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Yearbook of Medical Informatics Aug 2018To summarize the recent public and population health informatics literature with a focus on the synergistic "bridging" of electronic data to benefit communities and... (Review)
Review
OBJECTIVE
To summarize the recent public and population health informatics literature with a focus on the synergistic "bridging" of electronic data to benefit communities and other populations.
METHODS
The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords "public health informatics" and "social determinants of health". The "social determinants of health" search was refined to include articles that contained the keywords "public health", "population health" or "surveillance".
RESULTS
Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health.
CONCLUSIONS
Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this "bridging" is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.
Topics: Datasets as Topic; Humans; Medical Informatics; Population Health; Public Health Informatics; Social Determinants of Health; Telemedicine; United States; Workforce
PubMed: 30157524
DOI: 10.1055/s-0038-1667081 -
Yearbook of Medical Informatics May 2014To provide an editorial introduction into the 2014 IMIA Yearbook of Medical Informatics with an overview of the content, the new publishing scheme, and upcoming 25th...
OBJECTIVES
To provide an editorial introduction into the 2014 IMIA Yearbook of Medical Informatics with an overview of the content, the new publishing scheme, and upcoming 25th anniversary.
METHODS
A brief overview of the 2014 special topic, Big Data - Smart Health Strategies, and an outline of the novel publishing model is provided in conjunction with a call for proposals to celebrate the 25th anniversary of the Yearbook.
RESULTS
'Big Data' has become the latest buzzword in informatics and promise new approaches and interventions that can improve health, well-being, and quality of life. This edition of the Yearbook acknowledges the fact that we just started to explore the opportunities that 'Big Data' will bring. However, it will become apparent to the reader that its pervasive nature has invaded all aspects of biomedical informatics - some to a higher degree than others. It was our goal to provide a comprehensive view at the state of 'Big Data' today, explore its strengths and weaknesses, as well as its risks, discuss emerging trends, tools, and applications, and stimulate the development of the field through the aggregation of excellent survey papers and working group contributions to the topic.
CONCLUSIONS
For the first time in history will the IMIA Yearbook be published in an open access online format allowing a broader readership especially in resource poor countries. For the first time, thanks to the online format, will the IMIA Yearbook be published twice in the year, with two different tracks of papers. We anticipate that the important role of the IMIA yearbook will further increase with these changes just in time for its 25th anniversary in 2016.
Topics: Anniversaries and Special Events; Data Mining; Databases, Factual; Medical Informatics; Periodicals as Topic
PubMed: 24853037
DOI: 10.15265/IY-2014-0030 -
Journal of Integrative Bioinformatics May 2018This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and...
This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various - omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.
Topics: Computational Biology; Data Mining; Databases, Factual; Delivery of Health Care; Electronic Health Records; Genomics; Humans; Medical Informatics; Metabolomics; Population Surveillance
PubMed: 29746254
DOI: 10.1515/jib-2017-0030 -
Database : the Journal of Biological... Jun 2022There are >2500 different genetically determined developmental disorders (DD), which, as a group, show very high levels of both locus and allelic heterogeneity. This has...
There are >2500 different genetically determined developmental disorders (DD), which, as a group, show very high levels of both locus and allelic heterogeneity. This has led to the wide-spread use of evidence-based filtering of genome-wide sequence data as a diagnostic tool in DD. Determining whether the association of a filtered variant at a specific locus is a plausible explanation of the phenotype in the proband is crucial and commonly requires extensive manual literature review by both clinical scientists and clinicians. Access to a database of weighted clinical features extracted from rigorously curated literature would increase the efficiency of this process and facilitate the development of robust phenotypic similarity metrics. However, given the large and rapidly increasing volume of published information, conventional biocuration approaches are becoming impractical. Here, we present a scalable, automated method for the extraction of categorical phenotypic descriptors from the full-text literature. Papers identified through literature review were downloaded and parsed using the Cadmus custom retrieval package. Human Phenotype Ontology terms were extracted using MetaMap, with 76-84% precision and 65-73% recall. Mean terms per paper increased from 9 in title + abstract, to 68 using full text. We demonstrate that these literature-derived disease models plausibly reflect true disease expressivity more accurately than widely used manually curated models, through comparison with prospectively gathered data from the Deciphering Developmental Disorders study. The area under the curve for receiver operating characteristic (ROC) curves increased by 5-10% through the use of literature-derived models. This work shows that scalable automated literature curation increases performance and adds weight to the need for this strategy to be integrated into informatic variant analysis pipelines. Database URL: https://doi.org/10.1093/database/baac038.
Topics: Child; Data Mining; Databases, Factual; Developmental Disabilities; Humans; Publications; ROC Curve
PubMed: 35670729
DOI: 10.1093/database/baac038 -
Journal of the American Medical... Feb 2020
Topics: Consumer Health Informatics; Consumer Health Information; Humans; Medical Informatics
PubMed: 31972023
DOI: 10.1093/jamia/ocz224 -
Yearbook of Medical Informatics Aug 2017To summarize current research in the field of Public Health and Epidemiology Informatics. : The complete 2016 literature concerning public health and epidemiology... (Review)
Review
To summarize current research in the field of Public Health and Epidemiology Informatics. : The complete 2016 literature concerning public health and epidemiology informatics has been searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the editorial team an enlightened selection of the best papers. : Among the 829 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one compares Google, Twitter, and Wikipedia as tools for Influenza surveillance. The second paper presents a Geographic Knowledge-Based Model for mapping suitable areas for Rift Valley fever transmission in Eastern Africa. The last paper evaluates the factors associated with the visit of Facebook pages devoted to Public Health Communication. Surveillance is still a productive topic in public health informatics but other very important topics in public health are appearing.
Topics: Epidemiology; Humans; Medical Informatics; Population Surveillance; Public Health Informatics
PubMed: 29063574
DOI: 10.15265/IY-2017-036 -
International Journal of Environmental... Sep 2022The term Big Data is used to describe extremely large datasets that are complex, multi-dimensional, unstructured, and heterogeneous and that are accumulating rapidly and...
The term Big Data is used to describe extremely large datasets that are complex, multi-dimensional, unstructured, and heterogeneous and that are accumulating rapidly and may be analyzed with appropriate informatic and statistical methodologies to reveal patterns, trends, and associations [...].
Topics: Big Data; Data Science; Delivery of Health Care; Health Facilities
PubMed: 36141962
DOI: 10.3390/ijerph191811677 -
Journal of the American Medical... May 2023
Topics: Public Health; Data Science; Medical Informatics; Public Health Informatics
PubMed: 37205729
DOI: 10.1093/jamia/ocad076 -
Yearbook of Medical Informatics Aug 2020To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2019. (Review)
Review
OBJECTIVE
To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2019.
METHODS
A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2019 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two editors led to a total of 30 papers. These papers were discussed for a selection of 15 finalist papers, which were then reviewed by the two editors and by three external reviewers from internationally renowned research teams.
RESULTS
The query process resulted in 626 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors.
CONCLUSION
The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.
Topics: Ambulatory Care; Decision Making, Computer-Assisted; Ergonomics; Health Information Systems; Humans; Medical Errors; Medical Informatics; Patient Safety; User-Computer Interface
PubMed: 32823303
DOI: 10.1055/s-0040-1702012 -
IEEE Journal of Biomedical and Health... Jul 2015This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data... (Review)
Review
This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.
Topics: Computational Biology; Databases, Factual; Diagnostic Imaging; Humans; Medical Informatics
PubMed: 26173222
DOI: 10.1109/JBHI.2015.2450362