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Journal of Evidence-based Medicine Feb 2020Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression... (Review)
Review
Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.
Topics: Big Data; Data Management; Data Mining; Databases, Factual; Software
PubMed: 32086994
DOI: 10.1111/jebm.12373 -
Indian Journal of Dermatology,... 2021
Topics: Data Management; Dermatology; Humans; Periodicals as Topic
PubMed: 34672477
DOI: 10.25259/IJDVL_989_2021 -
The American Journal of Medicine Dec 2022
Topics: Humans; Clinical Reasoning; Data Management
PubMed: 36063858
DOI: 10.1016/j.amjmed.2022.08.022 -
Proceedings of the National Academy of... Mar 2020Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is...
Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.
Topics: Data Management; Database Management Systems; Expert Systems; Machine Learning; Medical Informatics
PubMed: 32071251
DOI: 10.1073/pnas.1906831117 -
Medical Reference Services Quarterly 2023Spurred by the National Institute of Health mandating a data management and sharing plan as a requirement of grant funding, research data management has exploded in...
Spurred by the National Institute of Health mandating a data management and sharing plan as a requirement of grant funding, research data management has exploded in importance for librarians supporting researchers and research institutions. This editorial examines the role and direction of libraries in this process from several viewpoints. Key markers of success include collaboration, establishing new relationships, leveraging existing relationships, accessing multiple avenues of communication, and building niche expertise and cachè as a valued and trustworthy partner.
Topics: Humans; Data Management; Libraries, Medical; Communication; Librarians; Research Personnel
PubMed: 37459491
DOI: 10.1080/02763869.2023.2218776 -
Sensors (Basel, Switzerland) Jul 2022Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare... (Review)
Review
Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain-cloud services being offered by existing Cloud Service providers.
Topics: Blockchain; Cloud Computing; Computer Security; Data Management; Technology
PubMed: 35890918
DOI: 10.3390/s22145238 -
The Journal of Allergy and Clinical... Feb 2020
Topics: Data Management; Drug Hypersensitivity Syndrome; Humans
PubMed: 32037119
DOI: 10.1016/j.jaip.2019.10.051 -
Anesthesiology Mar 2019
Topics: Acetaminophen; Administration, Intravenous; Analgesics, Opioid; Colectomy; Data Management
PubMed: 30762645
DOI: 10.1097/ALN.0000000000002570 -
Methods in Molecular Biology (Clifton,... 2022With the evermore emphasis put on open science and its invaluable benefits to the scientific community, it is no longer the case where a research project simply ends...
With the evermore emphasis put on open science and its invaluable benefits to the scientific community, it is no longer the case where a research project simply ends with a scientific publication. The benefits of data sharing and reproducibility of results have taken the centerpiece within the life science research supported by FAIR principles that firmly underline the importance of open data. The current data-intensive multidisciplinary research has also highlighted the significance of how data is mined and managed. Here we describe some of the features adopted by EMBL-EBI data resources to support data mining, data quality, and data management. We also highlight how EMBL-EBI has responded to the current pandemic through its data resources.
Topics: Biological Science Disciplines; Data Management; Data Mining; Information Dissemination; Reproducibility of Results
PubMed: 35507257
DOI: 10.1007/978-1-0716-2095-3_1 -
The Journal of Urology Jun 2020
Topics: Cytoreduction Surgical Procedures; Data Management; Humans; Kidney; Kidney Neoplasms; Registries
PubMed: 32208967
DOI: 10.1097/JU.0000000000000741.01