Showing papers in "Journal of Biomedical Informatics in 2014"

TL;DR: This review provided a conceptual framework of sample size calculations in the studies of diagnostic test accuracy in various conditions and test outcomes to help clinicians when designing diagnostic test studies that an adequate sample size is chosen based on statistical principles in order to guarantee the reliability of study.

TL;DR: The results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.

TL;DR: This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN) and describes the inherent discrimination capacity of the proposed system.

TL;DR: In this article, the authors discuss possible application scenarios for homomorphic encryption in order to ensure privacy of sensitive medical data, and present a working implementation of a prediction service running in the cloud (hosted on Microsoft's Windows Azure).

TL;DR: This work provides a summary of the Vanderbilt research data warehouse framework's approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others assembling similar services and infrastructure.

TL;DR: A systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems, molecular epidemiology, and social network analysis, identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools.

TL;DR: This work presents a survey of algorithms that have been proposed for publishing structured patient data, in a privacy-preserving way, and derives insights on their operation, and highlights their advantages and disadvantages.

TL;DR: A review of the literature aiming at identifying what is being referred to as "time motion studies" can be found in this paper, where the authors present a disambiguation approach by preserving the expanded conception, while recommending the use of a specific qualifier "continuous observation time motion studies".

TL;DR: The Integrated model performed the best and showed that perceived ease of use, professional norm, social norm, and demonstrability of the results are the strongest predictors of physicians' intention to use the EHR.

TL;DR: The proposed Limestone, a nonnegative tensor factorization method to derive phenotype candidates with virtually no human supervision, is proposed and multiple phenotypes can be identified simultaneously from data.

TL;DR: This is the first systematic review of text summarization in the biomedical domain and identified research gaps and provides recommendations for guiding future research on biomedicalText summarization.

TL;DR: The Secure Anonymized Information Linkage (SAIL) Gateway as discussed by the authors provides data users with a familiar Windows interface and their usual toolsets to access approved anonymously-linked datasets for research and evaluation.

TL;DR: This work extends the methods developed in Uhler et al. (2013) for releasing differentially-private χ(2)-statistics by allowing for arbitrary number of cases and controls, and provides a new interpretation by assuming the controls' data are known, which is a realistic assumption because some GWAS use publicly available data as controls.

TL;DR: This paper presents a unifying framework that aims to improve the understanding of semantic measures, to highlight their equivalences and to propose bridges between their theoretical bases, and unify a large number of state-of-the-art semantic similarity measures through common expressions.

TL;DR: In this paper, the authors applied active learning with two criteria (certainty and uncertainty) and several enhancements in both clinical medicine and social science (specifically, public health) areas, and compared the results in both.

TL;DR: This paper presents a method for interactive pattern mining and analysis that supports ad hoc visual exploration of patterns mined from retrospective clinical patient data and describes a prototype implementation and use cases highlighting the types of insights or hypotheses that the approach can help uncover.

TL;DR: The entity recognition results for the individual entities Disorder and Finding show that it is meaningful to separate the general category Medical Problem into these two more granular entity types, e.g. for knowledge mining of co-morbidity relations and disorder-finding relations.

TL;DR: This paper aims to address the limitations posed by the traditional bag-of-word based methods and propose to use heterogeneous features in combination with ensemble machine learning techniques to discover health-related information, which could prove to be useful to multiple biomedical applications.

TL;DR: This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data and can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information.

TL;DR: This study proposes a novel approach to CP pattern discovery by modeling CPs using mixtures of an extension to the Latent Dirichlet Allocation family that jointly models various treatment activities and their occurring time stamps in CPs.

TL;DR: The privacy preserving linkage method was tested on ten years of New South Wales and Western Australian hospital admissions data, comprising in total over 26 million records, and no difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers.

TL;DR: This edition has been the first attempt to compare the performance of Information Extraction techniques specific for each of the basic steps of the DDI extraction pipeline and shows advances in the state of the art and demonstrates that significant challenges remain to be resolved.

TL;DR: A surgical training simulator with virtual and haptic force feedback for maxillofacial surgery was developed and validated the effects on the learning of bone-sawing skills through empirical evaluation, indicating that this simulator was able to produce the effect of learning bone- Sawing skill, and it could provide a training alternative for novices.

TL;DR: A methodology for leveraging measurement frequency to identify and reduce laboratory test biases is presented, showing that the context of a laboratory test measurement can often be captured by the way the test is measured through time.

TL;DR: A reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests and demonstrates the effectiveness of the developed system when compared to the gold standard.

TL;DR: The key informatics use cases of each phase of a study's scientific lifecycle are described, OCRe is presented and the principles behind its modeling are presented, and applications of OCRe and associated technologies to a range of clinical research use cases are described.

TL;DR: The results indicate that the VR-based ACLs training with proper feedback components can provide a learning experience similar to face-to-face training, and therefore could serve as a more easily accessed supplementary training tool to the traditional ACLS training.

TL;DR: A Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses, which enables reliable and highly scalable execution of sequencing analyses workflows in a fully automated manner is proposed.

TL;DR: It is demonstrated that both RRI and PSI can recover known drug-adverse event associations, and RRI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.

TL;DR: A clear, comprehensive strategy requiring collaborative efforts by clinical and public health stakeholders is suggested as a guide for the long road towards better population health data and outcomes.