Decision Support Tool Research Articles

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations.

In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors.

Habitat use of anadromous and amphidromous sturgeons in North America: a systematic review

Sturgeons are among the most endangered fishes in the world. Identifying habitat use characteristics to inform restoration projects is crucial for recovery. However, small sample sizes, inadequate replication of studies, and limited spatial extents complicate our ability to effectively apply the findings of single studies to endangered species conservation across the larger riverscape. We synthesized information from amphidromous and anadromous sturgeons in North America to identify species-specific knowledge gaps and conduct a quantitative comparison of species–habitat relationships. We provided a qualitative summary of substrate use and synthesized estimates of depth and velocity during spawning and non-spawning activity. Generalized patterns among species were identified, such as spawning in fast water on hard substrate and then using slow water with soft substrate areas when not spawning. We noted species-specific variability during spawning that may be attributed to historical maximum length, egg characteristics, and watershed features. This study provides some of the first estimates of habitat use that can be adapted for many populations. Results can contribute to empirically grounded decision-support tools used to prioritize information needs for recovery.

Building a decision-support tool to inform sustainability approaches under complexity: Case study on managing wild ruminants

In wildlife management, differing perspectives among stakeholders generate conflicts about how to achieve disparate sustainability goals that include ecological, economic, and sociocultural dimensions. To mitigate such conflicts, decisions regarding wildlife management must be taken thoughtfully. To our knowledge, there exists no integrative modeling framework to inform these decisions, considering all dimensions of sustainability. We constructed a decision-support tool based on stakeholder workshops and a Bayesian decision network to inform management of wild ruminants in the federal state of Lower Austria. We use collaborative decision analysis to compare resource allocations while accounting for trade-offs among dimensions of sustainability. The tool is designed for application by non-technical users across diverse decision-making contexts with particular sets of wildlife management actions, objectives, and uncertainties. Our tool represents an important step toward developing and evaluating a transparent and replicable approach for mitigating wildlife-based conflicts in Europe and beyond.

Development and validation of machine learning-based models for predicting healthcare-associated bacterial/fungal infections among COVID-19 inpatients: a retrospective cohort study

BackgroundCOVID-19 and bacterial/fungal coinfections have posed significant challenges to human health. However, there is a lack of good tools for predicting coinfection risk to aid clinical work.ObjectiveWe aimed to investigate the risk factors for bacterial/fungal coinfection among COVID-19 patients and to develop machine learning models to estimate the risk of coinfection.MethodsIn this retrospective cohort study, we enrolled adult inpatients confirmed with COVID-19 in a tertiary hospital between January 1 and July 31, 2023, in China and collected baseline information at admission. All the data were randomly divided into a training set and a testing set at a ratio of 7:3. We developed the generalized linear and random forest models for coinfections in the training set and assessed the performance of the models in the testing set. Decision curve analysis was performed to evaluate the clinical applicability.ResultsA total of 1244 patients were included in the training cohort with 62 healthcare-associated bacterial/fungal infections, while 534 were included in the testing cohort with 22 infections. We found that patients with comorbidities (diabetes, neurological disease) were at greater risk for coinfections than were those without comorbidities (OR = 2.78, 95%CI = 1.61–4.86; OR = 1.93, 95%CI = 1.11–3.35). An indwelling central venous catheter or urinary catheter was also associated with an increased risk (OR = 2.53, 95%CI = 1.39–4.64; OR = 2.28, 95%CI = 1.24–4.27) of coinfections. Patients with PCT > 0.5 ng/ml were 2.03 times (95%CI = 1.41–3.82) more likely to be infected. Interestingly, the risk of coinfection was also greater in patients with an IL-6 concentration < 10 pg/ml (OR = 1.69, 95%CI = 0.97–2.94). Patients with low baseline creatinine levels had a decreased risk of bacterial/fungal coinfections(OR = 0.40, 95%CI = 0.22–0.71). The generalized linear and random forest models demonstrated favorable receiver operating characteristic curves (ROC = 0.87, 95%CI = 0.80–0.94; ROC = 0.88, 95%CI = 0.82–0.93) with high accuracy, sensitivity and specificity of 0.86vs0.75, 0.82vs0.86, 0.87vs0.74, respectively. The corresponding calibration evaluation P statistics were 0.883 and 0.769.ConclusionsOur machine learning models achieved strong predictive ability and may be effective clinical decision-support tools for identifying COVID-19 patients at risk for bacterial/fungal coinfection and guiding antibiotic administration. The levels of cytokines, such as IL-6, may affect the status of bacterial/fungal coinfection.

