESULTS: We sought to convey to designers the key findings from ethnographic studies: 1) that public health CD work is mobile and episodic, in contrast to current CD reporting systems, which are stationary and fixed 2) health department efforts are focused on CD investigation and response rather than reporting and 3) current CD information systems must conform to PH workflow to ensure their usefulness. In an effort to illustrate our findings to designers, we developed three contemporary design-support representations: persona, scenario, and user story. CONCLUSIONS: Through application of user centered design principles, we were able to create design representations that illustrate complex public health communicable disease workflow and key user characteristics to inform the design of CD information systems for public health.
  Journal of Biomedical Informatics 04/2013;
• Article: Applying Formalized Rules for Treatment Procedures to Data Delivered by Personal Medical Devices.

  Kornel Skałkowski, Krzysztof Zieliński
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  ABSTRACT: The paper presents a novel approach to online application of formalized rules for medical treatment procedures when processing data from personal medical devices. The rules are formalized by using a rule-based reasoning approach and are applied in order to enhance patient safety and support physicians in their daily work. The presented approach relies on dividing data processing into two stages: 1) the event processing stage and 2) the knowledge application stage. At the event processing stage raw data produced by personal medical devices is transformed into an aggregated/correlated form, as required by the rules for treatment procedures. At the knowledge application stage formalized rules are applied to transformed data, resulting in execution of various support actions. This paper describes how rules for treatment of patients suffering from cardiovascular diseases can be expressed in terms of an event processing statement set and a rule engine knowledge base. The technical feasibility of the proposed approach is supported by a detailed description of the TeleCARE remote healthcare framework - an implementation of the proposed approach along with evaluation performed using a large number of simulated personal medical devices.
  Journal of Biomedical Informatics 04/2013;
• Article: Using Chief Complaints for Syndromic Surveillance: A Review of Chief Complaint Based Classifiers in North America.

  Mike Conway, John N Dowling, Wendy W Chapman
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  ABSTRACT: A major goal of Natural Language Processing in the public health informatics domain is the automatic extraction and encoding of data stored in free text patient records. This extracted data can then be utilized by computerized systems to perform syndromic surveillance. In particular, the chief complaint - a short string that describes a patient's symptoms - has come to be a vital resource for syndromic surveillance in the North American context due to its near ubiquity. This paper reviews fifteen systems in North America - at the city, county, state and federal level - that use chief complaints for syndromic surveillance.
  Journal of Biomedical Informatics 04/2013;
• Article: Recommendations for the Design, Implementation and Evaluation of Social Support in Online Communities, Networks, and Groups.

  Jacob B Weiss, Eta S Berner, Kevin B Johnson, Dario A Giuse, Barbara A Murphy, Nancy M Lorenzi
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  ABSTRACT: A new model of health care is emerging in which individuals can take charge of their health by connecting to online communities and social networks for personalized support and collective knowledge. Web 2.0 technologies expand the traditional notion of online support groups into a broad and evolving range of informational, emotional, as well as community-based concepts of support. In order to apply these technologies to patient-centered care, it is necessary to incorporate more inclusive conceptual frameworks of social support and community-based research methodologies. This paper introduces a conceptualization of online social support, reviews current challenges in online support research, and outlines six recommendations for the design, evaluation, and implementation of social support in online communities, networks, and groups. The six recommendations are illustrated by CanConnect, an online community for cancer survivors in Middle Tennessee. These recommendations address the interdependencies between online and real-world support and emphasize an inclusive framework of interpersonal and community-based support. The applications of these six recommendations are illustrated through a discussion of online support for cancer survivors.
  Journal of Biomedical Informatics 04/2013;
• Article: A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine.

  Rong Xu, Quanqiu Wang
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  ABSTRACT: Personalized medicine is to deliver the right drug to the right patient in the right dose. Pharmacogenomics (PGx) is to identify genetic variants that may affect drug efficacy and toxicity. The availability of a comprehensive and accurate PGx-specific drug-gene relationship knowledge base is important for personalized medicine. However, building a large-scale PGx-specific drug-gene knowledge base is a difficult task. In this study, we developed a bootstrapping, semi-supervised learning approach to iteratively extract and rank drug-gene pairs according to their relevance to drug pharmacogenomics. Starting with a single PGx-specific seed pair and 20 million MEDLINE abstracts, the extraction algorithm achieved a precision of 0.219, recall of 0.368 and F1 of 0.274 after two iterations, a significant improvement over the results of using non-PGx-specific seeds (precision: 0.011, recall: 0.018, and F1: 0.014) or co-occurrence (precision: 0.015, recall: 1.000, and F1: 0.030). After the extraction step, the ranking algorithm further improved the precision from 0.219 to 0.561 for top ranked pairs. By comparing to a dictionary-based approach with PGx-specific gene lexicon as input, we showed that the bootstrapping approach has better performance in terms of both precision and F1 (precision: 0.251 vs. 0.152, recall: 0.396 vs. 0.856 and F1: 0.292 vs. 0.254). By integrative analysis using a large drug adverse event database, we have shown that the extracted drug-gene pairs strongly correlate with drug adverse events. In conclusion, we developed a novel semi-supervised bootstrapping approach for effective PGx-specific drug-gene pair extraction from large number of MEDLINE articles with minimal human input.
  Journal of Biomedical Informatics 04/2013;
• Article: A three stage ontology-driven solution to provide personalized care to chronic patients at home.

