Decision-making Model Research Articles

Presentation and physical therapy management using a neuroplasticity approach for patients with hypermobility-related upper cervical instability: a brief report

BackgroundUpper cervical instability (UCI) is a potentially disabling complication of the connective tissue disorders hypermobile Ehlers-Danlos Syndrome and Hypermobility Spectrum Disorders (hEDS/HSD). UCI can impact various neurological structures, including the brainstem, spinal cord, cranial nerves, and blood supply to and from the brain, resulting in complex neurological signs and symptoms in this population. The current study was an observational study applying recent expert consensus recommendations for physical therapy assessment and management of patients with UCI associated with hEDS/HSD.MethodsThis was a retrospective observational study describing how the clinical decision-making model was used to screen, examine, and treat three patients with highly irritable hEDS/HSD-related UCI, resulting in complex neurological presentation. The treatment used a neuroplasticity approach, including proprioception and motor control training emphasizing patient education and biofeedback. Outcome measures tracked progress.ResultsAll patients started with significant disability associated with UCI. One patient returned to full function with intermittent flares that he was able to manage. The second patient continued to have mild-moderate irritability but returned to parenting responsibilities and full-time work. The third patient required cervical fusion and remained disabled but was better able to minimize flares. The number of initial red and yellow flags was associated with the final outcomes, suggesting that the decision-making model might be useful for predicting patient prognosis.ConclusionThis brief report applies recent recommendations for safely evaluating and managing hypermobility-related UCI and provides a first step in experimental studies to test both the assessment and physical therapy treatment approaches.

THE APPLICATION OF DATA MINING AND OLAP TECHNOLOGIES IN HEALTHCARE

The article examines the application of Data Mining and real-time Online Analytical Processing (OLAP) in healthcare. The calculation of analytical indicators and solving planning problems in healthcare are complex and multi-stage processes. The extraction of knowledge from large volumes of data accumulated in databases is referred to as Data Mining. Today, databases are measured in terabytes, containing strategically significant information. One of the most critical issues is determining methods for discovering important information within such vast amounts of data. Data Mining technology, by utilizing a set of analytical tools, helps to uncover relationships within large datasets and make predictions about future trends. Simply defined, Data Mining automatically identifies relevant patterns within databases. For many years, analysts manually recorded statistically significant relationships within databases. Today, Data Mining automates this process. The goal of Data Mining is to create decision-making models that predict future behavior based on the analysis of past actions. With the advent of computers and the rise of the digital information age, there have been changes in both the scale and complexity of existing problems in terms of analytical analysis. Advances in the storage, management, and examination of data have led to the emergence of a new field that promotes the development of bioinformatics and introduces analytical analysis and computational problems in the fields of biology and medicine. Keywords: Data Mining, OLAP, database, factor analysis, healthcare network, multidimensional models.

Artificial Trust in Mutually Adaptive Human-Machine Teams

As machines' autonomy increases, their capacity to learn and adapt to humans in collaborative scenarios increases too. In particular, machines can use artificial trust (AT) to make decisions, such as task and role allocation/selection. However, the outcome of such decisions and the way these are communicated can affect the human's trust, which in turn affects how the human collaborates too. With the goal of maintaining mutual appropriate trust between the human and the machine in mind, we reflect on the requirements for having an AT-based decision-making model on an artificial teammate. Furthermore, we propose a user study to investigate the role of task-based willingness (e.g. human preferences on tasks) and its communication in AT-based decision-making.

Multi-attribute decision-making model based on Remote Sensing to enhance the environmental auditing of Sustainable Forest Management in the Brazilian Amazon forest

Multi-attribute decision-making model based on Remote Sensing to enhance the environmental auditing of Sustainable Forest Management in the Brazilian Amazon forest

A thematic analysis of shared decision-making in consultations with patients with a presumed brain tumor and neurosurgeons

