Research on Data Mining Application of Orthopedic Rehabilitation Information for Smart Medical

Ye, Mao; Zhang, Hangzhou; Li, Li

doi:10.1109/access.2019.2957579

Cited by 14 publications

(8 citation statements)

References 35 publications

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“…[ 12 , 13 ] As for individual researcher, the most productive author is SHAN NH, who based at the Stanford Center for Biomedical Informatics Research (BMIR) and studies pharmacovigilance and text mining, [ 14 ] then followed by LI L, who based at the Icahn School of Medicine at Mount Sinai and studies systems biology and machine learning. [ 15 ] HU YH, based at the National Chung Cheng University, Taiwan, and studies large scale medical data preprocessing approach. [ 16 ] WANG S, based at the University of Queensland, Australia, and studies medical database management.…”

Section: Resultsmentioning

confidence: 99%

A bibliometric analysis and visualization of medical data mining research

Cheng

et al. 2020

Medicine

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Background: Data mining technology used in the field of medicine has been widely studied by scholars all over the world. But there is little research on medical data mining (MDM) from the perspectives of bibliometrics and visualization, and the research topics and development trends in this field are still unclear. Methods: This paper has applied bibliometric visualization software tools, VOSviewer 1.6.10 and CiteSpace V, to study the citation characteristics, international cooperation, author cooperation, and geographical distribution of the MDM. Results: A total of 1575 documents are obtained, and the most frequent document type is article (1376). SHAN NH is the most productive author, with the highest number of publications of 12, and the Gillies's article (750 times citation) is the most cited paper. The most productive country and institution in MDM is the USA (559) and US FDA (35), respectively. The Journal of Biomedical Informatics, Expert Systems with Applications and Journal of Medical Systems are the most productive journals, which reflected the nature of the research, and keywords “classification (790)” and “system (576)” have the strongest strength. The hot topics in MDM are drug discovery, medical imaging, vaccine safety, and so on. The 3 frontier topics are reporting system, precision medicine, and inflammation, and would be the foci of future research. Conclusion: The present study provides a panoramic view of data mining methods applied in medicine by visualization and bibliometrics. Analysis of authors, journals, institutions, and countries could provide reference for researchers who are fresh to the field in different ways. Researchers may also consider the emerging trends when deciding the direction of their study.

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Section: Resultsmentioning

confidence: 99%

A bibliometric analysis and visualization of medical data mining research

Cheng

et al. 2020

Medicine

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“…According to the study, it says that irrespective of challenges associated with the deep learning mechanism, it is still the best solution. Nearly, similar category of the review work has also being carried out by Ye et al [16] which also concludes that adoption of artificial intelligence is one of the best option for analysis of disease condition from medical data. A unique implementation of the medical data has been also reported to be used in preserving privacy factor associated with it.…”

Section: Related Workmentioning

confidence: 80%

Modeling Method for Leveraging Data Quality in Healthcare Big Data

K.*,

Ramesh

2020

IJITEE

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An accurate diagnosis of the healthcare-based Big Data will always demand a significant level of quality in its input data itself, which is a serious level of concern in the area of healthcare analytics. Review of existing approaches shows that there has been various learning-based approaches being used for disease diagnosis which often ignores various issues viz. data aggregation, presence of error prone data, accuracy etc. Therefore, this paper presents a novel framework which offers cost effective modeling of the aggregation process of healthcare-big data followed by facilitating solution towards identifying and rectifying all the positions within a database system where there are presence of an error. The proposed system offer a mechanism where the error-prone data has been identified and substituted with data of better quality in order to offer better analytical outcomes. The study offers a strong baseline in order to leverage the data quality in healthcare big data.

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“…Data mining can be understood as extracting knowledge and information from a large amount of data. The approach comprised analyzing the data, discovering patterns within the data, and determining how the novel patterns may be applied to forecasting and planning in the future [13]. Data mining requires human interpretation to make a decision.…”

Section: Distribution Of Data Mining Techniquementioning

confidence: 99%

Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

et al. 2023

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Data mining has been widely used in healthcare to provide treatment and care recommendations based on a collective prediction of individual conditions. For rehabilitation, various data mining techniques have been applied to predict and recommend suitable recovery paths and training. Also, the gamification concept was applied to rehabilitation training to motivate the patient to follow the training until the end. Researchers have conducted considerable research to investigate the validity and effectiveness of those techniques on massive patient data on specific conditions and treatment contexts. However, it is still unclear how to effectively offer customized rehabilitation training to stroke patients using gamification and data mining approaches. Thus, to understand how researchers studied them, we examined 34 peer-reviewed articles published in computer science and medical proceedings and journals between 2012 and 2022. We systematically reviewed the data mining and gamification techniques researchers had applied for post-stroke rehabilitation and related prediction models resulting from the data mining processes. As a result of the analyses, three significant contributions are identified. This article 1) identifies trends in data mining and gamification used in personalized post-stroke rehabilitation training; 2) maps trends in the study of data mining and gamification in post-stroke rehabilitation; and 3) identifies underexplored studies for future work. There is a definite need to continue developing and researching intervention strategies related to rehabilitation to address recovery problems by providing accuracy and protection of healthcare, as well as incorporating components that promote patients' motivation and engagement.

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Research on Data Mining Application of Orthopedic Rehabilitation Information for Smart Medical

Cited by 14 publications

References 35 publications

A bibliometric analysis and visualization of medical data mining research

A bibliometric analysis and visualization of medical data mining research

Modeling Method for Leveraging Data Quality in Healthcare Big Data

Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

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