  N Lasierra, A Alesanco, S Guillén, J García
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  ABSTRACT: PURPOSE: The goal of this work is to contribute to personalized clinical management in home-based telemonitoring scenarios by developing an ontology-driven solution that enables a wide range of remote chronic patients to be monitored at home. METHODS: Through three stages, the challenges of integration and management were met through the ontology development and evaluation. The first stage dealt with the ontology design and implementation. The second stage dealt with the ontology application study in order to specifically address personalization issues. For both stages, interviews and working sessions were planned with clinicians. Clinical guidelines and MDs (medical device) interoperability were taken into account as well during these stages. Finally the third stage dealt with a software prototype implementation. RESULTS: An ontology was developed as an outcome of the first stage. The structure, based on the autonomic computing paradigm, provides a clear and simple manner to automate and integrate the data management procedure. During the second stage, the application of the ontology was studied to monitor patients with different and multiple morbidities. After this task, the ontology design was successfully adjusted to provide useful personalized medical care. In the third and final stage, a proof-of-concept on the software required to remote monitor patients by means of the ontology-based solution was developed and evaluated. CONCLUSIONS: Our proposed ontology provides an understandable and simple solution to address integration and personalized care challenges in home-based telemonitoring scenarios. Furthermore, our three-stage approach contributes to enhance the understanding, re-usability and transferability of our solution.
  Journal of Biomedical Informatics 04/2013;
• Article: Selecting significant genes by randomization test for cancer classification using gene expression data.

  Zhiyi Mao, Wensheng Cai, Xueguang Shao
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  ABSTRACT: Gene selection is an important task in bioinformatics studies, because the accuracy of cancer classification generally depends upon the genes that have biological relevance to the classifying problems. In this work, randomization test (RT) is used as a gene selection method for dealing with gene expression data. In the method, a statistic derived from the statistics of the regression coefficients in a series of partial least squares discriminant analysis (PLSDA) models is used to evaluate the significance of the genes. Informative genes are selected for classifying the four gene expression datasets of prostate cancer, lung cancer, leukemia and non-small cell lung cancer (NSCLC) and the rationality of the results is validated by multiple linear regression (MLR) modeling and principal component analysis (PCA). With the selected genes, satisfactory results can be obtained.
  Journal of Biomedical Informatics 04/2013;
• Article: A Controlled Greedy Supervised Approach for Co-reference Resolution on Clinical Text.

  Md Faisal Mahbub Chowdhury, Pierre Zweigenbaum
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  ABSTRACT: Identification of co-referent entity mentions inside text has significant importance for other natural language processing (NLP) tasks (e.g.event linking). However, this task, known as co-reference resolution, remains a complex problem, partly because of the confusion over different evaluation metrics and partly because the well-researched existing methodologies do not perform well on new domains such as clinical records. This paper presents a variant of the influential mention-pair model for co-reference resolution. Using a series of linguistically and semantically motivated constraints, the proposed approach controls generation of less-informative/sub-optimal training and test instances. Additionally, the approach also introduces some aggressive greedy strategies in chain clustering. The proposed approach has been tested on the official test corpus of the recently held i2b2/VA 2011 challenge. It achieves an unweighted average F1 score of 0.895, calculated from multiple evaluation metrics (MUC,B(3) and CEAF scores). These results are comparable to the best systems of the challenge. What makes our proposed system distinct is that it also achieves high average F1 scores for each individual chain type (Test: 0.897, Person: 0.852, Problem: 0.855, Treatment: 0.884). Unlike other works, it obtains good scores for each of the individual metrics rather than being biased towards a particular metric.
  Journal of Biomedical Informatics 04/2013;
• Article: EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed Privacy-Preserving Online Model Learning.

