Showing papers in "Journal of Biomedical Informatics in 2011"

TL;DR: The miRWalk database as mentioned in this paper is a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat.

TL;DR: A theoretical lens for understanding and studying complexity in healthcare systems based on degrees of interrelatedness of system components is proposed, and the feasibility of this approach for understanding, studying, and managing complex healthcare systems is explained.

TL;DR: A new measure based on the exploitation of the taxonomical structure of a biomedical ontology is proposed, using SNOMED CT as the input ontology and shows that it outperforms most of the previous measures avoiding, at the same time, some of their limitations.

TL;DR: It is stated that usability can not only be defined scientifically under a coherent, unified framework, it can also be measured objectively and systematically.

TL;DR: A clinical question answering system named AskHERMES is built to perform robust semantic analysis on complex clinical questions and output question-focused extractive summaries as answers and demonstrates the potential to outperform both Google and UpToDate systems.

TL;DR: The results show that TAM predicts a substantial proportion of the intention to use clinical information systems, in a random sample of 604 medical staff working in 14 hospitals in Greece.

TL;DR: It is shown that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods.

TL;DR: DEDUCE is envisioned as a simple, web-based environment that allows investigators access to administrative, financial, and clinical information generated during patient care that lets users filter through millions of clinical records, explore aggregate reports, and, export extracts.

TL;DR: An approach is presented that is able to collect a corpus of medical message board posts, de-identify the corpus, and extract information on potential adverse drug effects discussed by users to identify drug event pairs using co-occurrence statistics.

TL;DR: It is found that an information-theoretical redefinition of well-known semantic measures and similarity coefficients, and an intrinsic estimation of concept IC result in noticeable improvements in their accuracy, resulting in new semantic similarity measures expressed in terms of concept Information Content.

TL;DR: This work has proposed the first full solution for the automatic extraction of DDIs from biomedical texts and confirms that the shallow linguistic kernel outperforms the authors' previous pattern-based approach.

TL;DR: This work transforms the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation, and demonstrates the computability of this approach.

TL;DR: Qualitative and quantitative analyses suggest that a high proportion of TSA-related communication can be characterized as susceptible to information loss, and the design principles and potential benefits of such a display are outlined.

TL;DR: Methods developed in the fields of Natural Language Processing, information extraction, information retrieval and machine learning provide techniques for automating the enrichment of an ontology from free-text documents.

TL;DR: A theory-driven computational model of dialogue that simulates a human health counselor who is helping his or her clients to change via a series of conversations over time is described, demonstrating high levels of fidelity to best practices in health behavior change counseling.

TL;DR: peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents, which was used to answer questions about the disease state, quality state, certainty state, and temporal state of the exam.

TL;DR: A novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference model for pancreatic cancer prediction shows remarkable accuracy improvement over existing representative methods.

TL;DR: Preliminary results of an ongoing effort to normalize the Gene Ontology by explicitly stating the definitions of compositional classes in a form that can be used by reasoners are presented.

TL;DR: The proposed feature selection algorithm can be helpful in ranking the genes and also is capable of identifying the most relevant genes responsible for diseases like leukemia, colon tumor, lung cancer, diffuse large B-cell lymphoma, prostate cancer.

TL;DR: An approach to translate definitions expressed in the openEHR Archetype Definition Language to a formal representation expressed using the Ontology Web Language (OWL), providing an approach to apply the SWRL rules to concrete instances of clinical data.

TL;DR: The design and development of NanoParticle Ontology is discussed, which is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles.

TL;DR: A conceptual model of clinical summarization is developed that describes the creation of complex, task-specific clinical summaries and provides a framework for clinical workflow analysis and directed research on test results review, clinical documentation and medical decision-making.

TL;DR: Changes and improvements made to SO are reported including new relationships to better define the mereological, spatial and temporal aspects of biological sequence.

TL;DR: A framework for creating reference standards for functional testing of computerized measures of semantic relatedness is developed and it is shown that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers.

TL;DR: This study shows that automatic pre-annotations are found helpful by most annotators, and suggests using an automatic tool to assist large-scale manual annotation projects to speed-up the annotation time and improve annotation consistency while maintaining high quality of the final annotations.

TL;DR: Preliminary results on a noise-free dataset of ten surgical procedures show that it is possible to recognize surgical high-level tasks with detection accuracies up to 90%.

TL;DR: A new method of collecting additional data for the purpose of skin cancer research from the patients in the hospital using the system Mobile Computing in Medicine Graz (MoCoMed-Graz), which departs from the traditional paper-based questionnaire data collection methods and implements a new composition of evaluation methods to demonstrate its effectiveness.

TL;DR: The goal was to review recent advances in general purpose coreference resolution to lay the foundation for methodologies in the clinical domain, facilitated by the availability of a shared lexical resource of gold standard coreference annotations, the Ontology Development and Information Extraction (ODIE) corpus.

TL;DR: The RE-USE architecture and associated profiles are focused on defining a set of scalable, standards-based, IHE-compliant profiles that can enable single-source data collection/entry and cross-system data reuse through semantic integration.

TL;DR: Standardized interdisciplinary, nursing-specific, and physician-specific handoff information elements that are organized around the CCD standard and incorporated into EHRs in a structured narrative format may increase the consistency of data shared across all handoffs, facilitate the establishment of common ground, and increase interdisciplinary communication.