Real-time Decision Support System Research Articles

Real-time Analytics and Clinical Decision Support Systems: Transforming Emergency Care

In the rapidly evolving landscape of emergency medicine, real-time analytics and Clinical Decision Support Systems (CDSS) have emerged as transformative tools for enhancing patient care and clinical decision-making. This comprehensive article examines these technologies' integration, implementation, and impact in emergency care settings. The article covers the foundational aspects of real-time analytics, the intelligence layer provided by CDSS, and the sophisticated technology stack supporting these systems. The article explores various clinical applications, including sepsis detection, trauma care, and cardiac emergency management, while addressing implementation challenges and future opportunities. The findings demonstrate significant improvements in patient outcomes, operational efficiency, and clinical workflow optimization across emergency departments. Integrating artificial intelligence, machine learning, and advanced visualization technologies has revolutionized emergency care delivery, setting new standards for clinical excellence and patient safety

Systematic Review of Quantitative Risk Quantification Methods in Construction Accidents

Construction accidents pose significant risks to workers and the public, affecting industry productivity and reputation. While several reviews have discussed risk assessment methods, recent advancements in artificial intelligence (AI), big data analytics, and real-time decision support systems have created a need for an updated synthesis of the quantitative methodologies applied in construction safety. This study systematically reviews the literature from the past decade, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search identified studies utilizing statistical analysis, mathematical modeling, simulation, and artificial intelligence (AI). These methods were categorized and analyzed based on their effectiveness and limitations. Statistical approaches, such as correlation analysis, examined relationships between variables, while mathematical models, like factor analysis, quantified risk factors. Simulation methods, such as Monte Carlo simulations, explored risk dynamics and AI techniques, including machine learning, enhanced predictive modeling, and decision making in construction safety. This review highlighted the strengths of handling large datasets and improving accuracy, but also noted challenges like data quality and methodological limitations. Future research directions are suggested to address these gaps. This study contributes to construction safety management by offering an overview of best practices and opportunities for advancing quantitative risk assessment methodologies.

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data

PurposeSpontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortality rate of 40 ​%, accurate diagnosis and prompt initiation of appropriate antibiotic therapy are crucial for optimizing patient outcomes and preventing life-threatening complications. This study aimed to expand the use of computational models to improve the diagnostic accuracy of SBP in cirrhotic patients by incorporating a broader range of data, including clinical variables and laboratory values. Patients and methodsWe employed 5 machine learning classification methods - Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Random Forest, utilizing a variety of demographic, clinical, and laboratory features and biomarkers. ResultsAscitic fluid markers, including white blood cell (WBC) count, lactate dehydrogenase (LDH), total protein, and polymorphonuclear cells (PMN), significantly differentiated between SBP and non-SBP patients. The Random Forest model demonstrated the highest overall accuracy at 86 ​%, while the Naive Bayes model achieved the highest sensitivity at 72 ​%. Utilizing 10 key features instead of the full feature set improved model performance, notably enhancing specificity and accuracy. ConclusionOur analysis highlights the potential of machine learning to enhance the accuracy of SBP diagnosis in cirrhotic patients. Integrating these models into clinical workflows could substantially improve patient outcomes. To achieve this, ongoing multidisciplinary research is crucial. Ensuring model interpretability, continuous monitoring, and rigorous validation will be essential for the successful implementation of real-time clinical decision support systems.

EHealth in pediatric respiratory allergy.

This review explores the relevance of eHealth technologies to address unmet needs in pediatric respiratory allergies, particularly allergic rhinitis (AR) and asthma. Given the increasing burden of these conditions, there is a pressing need for effective solutions to enhance disease surveillance, diagnosis, and management. Recent literature highlights the potential of eHealth tools to transform pediatric respiratory allergy care. The use of digital data for infodemiology, application of machine learning models to improve diagnostic sensitivity, smartphone apps with digital patient reported outcome measure (PROMs) and embedded sensors to monitor disease, healthcare professional dashboards with real-time data monitoring and clinical decision support systems (CDSS) are advances emerging to optimize pediatric respiratory allergy care. Integrating eHealth technologies into the pediatric respiratory allergy care pathway is a potential solution for current healthcare challenges to better meet the needs of children with AR and asthma. However, while the potential of eHealth is evident, its widespread implementation in real-world practice requires continued research, collaboration, and efforts to overcome existing barriers.

Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Artificial Intelligence-Based Models for Prediction of Mortality in ICU Patients: A Scoping Review.

Healthcare professionals may be able to anticipate more accurately a patient's timing of death and assess their possibility of recovery by implementing a real-time clinical decision support system. Using such a tool, the healthcare system can better understand a patient's condition and make more informed judgements about distributing limited resources. This scoping review aimed to analyze various death prediction AI (Artificial Intelligence) algorithms that have been used in ICU (Intensive Care Unit) patient populations. The search strategy of this study involved keyword combinations of outcome and patient setting such as mortality, survival, ICU, terminal care. These terms were used to perform database searches in MEDLINE, Embase, and PubMed up to July 2022. The variables, characteristics, and performance of the identified predictive models were summarized. The accuracy of the models was compared using their Area Under the Curve (AUC) values. Databases search yielded an initial pool of 8271 articles. A two-step screening process was then applied: first, titles and abstracts were reviewed for relevance, reducing the pool to 429 articles. Next, a full-text review was conducted, further narrowing down the selection to 400 key studies. Out of 400 studies on different tools or models for prediction of mortality in ICUs, 16 papers focused on AI-based models which were ultimately included in this study that have deployed different AI-based and machine learning models to make a prediction about negative patient outcome. The accuracy and performance of the different models varied depending on the patient populations and medical conditions. It was found that AI models compared with traditional tools like SAP3 or APACHE IV score were more accurate in death prediction, with some models achieving an AUC of up to 92.9%. The overall mortality rate ranged from 5% to more than 60% in different studies. We found that AI-based models exhibit varying performance across different patient populations. To enhance the accuracy of mortality prediction, we recommend customizing models for specific patient groups and medical contexts. By doing so, healthcare professionals may more effectively assess mortality risk and tailor treatments accordingly. Additionally, incorporating additional variables-such as genetic information-into new models can further improve their accuracy.

A Dynamic Simulation Model for Near-Zero Rebar-Cutting Waste through Special-Length-Priority Optimization

Global economic fluctuations as exemplified by the recent COVID-19 financial crisis significantly impact the construction industry, particularly steel rebar supply chain and procurement. This impedes engineers’ efforts toward achieving near-zero rebar-cutting waste due to dynamic rebar minimum order quantities and maximum lengths imposed by steel mills. This study addresses the challenge of achieving near-zero rebar-cutting waste by proposing a model that simulates the level of optimization in minimizing rebar-cutting waste amidst such dynamics. The model was implemented in a case study involving reinforced concrete columns in a high-rise building. While achieving near-zero waste consistently proved challenging, particularly for greater than 50 tons of minimum quantity, the study identified a maximum 12 m rebar variant that attained this target regardless of minimum order quantity. Nonetheless, this study introduces a real-time decision-support system for rebar procurement, empowering engineers to optimize usage and minimize waste. This system facilitates near-zero rebar-cutting waste levels in response to rebar procurement requirement dynamics.

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs.

