Showing papers in "Journal of Biomedical Informatics in 2012"

TL;DR: In this paper, the authors describe the features of mobile phones that make them a particularly promising platform for health interventions, and identify five basic intervention strategies that have been used in mobile-phone health applications across different health conditions.

TL;DR: The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports.

TL;DR: Suggestions outlined in this report may help clarify the goals of optimal CDS design but larger national initiatives are needed for systematic application of human factors in health information technology (HIT) development.

TL;DR: An ontology for the care of chronically ill patients is introduced and two personalization processes and a decision support tool are implemented that help health-care professionals to detect anomalous circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unob served related diseases, or preventive actions.

TL;DR: The empirical results of the study provide support for the EU CS model (incorporating new factors) and enhance the generalizability of the EUCS instrument and its robustness as a valid measure of computing satisfaction and a surrogate for system success in a variety of cultural and linguistic settings.

TL;DR: One year into the design and development of the SHARPn framework, end to end data flow is demonstrated and the EHR data throughput demonstration showed success in normalizing native Ehr data, both structured and narrative, from two independent organizations and EHR systems.

TL;DR: This paper describes an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface, showing that the inter-annotator agreement is much better than the agreement with the system provided annotations.

TL;DR: In this paper, the similarity measure between terms in an ontology and between entities annotated with terms drawn from the ontology, based on both co-occurrence and information content, is presented.

TL;DR: This paper proposes a generic architecture, associated terminology and a classificatory framework for comparing mobile patient monitoring systems, and shows how to use this framework to determine feature sets of prospective real-time mobile patient Monitoring systems using the example of epilepsy monitoring.

TL;DR: A production-ready development environment, compiler, rule engine and application server for Arden Syntax, which is currently working on incorporating the HL7 standard GELLO, which provides a standardized interface and query language for accessing data in health information systems.

TL;DR: It seems that a single screening question may be a reliable, valid, and feasible means for establishing health literacy, and a combination of screening questions that assess health literacy and technological literacy may enable tailoring eHealth applications to user needs.

TL;DR: This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text, and is the first effort exploring the use of distributional semantics.

TL;DR: The characteristics of biomolecular data are described that differentiate it from other classes of data commonly found in EHRs, a set of technical desiderata for its management in healthcare settings are enumerated, and a candidate technical approach to its compact and efficient representation in operational systems are offered.

TL;DR: The clinician-centered approach is proposed, which identifies the nature, inherent characteristics and the interdependencies between three phases of the handoff process and develops a descriptive framework of handoff communication in critical care that captures the non-linear, recursive and interactive nature of collaboration and decision-making.

TL;DR: The purpose of this study is to design a decision-making model that provides critical factors and knowledge associated with congestive heart failure (CHF) using an approach that makes use of rough sets (RSs) and decision trees.

TL;DR: An integrated framework that uses multi-level genomic data for prediction of clinical outcomes in brain cancer and ovarian cancer is proposed to improve comprehension of the molecular pathogenesis and underlying biology of both cancer types.

TL;DR: This paper explores the automatic classification of a set of SPs based on the Dynamic Time Warping (DTW) algorithm, which is used to compute a similarity measure between two SPs that focuses on the different types of activities performed during surgery and their sequencing, by minimizing time differences.

TL;DR: Novel feature engineering techniques that leverage the biomedical domain knowledge encoded in the UMLS to improve machine-learning based clinical text classification and a semantic similarity measure that projects clinical text into a feature space that improves classification are presented.

TL;DR: Overall, implementers reported a very positive experience with the HL7 Infobutton Standard, and implementers recommended HL7-compliant infobutton capabilities to be included in the United States Meaningful Use Certification Criteria for EHR systems.

TL;DR: The methods and results of a study in which a DC methodology was applied to study the use of infusion pumps by nurses in an Intensive Care Unit show that there is significant distribution of cognition in the ICU.

TL;DR: Alignment techniques can be used to derive consensus and to detect outliers from clinical activity logs and it is shown that outliers can be detected from surgeries using the consensus and the surgical activity log.

TL;DR: Methods of hyperdimensional computing are utilized to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery, demonstrating the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships and suggesting a role of these methods in drug repurposing efforts.

TL;DR: The Lombardy Region healthcare system is a fully interoperable social healthcare system connecting patients, healthcare providers, healthcare organizations, and healthcare professionals in a large and heterogeneous territory through the implementation of international health standards.

TL;DR: Although vocabulary mapping affected the semantic space of some outcome definitions, as well as the prevalence of some outcomes in the test databases, it had only minor effects on the analysis of drug-outcome associations.

TL;DR: The high rate of misclassification indicates that, until the specifications for qualifiers are better documented and/or brought more in line with common clinical usage, anyone attempting to use post-coordination in SNOMED CT must be aware that there are significant pitfalls.

TL;DR: This paper proposes a novel approach to tree kernel-based PPI extraction, where the tree representation generated from a constituent syntactic parser is further refined using the shortest dependency path between two proteins derived from a dependency parser.

TL;DR: A review of medication terminological systems and their linkages is provided, then a case study in which proprietary medication codes from an electronic health record to SNOMED CT and the UMLS Metathesaurus are mapped, to create a polyhierarchical classification system for querying an i2b2 clinical data warehouse.

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TL;DR: Two methods are proposed to tackle the problem of integrating heterogeneous knowledge sources by analysing the similarity between the modelled taxonomical knowledge and the structure of different ontologies and improve the accuracy of the similarity estimation.

TL;DR: This study contributes to the understanding of ontology development and evaluation methods and addresses one knowledge gap related to using ontologies as a clinical decision support system component-a need for formal ontology evaluation methods to measure their quality from the perspective of their intrinsic characteristics and their usefulness for specific tasks.