An effective method for the protection of user health topic privacy for health information services

Wu, Zongda; Liu, Huawen; Xie, Jian; Xu, Guandong; Li, Gang; Lu, Chenglang

doi:10.1007/s11280-023-01208-5

Cited by 11 publications

(2 citation statements)

References 50 publications

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“…However, cloud-based automation offers several advantages that must be weighed against the potential for data leakage—strategies for obviating security concerns while maintaining the flexibility, reliability, and accelerated deployment afforded by these tools are under development. A full discussion of such methods is beyond the scope of this paper, but can be further explored in recent works by Mei et al 53 and Wu et al 54 , among others.…”

Section: Discussionmentioning

confidence: 99%

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Heman-Ackah,

Blue,

Quimby

et al. 2024

Sci Rep

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Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients’ quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II–VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.

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Section: Discussionmentioning

confidence: 99%

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Heman-Ackah,

Blue,

Quimby

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This diagnostic test produces a highly detailed image of the interior constructions of the knee, which is then carefully examined by a radiologist or other trained medical professional 18 . By analyzing these images, the radiologist can identify any signs of damage to the ACL, including tears or other abnormalities 19 . This thorough examination helps to determine the extent of the injury, which is crucial for developing an effective treatment plan 20 .…”

Section: Introductionmentioning

confidence: 99%

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

Wang,

Yan

2024

Sci Rep

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In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions.

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A Blockchain PoW Consensus Mechanism Based on Edge Computing in the IoVs

Xu,

Ge,

Zhang

et al. 2024

Lecture Notes in Computer Science

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An effective method for the protection of user health topic privacy for health information services

Cited by 11 publications

References 50 publications

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

A Blockchain PoW Consensus Mechanism Based on Edge Computing in the IoVs

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