Optimization of E-bike networks

ABSTRACT Battery-assisted bicycles, or E-bikes, are part of a disruptive wave of transportation technology that uses electricity and rechargeable batteries to increase the velocity, the traveled distance and, as a consequence, the ridership. Biking and E-biking are globally recognized to have the potential to play an important role in the transition to a Net-Zero society. The widespread availability of E-bikes is significantly impacting several sectors of the tourist industry. Therefore, Touristic Administrations (TAs) now provide tourists with trail options and the corresponding charging infrastructure for E-bikers with different profiles. Our main objective is to provide TAs with a suitable decision-support tool that serves two purposes: (1) finding locations for charging stations by considering the difficulty and the cost of installing such stations in remote, often off-the-road locations; and (2) designing itineraries that are suitable for different categories of E-bikers. In the scientific literature, the first decision component has been mostly addressed in the context of electric cars, and it is not suitable for E-bikes. On the other hand, works on the second decision focused on muscular bikes, thus ignoring the first decision component. In this paper, we aim at closing this gap. We formulate this problem as a mixed-integer linear program. We develop an efficient branch-and-cut algorithm and present a comprehensive computational experiment. In particular, we provide a case study in the Asiago Sette Comuni Plateau in Italy, where the obtained charging stations and bike trails maximize a measure of attractiveness for three types of users.

Assessment of drug utilization and potentially inappropriate medications in hemodialysis patients with end-stage renal dysfunction: A study in a tertiary care hospital in Bahrain.

Patients undergoing dialysis pose therapeutic challenges in terms of polypharmacy, administration of potentially inappropriate drugs, and drugs with the potential risk of toxicity. This study evaluated the use of drugs, potentially inappropriate medicines (PIM), drugs with risk of Torsades de Pointes (TdP), and the complexity of the prescribed regimen using the medication regimen complexity index scale in patients undergoing hemodialysis. A retrospective cohort study was carried out amongst patients receiving hemodialysis. Drugs were classified into one of four classes: (i) drugs used in managing renal complications, (ii) cardiovascular drugs, (iii) anti-diabetic drugs, (iv) drugs for symptomatic management, and (v) others. Drugs were considered as PIM according to the Can-SOLVE CKD working group from a network of Canadian nephrology health professionals. The study adhered to the CredibleMeds classification of drugs with known, possible, and conditional risk of TdP and the complexity of prescribed medicines was evaluated based on the pre-validated medication regimen complexity index scale based on form/route, frequency of dosing, and requirement of special instructions. Sixty-three participants were included in the study (49 males and 14 females) with the median (range) age of 45 (21-66) years. Cardiovascular drugs followed by drugs used for managing renal complications were the most common classes administered. Notably, 12 (19.1%) patients received one of the non-steroidal anti-inflammatory drugs, 21 (33.3%) received a proton pump inhibitor, three (4.8%) received pregabalin, two (3.2%) received opioid drugs, and one (1.6%) was administered celecoxib. Atorvastatin, furosemide, omeprazole, and allopurinol were the most common PIM drugs administered to the study participants followed by others. Drugs used for symptomatic management had significantly more PIM compared to other classes (p <0.0001). Six (9.5%) patients received drugs with known TdP risk, one with possible TdP risk, and 61 with conditional risk. Median (range) medical regimen complexity index score was 26.5 (2-62.5). A huge burden of drug therapy was observed in the hemodialysis patients in terms of higher proportions of PIM, complex medical regimen, and prescription of drugs with risk of TdP. Implementation of clinical decision support tools enhancing rational prescription and identification of drugs with TdP risk, introducing antimicrobial stewardship, and stepwise deprescription of the drugs with the least benefit-risk ratio are warranted.

Contextual design requirements for decision-support tools involved in weaning patients from mechanical ventilation in intensive care units

Weaning patients from ventilation in intensive care units (ICU) is a complex task. There is a growing desire to build decision-support tools to help clinicians during this process, especially those employing Artificial Intelligence (AI). However, tools built for this purpose should fit within and ideally improve the current work environment, to ensure they can successfully integrate into clinical practice. To do so, it is important to identify areas where decision-support tools may aid clinicians, and associated design requirements for such tools. This study analysed the work context surrounding the weaning process from mechanical ventilation in ICU environments, via cognitive task and work domain analyses. In doing so, both what cognitive processes clinicians perform during weaning, and the constraints and affordances of the work environment itself, were described. This study found a number of weaning process tasks where decision-support tools may prove beneficial, and from these a set of contextual design requirements were created. This work benefits researchers interested in creating human-centred decision-support tools for mechanical ventilation that are sensitive to the wider work system.

