Impact of big data resources on clinicians’ activation of prior medical knowledge

Wang, Su Fen; Yuan, Junyi; Pan, Changqing

doi:10.1016/j.heliyon.2022.e10312

Cited by 4 publications

(18 citation statements)

References 72 publications

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“…The substantial accumulation of omics data through data collection necessitates urgent integration and analysis. However, big data itself also has many limitations, including but not limited to sample size, selection bias, and missing values. ,, Sometimes the sample size is insufficient to compare the significance with the number of attributes. In addition, any data set is a selection of data, and the selection bias and missing values remain when the sample size is large enough.…”

Section: “Big Data” Collectionmentioning

confidence: 99%

“…The collection of big data is an important proposition in disease diagnosis. , To date, a large number of analytical chemistry and medical studies using standard tests or sensors demonstrate a wide range of disease diagnosis-related data sets. ,, In addition, international medical research institutions have also made a large investment in the field of big data for disease diagnosis and built multiple public platforms to provide big data services for users . Data sets from different research fields and institutions have broad diversity in data type, dimensionality, and sample size, thus adapting to different application scenarios.…”

Section: “Big Data” Collectionmentioning

confidence: 99%

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Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Lu,

Yang,

et al. 2024

ACS Sens.

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Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

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Section: “Big Data” Collectionmentioning

confidence: 99%

Section: “Big Data” Collectionmentioning

confidence: 99%

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Lu,

Yang,

et al. 2024

ACS Sens.

View full text Add to dashboard Cite

show abstract

“…The diagnosis and treatment ability of medical personnel is the foundation for ensuring the high quality of medical services. Wang et al proposed that whether medical personnel have the ability to recall relevant medical knowledge in a timely manner during the actual complex diagnosis and treatment process it is the key to ensuring their diagnosis and treatment ability [ 1 ]. Baeninger et al showed that the minimum knowledge of keratoconus expected by corneal experts by Swiss general ophthalmologists directly leads to the number of cases of keratoconus diagnosed by ophthalmologists.…”

Section: Introductionmentioning

confidence: 99%

“…Big data technology, medical big data, various policies and regulations are integrated into medical big data resources from both the organizational and public levels [ 10 , 11 ], affecting and changing the learning and diagnosis environment of medical personnel. Wang et al (2022) has explored the impact of medical big data resources on clinicians to recall the prior knowledge, and shown that big data technology, big data itself and big data service at the public level and institutional level interact and influence each other to activate prior medical knowledge [ 1 ]. However, it remains unclear how medical big data resources can be effectively utilized and how they may impact clinicians in two scenarios: when using remote consultation platforms, and when not utilizing any remote platform to access prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform

Yuan,

Mi,

Wang

et al. 2023

BMC Med Educ

Self Cite

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Background This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms. Method This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels—medical institutional level and public level—and three types, namely data, technology, and services. The data are analyzed using SmartPLS2. Results (1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways. Conclusions The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.

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Research on performance evaluation and optimization of college budget management under the background of big data

Zhao

Zhou³

2023

Applied Mathematics and Nonlinear Sciences

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At present, due to the limitations of technology and resources, the comprehensive budget management generally has problems such as imperfect foundation of establishment, weak binding force of execution, and lack of performance appraisal system. These problems become more prominent in the context of the era of big data. The use of big data technology, such as large capacity, real-time control, intelligent Settings and other characteristics, will be able to improve the current many problems. Therefore, this paper focuses on how to combine big data with comprehensive budget management. This paper holds that it is not enough for comprehensive budget management to stay in the current stage, but also need to use big data technology, which is a higher level of information stage. Therefore, this paper adopts the method of double subordination fuzzy support vector machine to study the effect of budget management of scientific research projects in Chinese universities. Based on the construction of comprehensive budget management performance evaluation index system throughout the whole process of college budget cycle, taking S university as an example, this paper analyzes the performance of comprehensive budget management in S university, and points out that big data can optimize and innovate the budget management mode, which has great significance for college budget management.

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Impact of big data resources on clinicians’ activation of prior medical knowledge

Cited by 4 publications

References 72 publications

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform

Research on performance evaluation and optimization of college budget management under the background of big data

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