A Stochastic Model for Prioritized Outpatient Scheduling in a Radiology Center

Abtahi, Zeinab; Sahraeian, Rashed; Rahmani, Donya

doi:10.5829/ije.2020.33.04a.11

Cited by 6 publications

(4 citation statements)

References 22 publications

(38 reference statements)

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“…Rashidifar et al [35] also presented a mathematical model for cloud-based scheduling using heavy traffic limit theorem in queuing process. Abtahi et al [36] studied on stochastic model for the prioritized outpatient scheduling in a radiology center for the purose of effective and efficient services given to patients. Yazdi et al [37] conducted an investigtaion by using a mathematical model for scheduling elective surgeries in order to minimize the waiting list in the emergency surgeries.…”

Section: Related Workmentioning

confidence: 99%

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Fazeli

Farjami

Bidgoly

2023

IJE

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Cloud manufacturing (CMfg) is a new advanced manucatring model developed with the help of enterprise information technologies under the support of cloud computing, Internet of Things and service-based technologies. CMfg compose multiple manufacturing resources to provide efficient and valuable services. CMfg has a highly dynamic environment. In this environment, many disruptions or events may occur that lead the system to unplanned situations. In CMfg, a series of service providers are scheduled for production. During the production operation, some of them may be damaged, stopped, and out of service. Therefore, rescheduling is necessary for the continuation of the production process according to the concluded contracts and initial schedule. When any disruptions or other events occurred, the rescheduling techniques used to updating the inital schedule. In this paper, the dynamic rescheduling problem in CMfg is analyzed. Then the multi-objective rescheduling in CMfg is modeled and defined as a multi-objective optimization problem. Defining this problem as a multi-objective optimization problem provides the possibility of applying, checking and comparing different algorithms. For solving this problem, previous optimization methods have improved and a multi-objective and elitist algorithm based on the Jaya algorithm, called advanced multi-objective elitist Jaya algorithm (AMEJ) is proposed. Several experiments have been conducted to verify the performance of the proposed algorithm. Computational results showed that the proposed algorithm performs better compared to other multiobjective optimization algorithms.

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Section: Related Workmentioning

confidence: 99%

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Fazeli

Farjami

Bidgoly

2023

IJE

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“…The increasing demand for accurate and timely radiology reports, coupled with the challenges radiologists face in examining medical images and creating diagnostic notes, has led to burnout, errors, and delays in providing care [11]. While experts have turned to artificial intelligence and deep learning (DL) technologies to automate the generation of radiology reports, implementing and adopting these technologies, face several challenges [12,13].…”

Section: Problem Statement and Contributionmentioning

confidence: 99%

Cross-modal Deep Learning-based Clinical Recommendation System for Radiology Report Generation from Chest X-rays

Shetty

Ananthanarayana

Mahale

2023

IJE

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Radiology report generation is a critical task for radiologists, and automating the process can significantly simplify their workload. However, creating accurate and reliable radiology reports requires radiologists to have sufficient experience and time to review medical images. Unfortunately, many radiology reports end with ambiguous conclusions, resulting in additional testing and diagnostic procedures for patients. To address this, we proposed an encoder-decoder-based deep learning framework that utilizes chest X-ray images to produce diagnostic radiology reports. In our study, we have introduced a novel text modelling and visual feature extraction strategy as part of our proposed encoder-decoder-based deep learning framework. Our approach aims to extract essential visual and textual information from chest X-ray images to generate more accurate and reliable radiology reports. Additionally, we have developed a dynamic web portal that accepts chest X-rays as input and generates a radiology report as output. We conducted an extensive analysis of our model and compared its performance with other state-of-the-art deep learning approaches. Our findings indicate significant improvement achieved by our proposed model compared to existing models, as evidenced by the higher BLEU scores (BLEU1 = 0.588, BLEU2 = 0.4325, BLEU3 = 0.4017, BLEU4 = 0.3860) attained on the Indiana University Dataset. These results underscore the potential of our deep learning framework to enhance the accuracy and reliability of radiology reports, leading to more efficient and effective medical treatment.

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“…In addition, this study deals with an integer model with definitive data for this problem and evaluates the model efficiency under different conditions and variable input of patients by means of simulation. In another study, Abtahi et al [36] investigated the scheduling problem of prioritized outpatients in a medical center. The objective function of the problem is to minimize the total length of time the patients spend in the medical center.…”

Section: Literature Reviewmentioning

confidence: 99%

Operating Room Scheduling Optimization Based on a Fuzzy Uncertainty Approach and Metaheuristic Algorithms

Maghzi

Mohammadi

Pasandide

et al. 2022

IJE

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T oday, planning and scheduling problems are the most significant issues in the world and make a great impact on improving organizational productivity and serving systems such as medical and healthcare providers. Since operating room planning is a major problem in healthcare organizations, the optimization of medical staff and equipment plays an essential role. Thus, this study presents a multiobjective mathematical model with a new categorization (preoperative, intraoperative, and postoperative) to minimize operating room scheduling and the risk of using equipment. Time constraints in healthcare systems and medical equipment limited capacity are the most significant considered limitation in the present study. In this regard, since the duration of patient preparation and implementation of treatment processes occur in three states of optimistic, pessimistic, and normal, the introduced parameters are examined relying on a fuzzy uncertainty analysis of the problem. Hence, the model is measured in a real numerical solution sample in a medical center to evaluate and confirm the proposed mathematical model. T hen, two meta-heuristic algorithms (NRGA and NSGAII) are implemented on the mathematical model to analyze the proposed model. Finally, the research results indicate that the NSGA-II is more efficient in the operating room scheduling problem.

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A Stochastic Model for Prioritized Outpatient Scheduling in a Radiology Center

Cited by 6 publications

References 22 publications

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Rescheduling Unreliable Service Providers in a Dynamic Multi-objective Cloud Manufacturing

Cross-modal Deep Learning-based Clinical Recommendation System for Radiology Report Generation from Chest X-rays

Operating Room Scheduling Optimization Based on a Fuzzy Uncertainty Approach and Metaheuristic Algorithms

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