Innovations in Time Related Expression Recognition Using LSTM Networks

The proposed architecture leverages the strengths of both Convolutional Neural Network (CNN) and Bidirectional Long Short-Term (BLSTM) to create a robust model for temporal expression recognition in clinical texts. The CNN component effectively captures morphological and orthographic features at the character level, which enriches the semantic understanding of complex medical terminologies that are often abbreviated or have unique suffixes and prefixes. The BLSTM component excels in capturing long-range dependencies in text, which is crucial for understanding the context in which temporal expressions occur. By integrating these models with a CRF layer, the system not only predicts discrete labels but also ensures that the sequence of predicted labels is coherent and contextually appropriate, addressing the limitations of models that predict labels independently. The integration of pre-trained biomedical word vectors provides significant contextual grounding tailored to the medical domain, enhancing the model's ability to discern and interpret the nuances of medical language. This is crucial in clinical contexts where accurate interpretation of temporal phrases can be critical for patient management and treatment timelines. Further, experiments conducted on the dataset validate the effectiveness of the proposed model, demonstrating a notable improvement over traditional methods that rely heavily on hand-crafted features and rule-based approaches. Future work could explore the adaptability of this model to other subdomains of the medical field and its efficacy in processing multilingual texts, potentially increasing its applicability in global healthcare settings, with further refinement of the neural architecture and optimization of training strategies potentially yielding even better performance and faster processing times essential for real-time clinical decision support systems.

Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review

The agricultural sector faces increasing challenges in ensuring food security and optimizing crop yield, necessitating innovative solutions for early detection and mitigation of plant growth stress. The integration of advanced imaging technologies with artificial intelligence (AI) has emerged as a powerful tool for non-invasive, real-time monitoring of plant health. The objective of this paper was to review the application of machine vision and AI in identifying and classifying plant growth stress, with a focus on stressors, datasets, and the use of intelligent algorithms. The significance of plant growth stress induced by environmental variables, including temperature, light, nutrient deficiencies, and water supply were addressed and the conventional stress detection methodologies, underscores their inherent limitations, and establishes the groundwork for the exploration of state-of-the-art technologies in stress assessment. Various sensor technologies were explored, encompassing traditional RGB cameras, multispectral and hyperspectral sensors, and thermal imaging, each capable of capturing distinct stress signatures. Machine vision, leveraging high-resolution imaging and spectroscopy, offers detailed insights into plant physiological responses. Coupled with AI approaches such as deep learning, neural networks, and pattern recognition, machine vision enables the automated analysis of vast datasets, enhancing the accuracy and speed of stress detection. The recent advancements in image processing techniques tailored for plant stress identification were focused and discussed the role of feature extraction, classification, and predictive modelling in achieving robust results. The potentials of AI in plant stress physiology and its role in overcoming the limitations of traditional methods, and the use of unsupervised identification of visual symptoms to quantify stress severity, allowing for the identification of different types of plant stress were studied. Moreover, the potentials of machine vision technology and AI for real-time monitoring and decision support systems in precision agriculture were discussed. The findings of this review would contribute to the growing field of agricultural technology, offering insights into the development of automated tools that could aid farmers and researchers in mitigating the impact of abiotic stressors on crop/plant health and productivity.

Real-time decision support for human–machine interaction in digital railway control rooms

This study proposes a real-time Decision Support System (DSS) using machine learning to enhance proactive management of Human–Machine Interaction (HMI) in safety–critical digital control rooms. The DSS provides explainable predictions and recommendations regarding near-future automation usage, customized for the railway control room management, who supervise the operations of traffic controllers (TCs). In this setting, TCs decide on the spot whether to manually or automatically open signals to regulate railway traffic, a critical aspect of ensuring punctuality and safety. This time-setting specific HMI differs across TCs and is not yet supported by a data-driven tool. The proposed DSS includes agreement levels for predictions among different modeling paradigms: linear models, tree-based models, and deep neural networks. SHAP (SHapley Additive exPlanations) values are deployed to assess the agreement level in explainability between these different modeling paradigms. The prescriptions are based on the HMI of well-performing peers. We implement the DSS as proof of concept at the Belgian railway infrastructure company and report end-user feedback on the perception, the operational impact, and the inclusion of agreement levels.