Abstract Background Patients with a brain tumor, family members and healthcare professionals face complex healthcare decisions that can significantly impact everyday life. Engaging in shared decision-making (SDM) can help ensure treatment decisions align with patients’ preferences and goals. Little is known about the decision-making process in neurosurgical consultations. This study aims to qualitatively explore and describe the SDM process during neurosurgical consultations for patients with a presumed brain tumor. Methods The first consultation between patients with a presumed glioma or meningioma and their neurosurgeons was audio-recorded and transcribed. Decision-making models were used as sensitization concepts, with coding carried out inductively. We employed a reflexive thematic analysis to develop themes and sub-themes until saturation was beginning to emerge. Results Analyzing eleven consultations, we developed three main themes and twelve sub-themes. The results depict decision-making guided by neurosurgeons (theme 1), with varying degrees of engagement from patients and family members (theme 2) and personalization of the decision-making process based on the medical situation and everyday life priorities (theme 3). The consultations lacked specific attention to everyday life priorities of the patients. Conclusions The decision-making processes seemed to focus primarily on medically informing patients with a presumed brain tumor, in line with IDM. However, less emphasis was placed on learning more about the patient’s everyday life and preferences to integrate into the decision-making process, which is key to SDM. Incorporating patient goals may lead to greater personalization and decisions that align with both the patient’s medical situation and their goals.

Enhancing Coronary Revascularization Decisions: The Promising Role of Large Language Models as a Decision-Support Tool for Multidisciplinary Heart Team.

While clinical practice guidelines advocate for multidisciplinary heart team (MDHT) discussions in coronary revascularization, variability in implementation across health care settings remains a challenge. This variability could potentially be addressed by language learning models like ChatGPT, offering decision-making support in diverse health care environments. Our study aims to critically evaluate the concordance between recommendations made by MDHT and those generated by language learning models in coronary revascularization decision-making. From March 2023 to July 2023, consecutive coronary angiography cases (n=86) that were referred for revascularization (either percutaneous or surgical) were analyzed using both ChatGPT-3.5 and ChatGPT-4. Case presentation formats included demographics, medical background, detailed description of angiographic findings, and SYNTAX score (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery; I and II), which were presented in 3 different formats. The recommendations of the models were compared with those of an MDHT. ChatGPT-4 showed high concordance with decisions made by the MDHT (accuracy 0.82, sensitivity 0.8, specificity 0.83, and kappa 0.59), while ChatGPT-3.5 (0.67, 0.27, 0.84, and 0.12, respectively) showed lower concordance. Entropy and Fleiss kappa of ChatGPT-4 were 0.09 and 0.9, respectively, indicating high reliability and repeatability. The best correlation between ChatGPT-4 and MDHT was achieved when clinical cases were presented in a detailed context. Specific subgroups of patients yielded high accuracy (>0.9) of ChatGPT-4, including those with left main disease, 3 vessel disease, and diabetic patients. The present study demonstrates that advanced language learning models like ChatGPT-4 may be able to predict clinical recommendations for coronary artery disease revascularization with reasonable accuracy, especially in specific patient groups, underscoring their potential role as a supportive tool in clinical decision-making.

Shared decision-making with patients with complex care needs: a scoping review

BackgroundA number of patients have complex care needs that arise from interactions among multiple factors, such as multimorbidity, mental health issues, and social vulnerability. These factors influence decisions about healthcare and health services. Shared decision-making (SDM), a collaborative process between patients and professionals, is known to improve the quality of the decision-making process. However, follow-up challenges of patients with complex care needs (PCCNs) can lead to SDM specificities.ObjectiveTo identify specificities of SDM with PCCNs.MethodsWe conducted a scoping review using the Joanna Briggs Institute (JBI) methodology. We conducted a systematic search across MEDLINE, CINAHL, PsycINFO, and Academic Search Complete databases. Empirical studies about SDM with PCCNs published between 1997 and 2023 were eligible for inclusion. We conducted a mixed thematic analysis using deductive (Ottawa Decision Support Framework and Interprofessional Shared Decision-Making Model) and inductive approaches. Following Arksey & O’Malley’s and Levac et al.’s methodological recommendations, we consulted experts (researchers, healthcare professionals, and patient partners) to enhance the findings.ResultsTwelve studies were included in the review. Overall, our results demonstrated the importance of recognizing some specificities of SDM with PCCNs, such as the simultaneous presence of multiple decisions and the multidisciplinary and intersectoral nature of the healthcare and health services they receive.ConclusionThis scoping review highlights some specificities that must be considered in SDM with PCCNs to maintain its already-known benefits and ensure positive health and decision-making outcomes.