The Effect of an Oxytocin Decision Support Checklist on Oxytocin Use and Maternal and Neonatal Outcomes: A Retrospective Cohort Study.

To assess the association between use of an oxytocin decision support checklist with oxytocin usage and clinical outcomes. We conducted a retrospective cohort study of patients with singleton gestations at 370/7 weeks or greater who received oxytocin during labor from October 2012 to February 2017 at an integrated community health care system during three exposure periods: (1) prechecklist; (2) after paper checklist implementation; and (3) after checklist integration into the electronic medical record (EMR). The checklist was a clinical decision support tool to standardize the dosing and management of oxytocin. Thus, our primary outcomes included oxytocin infusion rates and cumulative dose. Secondary outcomes included maternal and neonatal outcomes. We controlled for maternal risk factors with multivariable regression analysis and stratified by mode of delivery. A total of 34,269 deliveries were included. Unadjusted analyses showed that compared with prechecklist, deliveries during the paper and EMR-integrated periods had a lower cumulative dose (4,670 ± 6,174 vs. 4,318 ± 5,719 and 4,286 ± 5,579 mU, p < 0.001 for both), lower maximal infusion rate (9.9 ± 6.8 vs. 8.7 ± 5.8 and 8.4 ± 5.6 mU/min, p < 0.001 for both), and longer duration of oxytocin use (576 ± 442 vs. 609 ± 476 and 627 ± 488 minutes, p < 0.001 and p = 0.01, respectively). The unadjusted rates of cesarean, 5-minute Apgar <7, mechanical ventilation, and neonatal hospital length of stay were similar between periods. The adjusted mean difference in time from admission to delivery was longer during the EMR-integrated period compared with prechecklist (3.0 [95% confidence interval: 2.7-3.3] hours, p < 0.001). Oxytocin checklist use was associated with decreased oxytocin use patterns at the expense of longer labor times. Findings were more pronounced with EMR integration. · An oxytocin decision support checklist is associated with reduced amounts of oxytocin used.. · However, checklists were associated with longer duration of oxytocin use and of labor.. · Results were more pronounced in the EMR-integrated checklist compared with paper checklist..

Assessment of the quality of online patient information resources for patients considering parastomal hernia treatment.

The aim was to examine the quality of online patient information resources for patients considering parastomal hernia treatment. A Google search was conducted using lay search terms for patient facing sources on parastomal hernia. The quality of the content was assessed using the validated DISCERN instrument. Readability of written content was established using the Flesch-Kincaid score. Sources were also assessed against the essential content and process standards from the National Institute for Health and Care Excellence (NICE) framework for shared decision making support tools. Content analysis was also undertaken to explore what the sources covered and to identify any commonalities across the content. Fourteen sources were identified and assessed using the identified tools. The mean Flesch-Kincaid reading ease score was 43.61, suggesting that the information was difficult to read. The overall quality of the identified sources was low based on the pooled analysis of the DISCERN and Flesch-Kincaid scores, and when assessed against the criteria in the NICE standards framework for shared decision making tools. Content analysis identified eight categories encompassing 59 codes, which highlighted considerable variation between sources. The current information available to patients considering parastomal hernia treatment is of low quality and often does not contain enough information on treatment options for patients to be able to make an informed decision about the best treatment for them. There is a need for high-quality information, ideally co-produced with patients, to provide patients with the necessary information to allow them to make informed decisions about their treatment options when faced with a symptomatic parastomal hernia.

Development and Implementation of a Decision-making Support Tool for Measures against Clubroot of Broccoli

Development and Implementation of a Decision-making Support Tool for Measures against Clubroot of Broccoli

Diagnosing the socio-technical refrigeration system with Radical Innovation Design methodology to target innovations

ABSTRACT This research aims to address the increasing regulatory and environmental challenges associated with refrigeration systems by fostering innovations. Refrigeration systems, as socio-technical entities, present a challenge due to their complexity, diverse stakeholders, and evolving regulatory constraints. Identifying opportunities for innovation in such intricate systems is further complicated by various performance-contributing factors. The Radical Innovation Design (RID) methodology, designed for aligning design solutions with activities, proves effective in pinpointing and prioritizing innovation targets across the lifecycle of a complex system. In this study, data from interviews and practices in the retail refrigeration sector inform the RID methodology. Two sets of results are presented. Firstly, a knowledge book of the socio-technical system is categorized into four RID dimensions: usage situations, user profiles, current problems, and existing solutions. The second set includes outcomes from two RID decision-support tools: the RID Comparator evaluates the effectiveness of existing solutions, while the RID Compass identifies three improvement areas – poor adaptation of new technical clusters, disrupted stakeholder interaction processes, and a lack of common tools. These results underwent validation through expert iterations, leading to a recommended hybrid solution involving the development of an integrated model-based platform.

