scholarly journals Towards identifying author confidence in biomedical articles

2018 ◽  
Author(s):  
Daniela Gifu ◽  
Mihaela Onofrei ◽  
Diana Trandabat
Keyword(s):  
Molecular Biology ◽  
Medical Literature ◽  
Academic Research ◽  
Patient Data ◽  
Huge Amount ◽  
Health Records ◽  
First Impression ◽  
Series Of Experiments ◽  
Open Archives ◽  
Open Archives Initiative

In an era when medical literature is increasing daily, researchers in biomedical and clinical areas have joined efforts with language engineers to analyze large amount of biomedical and molecular biology literature (such as PubMed), patient data or health records. With such a huge amount of reports, evaluating their impact has long seized to be a trivial task. In this context, this paper intends to introduce a non-scientific factor that represents an important element in the effort of gaining acceptance of claims. Thus, we postulate that the confidence the author is expressing in his work plays an important role in shaping the first impression that influences the reader’s perception of the paper. The results discussed in this paper are based on a series of experiments ran over data from the Open Archives Initiative (OAI) corpus that provides interoperability standards in order to facilitate the effectiveness dissemination of the content. This method can be useful to the direct beneficiaries (authors, who are engaged in medical or academic research), but, also, researchers in the fields of BioNLP and NLP, etc.

scholarly journals Towards Identifying Author Confidence in Biomedical Articles

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 18 ◽  
Author(s):  
Mihaela Onofrei Plămadă ◽  
Diana Trandabăț ◽  
Daniela Gîfu
Keyword(s):  
Medical Literature ◽  
Academic Research ◽  
Biomedical Text Mining ◽  
Huge Amount ◽  
Health Records ◽  
Effective Dissemination ◽  
First Impression ◽  
Series Of Experiments ◽  
Using Data ◽  
Open Archives

In an era where the volume of medical literature is increasing daily, researchers in the biomedical and clinical areas have joined efforts with language engineers to analyze the large amount of biomedical and molecular biology literature (such as PubMed), patient data, or health records. With such a huge amount of reports, evaluating their impact has long stopped being a trivial task. In this context, this paper intended to introduce a non-scientific factor that represents an important element in gaining acceptance of claims. We postulated that the confidence that an author has in expressing their work plays an important role in shaping the first impression that influences the reader’s perception of the paper. The results discussed in this paper were based on a series of experiments that were ran using data from the open archives initiative (OAI) corpus, which provides interoperability standards to facilitate effective dissemination of the content. This method may be useful to the direct beneficiaries (i.e., authors, who are engaged in medical or academic research), but also, to the researchers in the fields of biomedical text mining (BioNLP) and NLP, etc.

FPGA-Based Accelerators for Bioinformatics Applications

2013 ◽  
pp. 605-635
Author(s):  
Alba Cristina Magalhaes Alves de Melo ◽  
Nahri Moreano
Keyword(s):  
Molecular Biology ◽  
Research Area ◽  
Sequence Profile ◽  
Huge Amount ◽  
Computational Tools ◽  
Computing Power ◽  
Open Questions ◽  
Similarities And Differences

The recent and astonishing advances in Molecular Biology, which led to the sequencing of an unprecedented number of genomes, including the human, would not have been possible without the help of Bioinformatics. Bioinformatics can be defined as a research area where computational tools and algorithms are developed to help biologists in the task of understanding the organisms. Some Bioinformatics applications, such as pairwise and sequence-profile comparison, require a huge amount of computing power and, therefore, are excellent candidates to run in FPGA platforms. This chapter discusses in detail several recent proposals on FPGA-based accelerators for these two Bioinformatics applications, highlighting the similarities and differences among them. At the end of the chapter, research tendencies and open questions are presented.

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

An XSLT Translator for the openEHR Archetype Definition Language

2017 ◽  
Author(s):  
John J. L. Chelsom
Keyword(s):  
Electronic Health Records ◽  
Patient Data ◽  
Specific Information ◽  
Health Record ◽  
Health Records ◽  
Information Models ◽  
Patient Health ◽  
Pipeline Processing ◽  
Open Standards ◽  
Iso 13606

The cityEHR Electronic Health Records system is a pure XML application for managing patient health records, using open standards. The structure of the health record follows the definition in the ISO 13606 standard, which is used in cityEHR as a basis for clinicians to develop specific information models for the patient data they gather for clinical and research purposes. In cityEHR these models are represented as OWL/XML ontologies. The most widely adopted approach to modelling patient data in accordance with ISO 13606 is openEHR, which uses its own Archetype Definition Language to specify the information models used in compliant health records systems. This paper describes a translator for the Archetype Definition Language, implemented using XSLT and XML pipeline processing, which generates OWL/XML suitable for use in cityEHR.

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