Computational Math Modeling and AI Optimization for Better Decision-Making: Applications in Machine Learning

The integration of computational mathematical modeling, optimization, machine learning (ML), data analysis, and artificial intelligence (AI) offers transformative opportunities to solve complex problems across diverse fields. This paper delves into the development and application of advanced modeling techniques that blend traditional mathematical approaches with advanced AI algorithms to enhance predictive accuracy, optimize resource allocation, and improve decision-making processes in areas such as logistics, finance, engineering, and healthcare. By leveraging data-driven insights, these models can simulate real-world scenarios and identify optimal solutions more effectively than conventional methods. The paper also explores how AI-enhanced modeling can impact broader systems by reshaping industry practices, influencing frameworks, and potentially challenging established societal norms. Eventually, the paper argues for a balanced and informed deployment of AI-driven modeling techniques to maximize their benefits while addressing potential risks and limitations.

Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information

In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic q-rung orthopair fuzzy (DHLq-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank t-norms and t-conorms are defined for DHLq-ROF numbers. A wide range of generalized aggregation operators, such as DHLq-ROF Frank weighted averaging, DHLq-ROF Frank weighted geometric, DHLq-ROF Frank generalized weighted averaging, and DHLq-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.

Pricing and Financing Strategies in a Dual-Channel Low-Carbon Supply Chain for Bilateral Capital-Constrained Retailers

In the context of low-carbon economy, financial constraints are prevalent among both upstream and downstream enterprises, exacerbated by the trend of online retailers extending into offline physical stores. This study first proposes and evaluates three financing combination models: “bank lending + advance payment,” (in short, AF), “bank lending + delayed payment,” (in short, DF), and “bilateral bank lending.” (in short, TF). It then examines the retailer’s dual-channel supply chain pricing and financing decision-making models under unified pricing strategy and independent self-pricing strategy respectively. The evaluation results indicate that among various financing models, the independent self-pricing strategy has the potential to enhance the retailer's profitability, outperforming the unified pricing strategy. In addition, when channel preference is significant, the retailer should prioritize the independent self-pricing strategy. A comparison of the optimal decisions across the three financing combination models indicates that both the cost coefficient of carbon emission reduction and the loan interest rate exert a limiting influence on the decision-making and income of all supply chain parties. Furthermore, the DF model emerges as a financing equilibrium strategy within the supply chain, with a shift to the AF model being considered only when the manufacturer’s loan interest rate is relatively high.

Shared decision making in primary malignant bone tumour surgery around the knee in children and young adults: protocol for a prospective study

BackgroundChildren and young adults needing surgery for a primary malignant bone tumour around the knee face a difficult, life-changing decision. A previous study showed that this population wants to be involved more in the decision-making process and that more involvement leads to less decisional stress and regret. Therefore, a well-designed and standardized decision-making process based on the principles of shared decision-making needs to be designed, implemented, and evaluated.MethodsWe developed a shared decision-making (SDM) model for this patient population, including an online decision aid (DA). This model has been implemented in the standard care of patients with a primary malignant bone tumour around the knee. Following implementation, we will analyse its effect on the decision-making process and the impact on patient experiences using questionnaires and interviews. Moreover, potential areas for improvement will be identified.DiscussionGiven the importance of involving patients and parents in surgical decision-making, particularly in life-changing surgery such as malignant bone tumour surgery, and given the lack of SDM models applicable for this purpose, we want to share our model with the international community, including our study protocol for evaluating and optimising the model. This study will generate valuable knowledge to facilitate the optimisation of current patient care for local treatment. The sharing of our implementation and study protocol can serve as an example for other centres interested in implementing SDM methods in an era characterized by more empowered patients and parents who desire autonomy and reliable and realistic information.