Feature importance for software development effort estimation using multi level ensemble approaches

Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.

Integrated image and location analysis for wound classification: a deep learning approach

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79–100% for Region of Interest (ROI) without location classifications, 73.98–100% for ROI with location classifications, and 78.10–100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Planning shared mobility hubs in European cities: A methodological framework using MCDA and GIS applied to Barcelona

In the move towards sustainable urban mobility through seamless intermodality, European cities are faced with the possibility of implementing shared mobility systems. They constitute an opportunity to create new urban hubs, considered as nodes of intermodality and places of urban intensity. In order to effectively plan the future locations of shared mobility hubs, this paper outlines the methodological framework that sustains the design of a new decision-support tool, the so-called Mcda2MobilityHub (M2MHub),11Link to the M2MHub tool, which is available as an open plugin for QGIS: https://github.com/natrouk/Mcda2MobilityHub-desicion-support-tool_QGIS-plugin. using Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS). For this purpose, the analysis of existing literature and interviews with local authorities and transport operators, led to the identification of six main location criteria (mobility, compactness, diversity of uses, profile of potential users, spatial configuration and environment). The criteria weights were calculated using the Analytic Hierarchy Process (AHP) and the framework was applied to the case study of the city of Barcelona. The results obtained are presented in the form of heat maps, highlighting the flexibility of the decision-support tool to transform open data into appropriate indicators and detailed maps. This output can be used to support future policies for planning shared mobility hubs as places for convenient multimodal transfers, which also enable social activities and improve the quality of life in European cities.

Abstract 68: Imaging Endothelial PD-L1 expression with ultrasound molecular imaging to stratify immune checkpoint inhibitor responders

Abstract Immune checkpoint blockade therapies (ICTs) have revolutionized oncology through substantial and lasting treatment outcomes, but only in 15-20% of patients. Non-invasive diagnostic tools to select patients that are most likely to respond to ICT and guide treatment regimens, and thus minimize toxicities to non-responder patients, are in high demand. Program death-1 ligand (PD-L1) marker evaluation in the tumor vasculature and tumor microenvironment is an essential criterion for identifying a suitable patient for PD-L1 based ICT, and it also serves as a prognostic marker in tumor response evaluation. Ultrasound molecular imaging (USMI) is an up-and-coming non-invasive imaging modality that enables longitudinal characterization of endothelial-specific markers. We are developing USMI methods to image immune markers expressed specifically on endothelial cells (EC) of tumors as a decision-support tool for guiding ICT strategies. In this work, we demonstrate for the first time the potential of imaging EC-specific immunosuppressive markers such as PD-L1 and investigate whether it can act as a marker for predicting ICT response. Target-ready microbubble (MB) was used (Trust Bio-sonics, Taiwan) to generate PD-L1 antibody (Ab) linked MBs (TMBs). DSPE-PEG was functionalized to anti-PD-L1 Ab or isotype-Ab (control) to generate stable TMBs (~5 × 107 MBs/mL) to target PD-L1 expressing ECs in vivo. We studied the treatment effect of anti-PD-L1-Ab and isotype-Ab using a syngeneic CT-26 tumor in mice and demonstrated the targeting potency of TMB or iso-Ab-MB (NMB). US imaging was performed using Vevo 2100 high-frequency US system with M250 transducer to evaluate and quantify the differentially targeted enhancement parameter (dTE). We observed that TMBs bound PD-L1 expressing ECs in vivo, with high and low enhancement for TMB and NMB, respectively, in tumors validated by dTE quantification through characterization of the slower washout kinetics. Furthermore, TMBs exhibited increased tumor retention and prolonged binding with average differential targeted enhancement values (dTE) (+/- SD) of 5.2 ± 7.4 for TMBs compared to 2.4 ± 3.9 for NMBs (p=0.14). Preliminary results of USMI (dTE values of TMBs) indicate that PD-L1-treated mice showed a higher response (4.5 ± 5.2; n=9) to the treatment group than control mice that did not receive Ab (1.4 ± 0.8; n=4). The tumor growth rate for the treated mice group was 5.4 -fold retarded compared to the control group which correlated with USMI dTE values. The USMI signals correlated with IHC performed on tumor tissue ex vivo from the respective mouse used for US imaging. Overall, our pre-clinical study suggests that USMI using TMBs may contribute to non-invasive assessment of IC expression on tumor ECs, and that it may serve as a predictive biomarker of ICT response. This novel TMB agent could be a game changer for CE USMI-based cancer prognosis in ICB therapy. Citation Format: Arutselvan Natarajan, Farbod Tabesh, Mahsa Bataghva, Ramasamy Paulmurugan, Ahmed Nagy El Kaffas. Imaging Endothelial PD-L1 expression with ultrasound molecular imaging to stratify immune checkpoint inhibitor responders [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 68.

