Volume 1 (2016): Edition 2 (May 2016)

The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. To improve patient safety reporting, the present paper suggests an ontological representation of patient safety knowledge.

We propose a framework for constructing an ontological knowledge base of patient safety. The present paper describes our design, implementation, and evaluation of the ontology at its initial stage.

We describe the design and initial outcomes of the ontology implementation. The evaluation results demonstrate the clinical validity of the ontology by a self-developed survey measurement.

The proposed ontology was developed and evaluated using a small number of information sources. Presently, US data are used, but they are not essential for the ultimate structure of the ontology.

The goal of improving patient safety can be aided through investigating patient safety reports and providing actionable knowledge to clinical practitioners. As such, constructing a domain specific ontology for patient safety reports serves as a cornerstone in information collection and text mining methods.

The use of ontologies provides abstracted representation of semantic information and enables a wealth of applications in a reporting system. Therefore, constructing such a knowledge base is recognized as a high priority in health care.

In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationships to a user’s intentions.

Based on technology acceptance model (TAM), network externality, trust, and an interview survey, this study proposes a personal cloud storage adoption model. We conducted an empirical analysis by structural equation modeling based on survey data obtained with a questionnaire.

Among the adoption factors we identified, network externality has the salient influence on a user’s adoption intention, followed by perceived usefulness, individual innovation, perceived trust, perceived ease of use, and subjective norms. Cloud storage characteristics are the most important indirect factors, followed by awareness to personal cloud storage and perceived risk. However, although perceived risk is regarded as an important factor by other cloud computing researchers, we found that it has no significant influence. Also, subjective norms have no significant influence on perceived usefulness. This indicates that users are rational when they choose whether to adopt personal cloud storage.

This study ignores time and cost factors that might affect a user’s intention to adopt personal cloud storage.

Our findings might be helpful in designing and developing personal cloud storage products, and helpful to regulators crafting policies.

This study is one of the first research efforts that discuss Chinese users’ personal cloud storage adoption, which should help to further the understanding of personal cloud adoption behavior among Chinese users.

This paper is an investigation of the effectiveness of the method of clustering biomedical journals through mining the content similarity of journal articles.

3,265 journals in PubMed are analyzed based on article content similarity and Web usage, respectively. Comparisons of the two analysis approaches and a citation-based approach are given.

Our results suggest that article content similarity is useful for clustering biomedical journals, and the content-similarity-based journal clustering method is more robust and less subject to human factors compared with the usage-based approach and the citation-based approach.

Our paper currently focuses on clustering journals in the biomedical domain because there are a large volume of freely available resources such as PubMed and MeSH in this field. Further investigation is needed to improve this approach to fit journals in other domains.

Our results show that it is feasible to catalog biomedical journals by mining the article content similarity. This work is also significant in serving practical needs in research portfolio analysis.

To the best of our knowledge, we are among the first to report on clustering journals in the biomedical field through mining the article content similarity. This method can be integrated with existing approaches to create a new paradigm for future studies of journal clustering.

In the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover, scientific publications are preserved in a digital library archive. It is challenging to identify the data usage that is mentioned in literature and associate it with its source. Here, we investigated the data usage of a government-funded cancer genomics project, The Cancer Genome Atlas (TCGA), via a full-text literature analysis.

We focused on identifying articles using the TCGA dataset and constructing linkages between the articles and the specific TCGA dataset. First, we collected 5,372 TCGA-related articles from PubMed Central (PMC). Second, we constructed a benchmark set with 25 full-text articles that truly used the TCGA data in their studies, and we summarized the key features of the benchmark set. Third, the key features were applied to the remaining PMC full-text articles that were collected from PMC.

The amount of publications that use TCGA data has increased significantly since 2011, although the TCGA project was launched in 2005. Additionally, we found that the critical areas of focus in the studies that use the TCGA data were glioblastoma multiforme, lung cancer, and breast cancer; meanwhile, data from the RNA-sequencing (RNA-seq) platform is the most preferable for use.

The current workflow to identify articles that truly used TCGA data is labor-intensive. An automatic method is expected to improve the performance.

This study will help cancer genomics researchers determine the latest advancements in cancer molecular therapy, and it will promote data sharing and data-intensive scientific discovery.

Few studies have been conducted to investigate data usage by government-funded projects/programs since their launch. In this preliminary study, we extracted articles that use TCGA data from PMC, and we created a link between the full-text articles and the source data.

To comprehensively evaluate the overall performance of a group or an individual in both bibliometrics and patentometrics.

Trace metrics were applied to the top 30 universities in the 2014 Academic Ranking of World Universities (ARWU)—computer sciences, the top 30 ESI highly cited papers in the computer sciences field in 2014, as well as the top 30 assignees and the top 30 most cited patents in the National Bureau of Economic Research (NBER) computer hardware and software category.

We found that, by applying trace metrics, the research or marketing impact efficiency, at both group and individual levels, was clearly observed. Furthermore, trace metrics were more sensitive to the different publication-citation distributions than the average citation and h-index were.

Trace metrics considered publications with zero citations as negative contributions. One should clarify how he/she evaluates a zero-citation paper or patent before applying trace metrics.

Decision makers could regularly examine the performance of their university/company by applying trace metrics and adjust their policies accordingly.

Trace metrics could be applied both in bibliometrics and patentometrics and provide a comprehensive view. Moreover, the high sensitivity and unique impact efficiency view provided by trace metrics can facilitate decision makers in examining and adjusting their policies.