How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective

Wu, Zongda; Xuan, Shaolong; Xie, Jian; Lin, Chongze; Lu, Chenglang

doi:10.1016/j.compbiomed.2022.105726

Cited by 94 publications

(23 citation statements)

References 64 publications

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“…In recent years, artificial intelligence has played an important role in the medical field, such as coronavirus disease 2019 (COVID-19) diagnosis [ 4 ], detection of gastrointestinal polyps [ 5 ], retinal vessel segmentation [ 6 ], image diagnosis of lung cancer [ 7 ], diagnosis of atrophic gastritis [ 8 ], and confidentiality management of electronic medical records on the cloud [ 9 ]. Machine learning, a form of artificial intelligence, combined with medical big data can create algorithms that rival those of human doctors [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

Xiong

Zhao

et al. 2022

Computational and Mathematical Methods in Medicine

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Purpose. Surgical site infection is one of the serious complications after lumbar fusion. Early prediction and timely intervention can reduce the harm to patients. The aims of this study were to construct and validate a machine learning model for predicting surgical site infection after posterior lumbar interbody fusion, to screen out the most important risk factors for surgical site infection, and to explore whether synthetic minority oversampling technique could improve the model performance. Method. This study reviewed 584 patients who underwent posterior lumbar interbody fusion for degenerative lumbar disease at our center from January 2019 to August 2021. Clinical information and laboratory test data were collected from the electronic medical records. The original dataset was divided into training set and validation set in a 1 : 1 ratio. Seven machine learning algorithms were used to develop predictive models; the training set of each model was resampled using synthetic minority oversampling technique. Finally, the model performance was assessed in the validation set. Results. Of the 584 patients, 33 (5.65%) occurred surgical site infection. Stepwise logistic regression showed that preoperative albumin level (OR 0.659, 95% CI 0.563-0.756), diabetes (OR 9.129, 95% CI 3.816-23.126), intraoperative dural tear (OR 8.436, 95% CI 2.729-25.334), and rheumatic disease (OR 8.471, 95% CI 1.743-39.567) were significant predictors associated with surgical site infection. The performance of the AdaBoost Classification Trees model was the best among the seven machine learning models, and synthetic minority oversampling technique improved the performance of all models. Conclusion. The prediction model we constructed based on machine learning and synthetic minority oversampling technique can accurately predict surgical site infection, which is conducive to clinical decision-making and optimization of perioperative management.

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Section: Introductionmentioning

confidence: 99%

Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

Xiong

Zhao

et al. 2022

Computational and Mathematical Methods in Medicine

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“…In conclusion, SRWPSO has better computational efficiency than the new peer variants. The SRWPSO and the future improved PSO can be applied in different fields, such as human activity recognition ( Qiu et al, 2022 ), dynamic module detection ( Ma et al, 2020 ; Li D. et al, 2021 ), recommender system ( Li et al, 2014 , 2017 ), smart contract vulnerability detection ( Zhang L. et al, 2022 ), privacy protection of electronic medical records ( Wu et al, 2022 ), named entity recognition ( Yang Z. et al, 2022 ), structured sparsity optimization ( Zhang X. et al, 2022 ), microgrids planning ( Cao et al, 2021c ), location-based services ( Wu et al, 2020 ; Wu Z. et al, 2021 ), disease prediction ( Su et al, 2019 ; Li L. et al, 2021 ), medical data processing ( Guo et al, 2022 ), drug discovery ( Zhu et al, 2018 ; Li Y. et al, 2020 ), and object tracking ( Zhang et al, 2015 ).…”

Section: Benchmark Function Validationmentioning

confidence: 99%

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Zhao²,

Xu³

et al. 2023

Front. Neuroinform.

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IntroductionAtopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians’ subjective judgment, which may be missed or misdiagnosed sometimes.MethodsThis paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population’s diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.ResultsTo prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.DiscussionThe 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

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“…Patient medical records have progressed from paper to electronic data integrity, allowing them to be kept safe, accessed, and legally authorized by only valid medical centers [1]. The amount of available health care data is rapidly increasing as medical information technology advances, this can aid individuals in disease prevention, increase the likelihood that they will be cured, give guidelines for hospitals and pharmaceutical businesses, and serve as proof in situations involving medical-legal culpability and medical conflicts.…”

Section: Introductionmentioning

confidence: 99%

A survey: medical health record data security based on interplanetary file system and blockchain technologies

Al-Kaabi

Abdullah

2023

IJEECS

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The adoption of modern health records is growing more mature, yet security issues always accompany it. Interplanetary file system (IPFS) and blockchain are developing technologies with decentralization, distributed fault tolerance, and trustworthiness. Using IPFS and blockchain technology to tackle medical health record data security issues is a very promising trend, and it is presently being utilized to secure medical health record data security. This article first explains the idea of IPFS and highlights the classification of existing IPFS and blockchain techniques before briefly discussing distributed ledger to tackle the existing medical health record data security challenges and faults. Finally, to preserve medical health records, a new medical health record storage architectural model based on IPFS and blockchain technologies is presented.

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How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective

Cited by 94 publications

References 64 publications

Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

A survey: medical health record data security based on interplanetary file system and blockchain technologies

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