  Shuang Wang, Xiaoqian Jiang, Yuan Wu, Lijuan Cui, Samuel Cheng, Lucila Ohno-Machado
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  ABSTRACT: We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications.
  Journal of Biomedical Informatics 04/2013;
• Article: An Analysis of FMA Using Structural Self-Bisimilarity.

  Lingyun Luo, Jose L V Mejino, Guo-Qiang Zhang
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  ABSTRACT: As ontologies are mostly manually created, they tend to contain errors and inconsistencies. In this paper, we present an automated computational method to audit symmetric concepts in ontologies by leveraging self-bisimilarity and linguistic structure in the concept names. Two concepts A and B are symmetric if concept B can be obtained from concept A by replacing a single modifier such as "left" with its symmetric modifier such as "right." All possible local structural types for symmetric concept pairs are enumerated according to their local subsumption hierarchy, and the pairs are further classified into Non-Matches and Matches. To test the feasibility and validate the benefits of this method, we computed all the symmetric modifier pairs in the Foundational Model of Anatomy (FMA) and selected six of them for experimentation. 9893 Non-Matches and 221 abnormal Matches with potential errors were discovered by our algorithm. Manual evaluation by FMA domain experts on 176 selected Non-Matches and all the 221 abnormal Matches found 102 missing concepts and 40 misaligned concepts. Corrections for them have currently been implemented in the latest version of FMA. Our result demonstrates that self-bisimilarity can be a valuable method for ontology quality assurance, particularly in uncovering missing concepts and misaligned concepts. Our approach is computationally scalable and can be applied to other ontologies that are rich in symmetric concepts.
  Journal of Biomedical Informatics 04/2013;
• Article: A Novel Approach for Connecting Temporal-Ontologies with Blood Flow Simulations.

  F Weichert, C Mertens, L Walczak, G Kern-Isberner, M Wagner
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  ABSTRACT: In this paper an approach for developing a temporal domain ontology for biomedical simulations is introduced. The ideas are presented in the context of simulations of blood flow in aneurysms using the Lattice Boltzmann Method. The advantages in using ontologies are manyfold: On the one hand, ontologies having been proven to be able to provide medical special knowledge e.g., key parameters for simulations. On the other hand, based on a set of rules and the usage of a reasoner, a system for checking the plausibility as well as tracking the outcome of medical simulations can be constructed. Likewise, results of simulations including data derived from them can be stored and communicated in a way that can be understood by computers. Later on, this set of results can be analyzed. At the same time, the ontologies provide a way to exchange knowledge between researchers. Lastly, this approach can be seen as a black-box abstraction of the internals of the simulation for the biomedical researcher as well. This approach is able to provide the complete parameter sets for simulations, part of the corresponding results and part of their analysis as well as e.g., geometry and boundary conditions. These inputs can be transferred to different simulation methods for comparison. Variations on the provided parameters can be automatically used to drive these simulations. Using a rule base, unphysical inputs or outputs of the simulation can be detected and communicated to the physician in a suitable and familiar way. An example for an instantiation of the blood flow simulation ontology and exemplary rules for plausibility checking are given.
  Journal of Biomedical Informatics 03/2013;
• Article: An Autonomous Mobile System for the Management of COPD.

  Maarten van der Heijden, Peter J F Lucas, Bas Lijnse, Yvonne F Heijdra, Tjard R J Schermer
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  ABSTRACT: Introduction: Managing chronic disease through automated systems has the potential to both benefit the patient and reduce health-care costs. We have developed and evaluated a disease management system for patients with chronic obstructive pulmonary disease (COPD). Its aim is to predict and detect exacerbations and, through this, help patients self-manage their disease to prevent hospitalisation. Materials: The carefully crafted intelligent system consists of a mobile device that is able to collect case-specific, subjective and objective, physiological data, to alert the patient by a patient-specific interpretation of the data by means of probabilistic reasoning. Collected data are also sent to a central server for inspection by health-care professionals. Methods: We evaluated the probabilistic model using cross-validation and ROC analyses on data from an earlier study and by an independent data set. Furthermore a pilot with actual COPD patients has been conducted to test technical feasibility and to obtain user feedback. Results: Model evaluation results show that we can reliably detect exacerbations. Pilot study results suggest that an intervention based on this system could be successful.
  Journal of Biomedical Informatics 03/2013;
• Article: A Method for Estimating from Thermometer Sales the Incidence of Diseases that are Symptomatically Similar to Influenza.

