Development of A Multi-Agent System for Automated Resource Allocation in Maintenance of Hospital Buildings

Facility managers of hospitals face complex maintenance decisions as they address a multitude of maintenance requests in an environment of limited resources and segmented information. The complex, uncertain, and dynamic nature of the maintenance management environment is a source of concern to facility managers in hospitals due to unexpected failure of building components, the daily arrival of maintenance orders, and changes in related schedules. Responding to a growing demand for maintenance, on one hand, and lack of proper maintenance management systems, on the other, has led to delays in repair and maintenance of the building components and systems in hospitals. Such delays could cause significant distress to patients and health-care personnel. In such circumstances, centralized systems become inadequate because of their top-down approach which lacks a feedback mechanism and ignores new information. Therefore, to address any change, centralized systems have to be reformulated making it impractical, short-sighted, and problematic to adopt them in hospitals. As such, the use of centralized systems can lead to financial losses and dissatisfaction of patients. It, therefore, becomes necessary to establish a distributed maintenance management system to support the decision-making process of facility managers. To address the issues stated above, this thesis presents three newly developed automated models (1) a computer resource allocation model for integrating fragmented maintenance information; (2) two simulation models that represent the dynamic environment of maintenance resource allocation; and (3) a Simulation-Based Optimization (SBO) model for resource allocation that minimizes the down-time of building components being considered for maintenance. Accordingly, this research initially focuses on the maintenance workflow and resource allocation issues pertinent to hospitals. A distributed system is developed to integrate segmented information at different levels of maintenance management with the aim of minimizing maintenance delays in hospital buildings. Multi-Agent Facility Management System (MAFMS) is conceptually designed as a distributed interactive system. This design employed Unified Modeling Language (UML) diagrams that illustrate the specific agents of the system and how these agents interact with each other. Two simulation models are then developed to demonstrate the benefits of the developed method in reducing the response time to maintenance requests. The developed simulation models consist of two components: a workflow process model and Resource Allocation System (RAS). A Discrete Event Simulation (DES) is used to simulate the maintenance process flow while a Multi-Agent System (MAS) is used to simulate the process of allocating resources for maintenance activities in hospital buildings. The workflow process is simulated as a DES. The workflow process contains order registration, arrangement, and maintenance tasks distribution (orders). For the RAS component, a Multi-Agent Resource Allocation Simulation (MARAS) is developed to simulate different resource allocation scenarios accounting for interactions among various agents (decision-makers) in the maintenance process. A case example is presented to demonstrate the essential features of the developed method. Maintenance data of a hospital building is used to initiate the multi-agent simulation for workflow management process. The simulation results show the benefits of the developed method, to reduce the response time to maintenance requests. Sensitivity analysis method is used to validate the simulation model. Finally, the third model, i.e. the SBO model, is developed using OptQuest. This model is proposed to optimize the use of limited resources and reduce the down-time of building services components. The SBO model is validated using the sensitivity analysis method

Assessing secular trends in HIV rapid diagnostic test uptake and positivity in Northeast Iran, a country in MENA region; ingredients for target-specific prevention policies

Abstract Background Iran is amongst the first three countries in Middle East and North Africa (MENA) region where two-thirds of region’s new HIV infections are reported. HIV testing at the population level is key to interrupting the HIV transmission chain. The current study aimed to evaluate the history of HIV rapid diagnostic testing (HIV-RDT) and its correlates in northeast Iran. Methods In this cross-sectional study, de-identified records of HIV-RDTs were extracted by the census method from the electronic health information system of 122 testing facilities between 2017 and 2021. Descriptive, bivariate, and multiple logistic regression analyses were performed to identify the factors associated with HIV-RDT uptake and risks and drivers of HIV-RDT positivity, separately among men and women. Results Conducting 66,548 HIV-RDTs among clients with a mean age of 30.31 years, 63% female, 75.2% married, and 78.5% with high school education or below, yielded 312 (0.47%) positive results. Test uptake was comparatively low among men and the unmarried sub-population. Prenatal care and high-risk heterosexual intercourse were the most frequent reasons for taking HIV-RDT among women and men, respectively (76% and 61.2%). High-risk heterosexual contact, tattooing, mother-to-child transmission (MTCT), having a partner at risk of HIV infection, and injecting drugs were test seekers’ most reported transmission routes. One-third of the newly-infected female clients were identified through prenatal testing. Multivariate analysis revealed older age at the time of testing (Adjusted Odd Ratio (AOR) = 1.03), divorce (AOR = 2.10), widowhood (AOR = 4.33), education level of secondary school (AOR = 4.67), and unemployment (AOR = 3.20) as significant demographic predictors of positive HIV-RDT (P-value  0.05). Conclusion Innovative strategies are required to scale up test uptake and positive yields among the key population in the region. The current evidence strongly suggests implementing gender-targeted strategies, according to the differences in demographic and behavioral risk between men and women

Predictive model for survival in patients with gastric cancer

Background and aim: Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer. Methods: This was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them. Results: Of the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ± SD of missing values for each patient was 4.43±1.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients’ family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival. Conclusion: The study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients’ survival

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan–Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein–protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC