An Approach to Combining Medical Device Fault Analysis with Trusted Computing Forensics

Oliver, Ian

doi:10.1109/bigdata50022.2020.9378272

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Most of those who use medical devices are clinical healthcare workers, so those in control of their quality should actively communicate with clinicians to understand the potential problems that exist in their use and put forward certain adjustment suggestions [12]. At the same time, it is necessary to establish a perfect quality of medical equipment use rules and regulations, requiring that each contact with the use of medical equipment can follow the rules and regulations to reduce the adverse events of medical devices caused by human factors [13]. Routine and regular maintenance of equipment can prolong the service life of hospital equipment.…”

Section: Introductionmentioning

confidence: 99%

Random Forest and LightGBM-Based Human Health Check for Medical Device Fault Detection

Wang

2022

Journal of Healthcare Engineering

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Medical devices are items used directly or indirectly in the human body and are a prerequisite for hospital treatment of patients, and their quality can have a direct impact on the health of patients, so strengthening the quality control of medical device use is a hot spot of concern in the clinic. Current medical device testing can reduce the occurrence of adverse events, but it cannot be completely avoided, and its work still needs to be further strengthened. In this paper, we design a two-way feature selection algorithm based on PSO_RF. We use random forest to calculate the importance of the feature attributes of the sample data and sort the results in descending order, where a particle swarm algorithm is introduced to optimize the parameters of the random forest algorithm. The 245 medical device adverse event reports received by the testing center were selected, the occurrence and types of adverse events were analyzed retrospectively, and quality control countermeasures for medical device use were formulated.

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Section: Introductionmentioning

confidence: 99%

Random Forest and LightGBM-Based Human Health Check for Medical Device Fault Detection

Wang

2022

Journal of Healthcare Engineering

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“…115 Oliver uses the trusted computing forensics to detect faults in digitalized medical devices. 116 Emakhu et al analyze user complaint data from 2016-18 USFDA report and use neural networks to investigate that two main causes of faults of software-related medical devices are control flow faults and integration faults. 117 Lakkamraju et al use artificial intelligence approaches in data analysis to identify the potential software and mechanical faults of medical systems for cardiac health.…”

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

Risk management of smart healthcare systems: Delimitation, state-of-arts, process, and perspectives

Liu

2022

Journal of Patient Safety and Risk Management

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Sensing, communication, computation, and control technologies are facilitating smart healthcare to improve efficiency and effectiveness of medical treatment and care. This study focuses on the risk issues relevant with the adverse events where novel technical systems do not serve as expected. We discuss the unique challenges, define the scope of risk management in healthcare and review the state-of-art research on diverse topics under the framework widely used in risk management. Then, we present a systematic approach to identify the hazards to patients and other asset of interest in the perception, cyber communication, and execution of smart technologies and their operational contexts. We also investigate different methods for scenario, likelihood, and consequence analyses for specifying the risks of adverse events, and categorize the approaches of risk reduction, as the main strategy of treating risks of smart healthcare systems, into four groups of design, operation, organization, and legislation. At the last, the article proposes some research perspectives responding to the developing trend of smart healthcare.

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Trust, Security and Privacy through Remote Attestation in 5G and 6G Systems

Oliver

2021

2021 IEEE 4th 5G World Forum (5GWF)

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An Approach to Combining Medical Device Fault Analysis with Trusted Computing Forensics

Cited by 3 publications

References 20 publications

Random Forest and LightGBM-Based Human Health Check for Medical Device Fault Detection

Random Forest and LightGBM-Based Human Health Check for Medical Device Fault Detection

Risk management of smart healthcare systems: Delimitation, state-of-arts, process, and perspectives

Trust, Security and Privacy through Remote Attestation in 5G and 6G Systems

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