Medical Decision Support Tool from a Fuzzy-Rules Driven Bayesian Network

Zarikas, Vasilios; Papageorgiou, Elpiniki I.; Pernebayeva, Damira; Tursynbek, Nurislam

doi:10.5220/0006642705390549

Cited by 5 publications

(5 citation statements)

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“…Then regression methods could be applied to state the cause, which highly influences the accident occurrence, whereas the least ones will be excluded from the analysis [25]. Bayesian networks have already successfully applied in many disciplines i.e., medicine, environment problems, risk management/safety and reliability [2], [26], [27], [28], [29], [30], [31], [32]. Bayesian networks together with utility theory can also be used for finding optimal policies in many different fields [22], [26], [33], [34], [35], [36], [37], [38].…”

Section: Accidents Analysismentioning

confidence: 99%

Statistical and AI modeling of UK and France elevator accidents and their violating safety rules

Zarikas,

Zholdasbayeva,

Mitra

2023

J Appl Eng Science

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This study presents a statistical analysis applying different statistical techniques, including trained Bayesian Networks an artificial intelligence (AI) method, to explore datasets of lift accidents involving safety rules for two countries: UK and France. The study concerns six years data for both countries and covers almost all elevator accidents taken place during private and professional uses; 218 cases for UK and 205 cases for France. The relevant time interval for U.K. is 6th January 2006 to 29th December 2012, while for France data concern the period of 18th February 2003 to 17th December 2009. The major aim of the study is to exhibit and demonstrate that for accident datasets, at least for similar datasets, multiple statistical methods have to be applied in order to extract reliable information, i.e. investigate interactions among factors and therefore help to develop prevention measures. Three statistical models were built to derive associations between factors concerning violation of rules related to the installation and maintenance of elevators, passengers' safety rules, risks and unforeseen circumstances. Associations between severity of injury and categories of gender or age of injured people have been found. Furthermore, specific influences between severity of injury and categories of type of rules or of type of accident have been identified. The obtained results will contribute to the design of efficient methods to avoid future accidents in both countries.

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Section: Accidents Analysismentioning

confidence: 99%

Statistical and AI modeling of UK and France elevator accidents and their violating safety rules

Zarikas,

Zholdasbayeva,

Mitra

2023

J Appl Eng Science

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“…A Markov logic network has been implemented for diagnosis of medical conditions from Chinese-language medical records [11], but there was no assessment of its performance. The construction of a fuzzy Bayesian network for medical diagnosis has also been proposed but its performance not assessed [12].…”

Section: Review Of Relevant Studiesmentioning

confidence: 99%

Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence

Chary¹,

Boyer²,

Burns³

2021

Preprint

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Medical toxicology is the clinical specialty that treats the toxic effects of substances, for example, an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay current with it. The application of machine learning/artificial intelligence (ML/AI) techniques to medical toxicology is challenging because initial treatment decisions are often based on a few pieces of textual data and rely heavily on prior knowledge, experience, and expertise. ML/AI techniques, moreover, often do not represent knowledge in a way that is transparent for the physician, raising barriers to usability. Rule-based systems are more transparent approaches, but often generalize poorly and require expert curation to implement and maintain. Here, we construct a probabilistic logic network to represent a portion of the knowledge base of a medical toxicologist. Our approach transparently mimics the knowledge representation and clinical decision-making of practicing clinicians and requires minimal maintenance. The software, dubbed Tak, performs comparably to humans on straightforward cases and intermediate difficulty cases, but is outperformed by humans on challenging clinical cases. Tak outperforms a decision tree classifier at all levels of difficulty. Probabilistic logic provides one form of explainable artificial intelligence that may be acceptable for use in healthcare.

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“…A straightforward way to do inference in BNs is to use entire joint distribution and sum out all latent variables [42]. However, for large BNs this task can be very cumbersome, since the full joint probability table for n binary variables will consist of 2 n entries [43]. A simple yet powerful technique called Variable Elimination (VA) can be used in order to reduce the computational burden while conducting inference.…”

Section: A Variable Elimination Algorithmmentioning

confidence: 99%

Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

Bapin

Zarikas²

2019

ijacsa

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Implementation of the intelligent elevator control systems based on machine-learning algorithms should play an important role in our effort to improve the sustainability and convenience of multi-floor buildings. Traditional elevator control algorithms are not capable of operating efficiently in the presence of uncertainty caused by random flow of people. As opposed to conventional elevator control approach, the proposed algorithm utilizes the information about passenger group sizes and their waiting time, provided by the image acquisition and processing system. Next, this information is used by the probabilistic decision-making model to conduct Bayesian inference and update the variable parameters. The proposed algorithm utilizes the variable elimination technique to reduce the computational complexity associated with calculation of marginal and conditional probabilities, and Expectation-Maximization algorithm to ensure the completeness of the data sets. The proposed algorithm was evaluated by assessing the correspondence level of the resulting decisions with expected ones. Significant improvement in correspondence level was obtained by adjusting the probability distributions of the variables affecting the decision-making process. The aim was to construct a decision engine capable to control the elevators actions, in way that improves user's satisfaction. Both sensitivity analysis and evaluation study of the implemented model, according to several scenarios, are presented. The overall algorithm proved to exhibit the desired behavior, in 94% case of the scenarios tested.

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Medical Decision Support Tool from a Fuzzy-Rules Driven Bayesian Network

Cited by 5 publications

References 0 publications

Statistical and AI modeling of UK and France elevator accidents and their violating safety rules

Statistical and AI modeling of UK and France elevator accidents and their violating safety rules

Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence

Smart Building’s Elevator with Intelligent Control Algorithm based on Bayesian Networks

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