Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification

Zhu, Qiang; Jiang, Xingpeng; Zhu, Qing; Pan, Min; He, Tingting

doi:10.3389/fgene.2019.01182

Cited by 25 publications

(31 citation statements)

References 42 publications

(54 reference statements)

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“…Developing AI models in healthcare faces several legal regimes, such as federal regulations, state tort law, the Common Rule, and the Federal Trade Commission Act. In malpractice claims, owing to the use of AI black-box models in clinical workflows, the current legal system is not suitable [47,48]. Therefore, the responsibilities of different parties, including AI developers, the source of training data, clinicians, and suppliers who provide the AI system platform, must be clearly defined [1].…”

Section: Resultsmentioning

confidence: 99%

Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Davahli¹,

Karwowski²,

Fiok³

et al. 2021

Preprint

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In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

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Section: Resultsmentioning

confidence: 99%

Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Davahli¹,

Karwowski²,

Fiok³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The key first level dimensions of the SCS are as follows: (1) safety policies; 2 In malpractice claims, owing to the use of AI black-box models in clinical workflows, the current legal system is not suitable [56,57]. Therefore, the responsibilities of different parties, including AI developers, the source of training data, clinicians, and suppliers who provide the AI system platform, must be clearly defined [1].…”

Section: Resultsmentioning

confidence: 99%

A Safety Controlling System Framework for Implementing Artificial Intelligence in Healthcare

Davahli¹,

Karwowski²,

Fiok³

et al. 2020

Preprint

View full text Add to dashboard Cite

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to solve the AI black-box mystery in the healthcare industry. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first-level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

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“…For example, in Figure 4, a deep network with 3 hidden layers [49] using RapidMiner tool is illustrated. In a 3-layer network, low-level, middle-level and high-level or much more complex features are extracted in the first, second and third layers, respectively.…”

Section: ) Deep Learning Modelmentioning

confidence: 99%

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

et al. 2020

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One of the most common and critical destructive attacks on the victim system is the advanced persistent threat (APT)-attack. An APT attacker can achieve its hostile goal through obtaining information and gaining financial benefits from the infrastructure of a network. One of the solutions to detect a unanimous APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the system may crash due to the high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods of C5.0 decision tree, Bayesian network, and deep learning are used for the timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, a 10-fold cross-validation method is used to experiment with these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 95.64%, 88.37%, and 98.85%, respectively. Also, in terms of the critical criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 2.56, 10.47, and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false-negative rate, and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.

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Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification

Cited by 25 publications

References 42 publications

Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

A Safety Controlling System Framework for Implementing Artificial Intelligence in Healthcare

Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

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