The Transformative Impact of Big Data in Healthcare: Improving Outcomes, Safety, and Efficiencies

The integration of Big Data analytics into the healthcare sector has initiated a period of unparalleled transformation, significantly influencing patient care, research, and healthcare administration. This detailed synthesis, derived from a systematic literature review of 31 articles published between 2013 and 2023, examines the extensive impact of Big Data within the healthcare industry. It addresses how Big Data enhances personalized medicine and optimizes treatment protocols, promotes the use of predictive analytics for the early detection and management of diseases, and bolsters patient safety through the implementation of real-time monitoring and decision support systems. The review also confronts the challenges and limitations associated with Big Data's application, including concerns over data privacy and security, ethical dilemmas, interoperability issues, and the necessity for both skilled personnel and advanced technological infrastructure. Furthermore, this overview ventures into future prospects and technological advancements, emphasizing the pivotal role of emerging technologies such as artificial intelligence (AI), machine learning, and blockchain in heralding a new era of healthcare. It highlights Big Data's instrumental role in supporting public health measures and preparing for pandemics, alongside forecasting the ongoing influence of Big Data in fostering innovative healthcare paradigms. This analysis advocates for a concerted effort among healthcare professionals, policymakers, and technology experts to fully leverage Big Data's capabilities in healthcare, aiming to enhance health outcomes, increase operational efficiencies, and ensure the long-term viability of healthcare systems.

TCCC Decision Support With Machine Learning Prediction ofHemorrhage Risk, Shock Probability.

Expected future delays in evacuation during near-peer conflicts in remote locales are expected to require extended care including prolonged field care over hours to days. Such delays can increase potential complications, such as insufficient blood flow (shock), bloodstream infection (sepsis), internal bleeding (hemorrhage), and require more complex treatment beyond stabilization. The Trauma Triage Treatment and Training Decision Support (4TDS) system is a real-time decision support system to monitor casualty health and identify such complications. The 4TDS software prototype operates on an Android smart phone or tablet configured for use in the DoD Nett Warrior program. It includes machine learning models to evaluate trends in six vital signs streamed from a sensor placed on a casualty to identify shock probability, internal hemorrhage risk, and need for a massive transfusion. The project team used a mixed methods approach to create and evaluate the system including literature review, rapid prototyping, design requirements review, agile development, an algorithm "silent test," and usability assessments with novice to expert medics from all three services. Both models, shock (showing an accuracy of 0.83) and hemorrhage/massive transfusion protocol, were successfully validated using externally collected data. All usability assessment participants completed refresher training scenarios and were able to accurately assess a simulated casualty's condition using the phone prototype. Mean responses to statements on evaluation criteria [e.g., fit with Tactical Combat Casualty Care (TCCC), ease of use, and decision confidence] fell at five or above on a 7-point scale, indicating strong support. Participatory design ensured 4TDS and machine learning models reflect medic and clinician mental models and work processes and built support among potential users should the system transition to operational use. Validation results can support 4TDS readiness for FDA 510k clearance as a Class II medical device.

Improving McDiarmid Tree Performance for Predicting Heart Disease from Data Streams with Missing and Meaningless Values

Building machine learning algorithms for real-time clinical decision support systems has become a current research hotspot. The success of these algorithms depends on their ability to handle data stream characteristics. For example, we cite as characteristics the large amounts of data, the high speed and rate of incoming data, and the change in data nature and distribution over time. The Very Fast Decision Tree (VFDT) is a method for incrementally building decision trees. Since its proposition in the literature, it has become one of the most popular tools for data stream classification. This paper aims to optimize a new version of VFDT called McDiarmid Tree (MT) for the early prediction of heart diseases in real-time clinical decision support systems. The proposed method for improving MT performance consists of two main mechanisms: detecting the presence of missing and meaningless values in data attributes and handling the impact of this presence. The proposed MT has been compared with MT and VFDT. Simulation results show that the proposed MT attains significantly higher prediction accuracy with less time and model cost (RAM-Hours) than the other two algorithms.

Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination.

Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the next phenotypic feature to assess, optimizing the diagnostic pathway to the smallest number of possible diagnoses. The objective of this study was to evaluate the performance of this real-time DSS using clinical data. This validation study was conducted on a database of 549 perinatal phenotypes collected from two referral centers (one in France and one in the UK). Inclusion criteria were: at least one anomaly was visible on fetal ultrasound after 11 weeks' gestation; the anomaly was confirmed postnatally; an associated rare disease was confirmed or ruled out based on postnatal/postmortem investigation, including physical examination, genetic testing and imaging; and, when confirmed, the syndrome was known by the DSS software. The cases were assessed retrospectively by the software, using either the full phenotype as a single input, or a stepwise input of phenotypic features, as prompted by the software, mimicking its use in a real-life clinical setting. Adjudication of discordant cases, in which there was disagreement between the DSS output and the postnatally confirmed ('ascertained') diagnosis, was performed by a panel of external experts. The proportion of ascertained diagnoses within the software's top-10 differential diagnoses output was evaluated, as well as the sensitivity and specificity of the software to select correctly as its best guess a syndromic or isolated condition. The dataset covered 110/408 (27%) diagnoses within the software's database, yielding a cumulative prevalence of 83%. For syndromic cases, the ascertained diagnosis was within the top-10 list in 93% and 83% of cases using the full-phenotype and stepwise input, respectively, after adjudication. The full-phenotype and stepwise approaches were associated, respectively, with a specificity of 94% and 96% and a sensitivity of 99% and 84%. The stepwise approach required an average of 13 queries to reach the final set of diagnoses. The DSS showed high performance when applied to real-world data. This validation study suggests that such software can improve perinatal care, efficiently providing complex and otherwise overlooked knowledge to care-providers involved in ultrasound-based prenatal diagnosis. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Developing a supervised learning-based simulation method as a decision support tool for rebalancing problems in bike-sharing systems

Bike-Sharing Systems (BSSs) have exploded in popularity worldwide because of their beneficial impacts on traffic, pollution levels, and public health, which has resulted in moving toward a green city. The rebalancing problem, as one of the most important operational problems of such systems, deals with planning bike distribution at different stations. Regarding conducted studies, simulation models are the most common tool for analyzing BSSs and decision-making. This popularity is based on simulation’s capabilities in modeling complexities of systems and uncertainty. Despite their advantages, lack of quickness is a significant drawback of simulation-based methods, making them inefficient for real-time decision-making processes, especially in large-scale and complex systems. In this regard, this paper introduces a Supervised Learning-Based Simulation (SLBS) method as an alternative to the conventional simulation-based methods dealing with rebalancing problems. SLBS is a huge step toward developing a real-time Decision Support System (DSS) for BSSs. For developing SLBS, firstly, we have developed a simulation model based on real-world assumptions of station-based BSSs and big data analysis of CitiBike, a well-known BSS located in New York City. The simulation model is able to calculate the number of unsatisfied demands (either number of failed pick-ups (FPs) or failed drop-offs (FDs)) as a result of different rebalancing plans. Then, the developed simulation model was used to generate quality and quantity training datasets to train Machine Learning (ML) algorithms involved in SLBS. While these ML models are trained once, SLBS will be capable of predicting the number of unsatisfied demands without running highly time-intensive simulations replications. The results obtained from a wide range of conducted experiments indicate that SLBS, up to 300 times faster than simulation models, can provide predictions with over 90% of R2 Score.

Flood modelling for a real-time decision support system of the covered Lower Paillons River, Nice, France

Abstract Nice Metropolis in Alpes Maritimes, France is prone to flood. The city is crossed by the Lower Paillons River (LPR). Its discharge for a return period of 100 years is estimated at 794 m3/s. Part of the river is covered by 2 km. In addition, there are two retention storages in the river bed and a floodable road tunnel on the left bank. Due to the increase in urban development, flood management is challenging. An existing decision support system, Aquavar, uses DHI Mike tools to reproduce runoff for the Lower Var River in the same region. To extend this system to the LPR and reinforce flood management, a new modelling tool adapted to the characteristics of the LPR is needed. Consequently, this research utilizes the DHI MIKEPLUS tool to develop a 1D -2D coupled model for real-time flood management. The results demonstrate that flood events like those in 2017 and 2019 were correctly reproduced. The linear regression R2 is above 0.8 for all stations. It was also estimated that the covered river should stay clean to avoid widespread flooding in the urban area. Overall, the model is useful for simulating flow in real time and can help sustain urban development.

