scholarly journals Clinical Research Informatics: Supporting the Research Study Lifecycle

2017 ◽  
Vol 26 (01) ◽  
pp. 193-200 ◽  
Author(s):  
S. B. Johnson
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Search Strategy ◽  
Research Study ◽  
Computational Statistics ◽  
Medical Subject Headings ◽  
Pubmed Database ◽  
Subject Headings ◽  
Clinical Research Informatics ◽  
New Applications

Summary Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the “research study lifecycle”, with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

scholarly journals Clinical Research Informatics: Supporting the Research Study Lifecycle

2017 ◽  
Vol 26 (01) ◽  
pp. 193-200
Author(s):  
S. B. Johnson
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Search Strategy ◽  
Research Study ◽  
Computational Statistics ◽  
Medical Subject Headings ◽  
Pubmed Database ◽  
Subject Headings ◽  
Clinical Research Informatics ◽  
New Applications

Summary Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the “research study lifecycle”, with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

scholarly journals Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression

2015 ◽  
Vol 23 (3) ◽  
pp. 617-626 ◽  
Author(s):  
Nophar Geifman ◽  
Sanchita Bhattacharya ◽  
Atul J Butte
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Biomedical Literature ◽  
Cytokine Gene Expression ◽  
Future Research ◽  
Cytokine Gene ◽  
Medical Subject Headings ◽  
Expression Arrays ◽  
Gene Expression Arrays ◽  
Subject Headings

Abstract Objective Cytokines play a central role in both health and disease, modulating immune responses and acting as diagnostic markers and therapeutic targets. This work takes a systems-level approach for integration and examination of immune patterns, such as cytokine gene expression with information from biomedical literature, and applies it in the context of disease, with the objective of identifying potentially useful relationships and areas for future research. Results We present herein the integration and analysis of immune-related knowledge, namely, information derived from biomedical literature and gene expression arrays. Cytokine-disease associations were captured from over 2.4 million PubMed records, in the form of Medical Subject Headings descriptor co-occurrences, as well as from gene expression arrays. Clustering of cytokine-disease co-occurrences from biomedical literature is shown to reflect current medical knowledge as well as potentially novel relationships between diseases. A correlation analysis of cytokine gene expression in a variety of diseases revealed compelling relationships. Finally, a novel analysis comparing cytokine gene expression in different diseases to parallel associations captured from the biomedical literature was used to examine which associations are interesting for further investigation. Discussion We demonstrate the usefulness of capturing Medical Subject Headings descriptor co-occurrences from biomedical publications in the generation of valid and potentially useful hypotheses. Furthermore, integrating and comparing descriptor co-occurrences with gene expression data was shown to be useful in detecting new, potentially fruitful, and unaddressed areas of research. Conclusion Using integrated large-scale data captured from the scientific literature and experimental data, a better understanding of the immune mechanisms underlying disease can be achieved and applied to research.

scholarly journals ASSOCIATION OF DELIRIUM AND FRAGILITY IN HOSPITALIZED ELDERLY: SYSTEMATIC REVIEW

2021 ◽  
Author(s):  
Clovis Cechinel ◽  
◽  
Joao Alberto Martins Rodrigues
Keyword(s):  
Elderly People ◽  
Search Strategy ◽  
Information Sources ◽  
Web Of Science ◽  
Medical Subject Headings ◽  
Hospitalized Elderly ◽  
Subject Headings ◽  
Review Question ◽  
The Relationship ◽  
Specific Search

Review question / Objective: What is the relationship between delirium and frailty in hospitalized elderly people? The objective of this research is to analyze the association between frailty and delirium in hospitalized elderly people, through a systematic literature review. Condition being studied: Frailty and delirium in hospitalized aged. Information sources: A specific search strategy for the language of each database was developed using, initially, the Medical Subject Headings (MEsH) descriptor and later translated to specific descriptors (Descriptors in Health Sciences (DeCS) and Embase Subject Headings (Emtree)). The search strategy will be applied by the researchers in the MEDLINE databases through the Pubmed Portal; Scielo; VHL; EMBASE, CINAHL, Scopus and Web of Science through the CAPES Journal Portal; CENTRAL via Cochrane.