  Ricardo Villamarín, Gregory Cooper, Michael Wagner, Fu-Chiang Tsui, Jeremy U Espino
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  ABSTRACT: Early detection and accurate characterization of disease outbreaks are important tasks of public health. Infectious diseases that present symptomatically like influenza (SLI), including influenza itself, constitute an important class of diseases that are monitored by public-health epidemiologists. Monitoring emergency department (ED) visits for presentations of SLI could provide an early indication of the presence, extent, and dynamics of such disease in the population. We investigated the use of daily over-the-counter thermometer-sales data to estimate daily ED SLI counts in Allegheny County (AC), Pennsylvania. We found that a simple linear model fits the data well in predicting daily ED SLI counts from daily counts of thermometer sales in AC. These results raise the possibility that this model could be applied, perhaps with adaptation, in other regions of the country, where commonly thermometer sales data are available, but daily ED SLI counts are not.
  Journal of Biomedical Informatics 03/2013;
• Article: Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role.

  Kleftogiannis Dimitrios, Korfiati Aigli, Theofilatos Konstantinos, Likothanassis Spiros, Tsakalidis Athanasios, Mavroudi Seferina
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  ABSTRACT: Traditional biology was forced to restate some of its principles when the microRNA (miRNA) genes and their regulatory role were firstly discovered. Typically, miRNAs are small non-coding RNA molecules which have the ability to bind to the 3'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. Existing experimental techniques for their identification and the prediction of the target genes share some important limitations such as low coverage and cost. Hence, many computational methods have been proposed for these tasks to overcome these limitations. Recently, many researchers emphasized on the development of computational approaches to predict the participation of miRNA genes in regulatory networks and to analyze their transcription mechanisms. All these approaches have certain advantages and disadvantages which are going to be described in the present survey. Our work is differentiated from existing review papers by updating the methodologies list and emphasizing on the computational issues that arise from the miRNA data analysis. Furthermore, in the present survey, the various miRNA data analysis steps are treated as an integrated procedure whose aims and scope is to uncover the regulatory role and mechanisms of the miRNA genes. This integrated view of the miRNA data analysis steps may be extremely useful for all researchers even if they work on just a single step.
  Journal of Biomedical Informatics 03/2013;
• Article: Complementary ensemble clustering of biomedical data.

  Samah Jamal Fodeh, Cynthia Brandt, Thai Binh Luong, Ali Haddad, Martin Schultz, Terrence Murphy, Michael Krauthammer
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  ABSTRACT: The rapidly growing availability of electronic biomedical data has increased the need for innovative data mining methods. Clustering in particular has been an active area of research in many different application areas, with existing clustering algorithms mostly focusing on one modality or representation of the data. Complementary ensemble clustering (CEC) is a recently introduced framework in which Kmeans is applied to a weighted, linear combination of the coassociation matrices obtained from separate ensemble clustering of different data modalities. The strength of CEC is its extraction of information from multiple aspects of the data when forming the final clusters. This study assesses the utility of CEC in biomedical data, which often have multiple data modalities, e.g., text and images, by applying CEC to two distinct biomedical datasets (PubMed images and radiology reports) that each have two modalities. Referent to five different clustering approaches based on the Kmeans algorithm, CEC exhibited equal or better performance in the metrics of micro-averaged precision and Normalized Mutual Information across both datasets. The reference methods included clustering of single modalities as well as ensemble clustering of separate and merged data modalities. Our experimental results suggest that CEC is equivalent or more efficient than comparable Kmeans based clustering methods using either single or merged data modalities.
  Journal of Biomedical Informatics 02/2013;
• Article: MPM: A Knowledge-Based Functional Model of Medical Practice.

  David Riaño, John A Bohada, Antoni Collado, Joan A López-Vallverdú
  Journal of Biomedical Informatics 02/2013;
• Article: An enhanced CRFs-based system for information extraction from radiology reports.

  Andrea Esuli, Diego Marcheggiani, Fabrizio Sebastiani
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  ABSTRACT: We discuss the problem of performing information extraction from free-text radiology reports via supervised learning. In this task, segments of text (not necessarily coinciding with entire sentences, and possibly crossing sentence boundaries) need to be annotated with tags representing concepts of interest in the radiological domain. In this paper we present two novel approaches to IE for radiology reports: (i) a cascaded, two-stage method based on pipelining two taggers generated via the well known linear-chain conditional random fields (LC-CRFs) learner and (ii) a confidence-weighted ensemble method that combines standard LC-CRFs and the proposed two-stage method. We also report on the use of "positional features", a novel type of feature intended to aid in the automatic annotation of texts in which the instances of a given concept may be hypothesized to systematically occur in specific areas of the text. We present experiments on a dataset of mammography reports in which the proposed ensemble is shown to outperform a traditional, single-stage CRFs system in two different, applicatively interesting scenarios.
  Journal of Biomedical Informatics 02/2013;