Plant Disease Detection and Classification Using Deep Learning

Abstract: Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for the identification of plant disease. This research proposes an intelligent method for plant disease classification using image processing techniques. The proposed method aims to assist farmers and experts in identifying and diagnosing plant diseases efficiently and accurately. The system first obtains images of the plant leaves from different perspectives and then preprocesses the images to enhance the quality and remove noise. The preprocessed images are then subjected to feature extraction using a deep convolutional neural network (CNN) model. The features extracted from the CNN are then fed into a classifier for the classification of plant diseases. The proposed method is evaluated using a dataset of plant images with three different types of diseases. The results obtained show that the proposed method achieves high accuracy in the classification of plant diseases, making it a useful tool for plant disease diagnosis and control. The proposed method can be integrated into a mobile application or web based platform for use by farmers and botanists.

Decision-making during urgent phase of a nuclear accident under extreme conditions

Disasters when occurs result in a disruption of the normal life of society or community and have a severe impact on human life, health, environment, and social and economic activities. Ionizing radiations are a concern associated with nuclear accidents by their very nature, and managing a nuclear catastrophe adds additional pressure on decision-makers to address the immediate and long-term effects of ionizing radiations. Decision-making is more difficult during the early stages of a nuclear disaster because of the high level of uncertainty in the situation and the unavailability of detailed information. We introduce a systematic methodology named ROSYNA, a real-time decision support system for a nuclear accident to be considered for such an uncertain situation. The core of this decision support system is use of a decision table specific to various situations by assessing the decision problem attributes. The prototype applicability of the method was implemented to the Fukushima situation and a comparison was made with HERCA-WENRA and the IAEA approaches. With due consideration of uncertainties regarding the situation during the early phase of a nuclear emergency, we recommend considering the implementation of protective measures by utilizing the ROSYNA philosophy in the decision-making process. The decision problem attributes, which are the key element of this methodology need to be assessed using professional judgment based on the nature of the accident, the information that can be gleaned from the nuclear facility, radiation monitoring, prior understanding of such occurrences and pre-established response arrangements.

JRODOS customization for Pakistan and assessment of the consequences of a hypothetical nuclear accident

Since the Chernobyl disaster in 1986, decision support systems and modelling tools have been utilized in response to nuclear and radiological emergencies. The java-based real-time online decision support system (JRODOS) is a decision support tool that can be utilised in response to an emergency in managing off-site radiological consequences. This article documents the customization and use of JRODOS for Pakistan. JRODOS was tailored to the local Pakistan conditions, and a case study of a theoretical nuclear power plant accident was used to assess JRODOS’s feasibility as a decision support tool. A worst-case probabilistic accident scenario was used to identify zones and areas where urgent protective actions, early protective actions and food restriction and other response actions could be required. The areas and distances identified for the implementation of protective and response actions for such a hypothetical accident were found to be in agreement with the emergency planning zones and distances suggested by the International Atomic Energy Agency (IAEA). Additionally, the implications of meteorological and source term input parameters on predicting the radiation doses to members of the public were investigated. It was identified that the output of such tools strongly depends on the availability and accuracy of the input parameters, such as radioactive release and meteorological data. Limitations and uncertainties associated with these tools need to be considered in deciding on protective and other response actions in response to a nuclear accident. As established by the IAEA, protective and other response actions need to be applied on a graded approach, taking into account the protection strategy and uncertainties and limitations in the available information and criteria, based on the conditions at the facility and off-site.