scholarly journals Using MeSH (Medical Subject Headings) to Enhance PubMed Search Strategies for Evidence-Based Practice in Physical Therapy

2012 ◽  
Vol 92 (1) ◽  
pp. 124-132 ◽  
Author(s):  
Randy R. Richter ◽  
Tricia M. Austin
Keyword(s):  
Search Strategy ◽  
Evidence Based Practice ◽  
Physical Therapists ◽  
Mesh Term ◽  
Evidence Based ◽  
Medical Subject Headings ◽  
Search Terms ◽  
Mesh Terms ◽  
Subject Headings ◽  
Pubmed Search

Background Evidence-based practice (EBP) is an important paradigm in health care. Physical therapists report lack of knowledge and time constraints as barriers to EBP. Objective The purpose of this technical report is to illustrate how Medical Subject Headings (MeSH), a controlled vocabulary thesaurus of indexing terms, is used to efficiently search MEDLINE, the largest component of PubMed. Using clinical questions, this report illustrates how search terms common to physical therapist practice do or do not map to appropriate MeSH terms. A PubMed search strategy that takes advantage of text words and MeSH terms is provided. Results A search of 139 terms and 13 acronyms was conducted to determine whether they appropriately mapped to a MeSH term. The search results were categorized into 1 of 5 outcomes. Nearly half (66/139) of the search terms mapped to an appropriate MeSH term (outcome 1). When a search term did not appropriately map to a MeSH term, it was entered into the MeSH database to search for an appropriate MeSH term. Twenty-one appropriate MeSH terms were found (outcomes 2 and 4), and there were 52 search terms for which an appropriate MeSH term was not found (outcomes 3 and 5). Nearly half of the acronyms did not map to an appropriate MeSH term, and an appropriate MeSH term was not found in the database. Limitations The results are based on a limited number of search terms and acronyms. Conclusions Understanding how search terms map to MeSH terms and using the PubMed search strategy can enable physical therapists to take full advantage of available MeSH terms and should result in more-efficient and better-informed searches.

scholarly journals Key Contributions in Clinical Research Informatics

2021 ◽  
Vol 30 (01) ◽  
pp. 233-238
Author(s):  
Christel Daniel ◽  
Ali Bellamine ◽  
Dipak Kalra ◽  
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Data Science ◽  
Adverse Drug Events ◽  
Quality Data ◽  
Generative Adversarial Networks ◽  
Editorial Team ◽  
Free Text ◽  
Clinical Research Informatics ◽  
Electronic Health

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020. Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers. Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records. Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.

Planning and Conducting a Large-Scale Mixed Methods Research Study: Research Team Experiences and Recommendations

2018 ◽  
Vol 10 (1) ◽  
pp. 283-295
Author(s):  
Katrin Niglas ◽  
◽  
Meril Ümarik ◽  
Maarja Tinn ◽  
Ivor Goodson ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Mixed Methods Research ◽  
Large Scale ◽  
Research Team ◽  
Research Study ◽  
Study Research

Fundamental study on clarification of brain molecular pathology of Nasu-Hakola disease

Impact ◽  
2019 ◽  
Vol 2019 (8) ◽  
pp. 24-26
Author(s):  
Jun-ichi Satoh
Keyword(s):  
Multiple Sclerosis ◽  
Human Brain ◽  
Clinical Research ◽  
Neurodegenerative Diseases ◽  
Molecular Pathology ◽  
Large Scale ◽  
Molecular Pathogenesis ◽  
Novel Therapies ◽  
Brain Pathology ◽  
Fundamental Study

Brain pathology expert Dr Jun-ichi Satoh, from the Department of Bioinformatics and Molecular Neuropathology of Meiji Pharmaceutical University in Tokyo, is drawing on his expertise on neurology and neuroimmunology to delve into some of the more complex diseases impacting the human brain. His knowledge and expertise have allowed him to direct his research interests to study neurodegenerative diseases, such as Alzheimer's disease (AD), and neuroinflammatory diseases, such as multiple sclerosis (MS), and the analysis of their molecular pathogenesis by using a bioinformatics approach. His current focus is on Nasu-Hakola disease (NHD), a disease whose rarity has posed significant barriers towards performing large-scale clinical research in order to understand what exactly causes this disease and develop effective novel therapies.

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