An Ontology-Enabled Approach for Modelling Business Processes

International audienceColoured Petri Nets (CPNs) have formal semantics and can describe any type of workflow system, behavioral and syntax wise simultaneously. They are widely studied and successfully applied in modelling of workflows and workflow systems. There is an inherent problem regarding business processes modelled with CPNs sharing and subsequently their reuse need to be considered. The Semantic Web technologies, such as ontologies, with their characteristics demonstrate that they can play an important role in this scenario. In this paper, we propose an ontological approach for representing business models in a meta-knowledge base. Firstly, the CPN ontology is defined to represent CPNs with OWL DL. Secondly, we introduce four basic types of manipulation operations on process models used to develop and modify business workflow patterns. To the best of our knowledge, representing business process definitions and business workflow patterns as knowledge based upon ontologies is a novel approach

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran. Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey. Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis. Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis. 2023 Dehdar, Salimifard, Mohammadi, Marzban, Saadatmand, Fararouei and Dianati-Nasab

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran. Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey. Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis. Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis. Copyright © 2023 Dehdar, Salimifard, Mohammadi, Marzban, Saadatmand, Fararouei and Dianati-Nasab

Comparing Petri Net and Activity Diagram Variants for Workflow Modelling:A Quest for Reactive Petri Nets

Petri net variants are widely used as a workflow modelling technique. Recently, UMLa ctivity diagrams have been used for the same purpose, even though the syntax and semantics of activity diagrams has not been yet fully worked out. Nevertheless, activity diagrams seem very similar to Petri nets and on the surface, one may think that they are variants of each other. To substantiate or deny this claim, we need to formalise the intended semantics of activity diagrams and then compare this with various Petri net semantics. In previous papers we have defined two formal semantics for UMLact ivity diagrams that are intended for workflow modelling. In this paper, we discuss the design choices that underlie these two semantics and investigate whether these design choices can be met in low-level and high-level Petri net semantics. We argue that the main difference between the Petri net semantics and our semantics of UML act ivity diagrams is that the Petri net semantics models resource usage of closed, active systems that are non-reactive, whereas our semantics of UMLact ivity diagrams models open, reactive systems. Since workflow systems are open, reactive systems, we conclude that Petri nets cannot model workflows accurately, unless they are extended with a syntax and semantics for reactivity

Carbon Footprints in Emergency Departments: A Simulation-Optimization Analysis

It is globally accepted to act against global warming through the reduction of carbon dioxide. Carbon footprint is historically defined as the total emissions caused by an individual, event, organization, or product, expressed as carbon dioxide equivalent. Healthcare system consumes large amount of energy in order to provide health services to patients who have to pass a series of treatment processes at each care unit. These treatments require different medical equipment that consume electrical power, and the more electrical power consumption is, the more greenhouse gases specifically CO2 emissions are. The discrete-event simulation has been applied to develop the model of the treatment process and the estimation of carbon dioxide in the treatment process. By the knowledge that the simulation is not an optimization method in itself, the OptQuest optimization method has been applied to reduce greenhouse gases and carbon footprint in the patients′ flow in the emergency department by considering leveling off the waiting time and length of stay as constraints to leveling up patient′s satisfaction. The numerical results provided by simulation and OptQuest show the efficiency of OptQuest as a technique for patient flow optimization