Game-theoretic analysis of competition between access service providers utilizing a Nash genetic algorithm

Fiber-to-the-home (FTTH) technology is a promising solution for providing advanced service delivery to end-users, but its implementation requires substantial capital expenditures. To minimize investment risks and aid decision-making for access service providers, we propose a game theoretic framework based on a modified Nash genetic algorithm. We illustrate how this framework can be applied to analyze the competition between access providers offering a flat-rate FTTH service on multiple geographical areas. Each provider determines its price for all areas simultaneously and decides whether to invest on a particular area depending on the anticipated revenues. Two distinct demand models are adopted to describe different types of consumer behavior. A solution engine, based on a modified mixed-variable Nash genetic algorithm is implemented under an open-source license. The significance and practical implications of the equilibrium points obtained for both single and multiple area games are discussed. The proposed framework and the solution engine developed, aid both providers and regulatory bodies to analyze competitive environments. They can also be used to implement decision support tools for similar problems as well. The paper concludes by pointing out further research directions in this context.

The centre for health informatics: a novel approach to accelerating the field of health data science

Precision Medicine and Precision Public Health are approaches to improve population health. Achieving these goals requires innovation in health informatics. The Centre for Health Informatics (CHI) within the Cumming School of Medicine (CSM) at the University of Calgary (UC), Canada, was created to respond to this need by fostering multidisciplinary collaborations, building capacity by recruiting and training outstanding faculty and students, and harnessing Alberta's rich health data to advance health informatics. To establish CHI as a health informatics leader, CHI has struck partnerships with stakeholders, including Alberta Health Services (AHS), Alberta Health (AH), and the Alberta Strategy for Patient-Oriented Research Unit (AbSPORU) among others. Through these close relationships, the CHI intake team facilitates access to Alberta's rich health data sources and educates researchers on the available health data in Alberta. The concept of a "One Stop Shop" for CSM and UC researchers encourages multidisciplinary collaboration, helps investigators access a wide range of datasets, and receive analytical support. Population-based data sets enable the development of methods to turn raw data into health information, improve health data collection, linkage, analysis, and quality, and applied studies creating clinical decision-support tools, prognostic tools, improved health surveillance methods, and health system performance indicators. CHI's ecosystem of diverse research expertise, cutting-edge technology, and embedded AHS analysts to support data access via a wide-ranging network of partnerships allows our provincial researchers, national and international collaborators tremendous opportunities for empirical research. It paves the way for implementing Precision Medicine in the real world.

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

Developing conceptual model for safety risk management in aviation as building capacity and resilience strategy

Purpose This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions. Design/methodology/approach The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research. Findings To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring. Research limitations/implications This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation. Social implications This research may contribute to more safe operations and functions in the aviation industry. Originality/value Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

National Immunization Program Decision Making Using the CAPACITI Decision-Support Tool: User Feedback from Indonesia and Ethiopia.

To ensure that limited domestic resources are invested in the most effective interventions, immunization programs in low- and middle-income countries (LMICs) must prioritize a growing number of new vaccines while considering opportunities to optimize the vaccine portfolio, as well as other components of the health system. There is a strong impetus for immunization decision-making to engage and coordinate various stakeholders across the health system in prioritization. To address this, national immunization program decision-makers in LMICs collaborated with WHO to structure deliberation among stakeholders and document an evidence-based, context-specific, and transparent process for prioritization or selection among multiple vaccination products, services, or strategies. The output of this effort is the Country-led Assessment for Prioritization on Immunization (CAPACITI) decision-support tool, which supports using multiple criteria and stakeholder perspectives to evaluate trade-offs affecting health interventions, taking into account variable data quality. Here, we describe the user feedback from Indonesia and Ethiopia, two initial countries that piloted the CAPACITI decision-support tool, highlighting enabling and constraining factors. Potential immunization program benefits and lessons learned are also summarized for consideration in other settings.