A Decision Model for Ship Overtaking in Straight Waterway Channels

Overtaking situations are commonly encountered in maritime navigation, and the overtaking process involves various risk factors that significantly contribute to collision incidents. It is crucial to conduct research on the maneuvering behaviors and decision-making processes associated with ship overtaking. This paper proposes a method based on the analysis of ship maneuvering performance to investigate overtaking behaviors in navigational channels. A relative motion model is established for both the overtaking and the overtaken vessels, and the inter-vessel distance is calculated, taking into account the psychological perceptions of the ship’s driver. A decision-making model for ship overtaking is presented to provide a safety protocol for overtaking maneuvers. Applying this method to overtaking data from the South Channel shows that it effectively characterizes both the permissible overtaking space and the driver’s overtaking desire. Additionally, it enables the prediction of optimal overtaking timing and strategies based on short-term trajectory forecasts. Thus, this method not only offers a safe overtaking plan for vessels but also provides auxiliary information for decision making in intelligent ship navigation.

Coupling Coordination and Data Management for Additive Manufacturing Systems Based on Smart Logistics

Additive Manufacturing (AM) has rapidly advanced due to its benefits in reducing material consumption and resource requirements, offering significant opportunities for energy-efficient and environmentally friendly production. As manufacturing technologies evolve, there is growing interest in smart manufacturing, prompting both academia and industry to explore ways to enhance efficiency and sustainability. Effective logistics management plays a crucial role in this context, requiring precise data prediction and optimization. This article introduces a Smart Logistics service to foster dynamic coordination within the smart logistics ecosystem. The first step is to integrate smart logistics components that can communicate effectively and provide specific value-added services. This integration aims to support contemporary industrial and production environments by enhancing decision-making models. The second step involves applying the Coupling Coordination model to assess the development of logistics in conjunction with broader economic growth. This model helps in constructing a high-quality development index system for logistics, which is essential for evaluating the efficiency of logistics operations. The third step is to evaluate the coordination between the automotive, digitalization, and logistics sectors. This assessment provides insights into the overall development levels of cities and highlights the significant impact on sustainable growth within the automotive industry. The final step includes analyzing the progress of coupling coordination in Indian provinces, identifying the transition from minor imbalances to primary coordination, and forecasting that strong coordination will be achieved by 2025. This analysis underscores the potential for mutual growth and cooperation by elevating the logistics sector’s standards and strengthening inter-industry relationships.

A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights

A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights

Design of a smart medical service quality evaluation system based on a hybrid multi-criteria decision model

As medical informatization continues to progress, the sophistication of smart medicine (SM) systems has led to a marked enhancement in the caliber of medical services provided. Nevertheless, the existing body of literature is sparse when it comes to frameworks for evaluating service quality, specifically within the domain of smart medicine (SM). Drawing on a hybrid multi-criteria decision-making model that fuses the best–worst method (BWM) with the VIKOR approach, this research has crafted an innovative framework for assessing the service quality of SM. BWM was used to obtain the weights of all dimensions and indicators under each dimension. Then, the service quality of hospitals H1, H2 and H3 in Xiamen was evaluated using the VIKOR method. The results showed that smart appointments, diagnosis and treatment are three important dimensions to evaluate SM service quality in medical institutions. The stability and robustness of the model were proved through sensitivity analysis. Findings suggest that hospitals can strengthen the construction of their appointment information platforms, the quality management of internal doctors, and the information connection between self-service terminals and information platforms to improve hospital service quality in the construction of smart medicine.

Employing Convolutional Neural Networks And Explainable Artificial intelligence for the Detection of Seizures from Electroencephalogram Signal

A seizure is a rapid, uncontrolled burst of electrical activity in the brain. Epilepsy is a neurological disorder caused by repeated seizures. Effective management requires early detection. This research aims to create a convolutional neural network (CNN) and explainable artificial intelligence (XAI) integrated system for epileptic seizure detection, using techniques such as Shapley additive explanation (SHAP) for better interpretability. The relevant data is acquired from a variety of electroencephalogram (EEG) dataset to facilitate the development of the model. To identify the EEG data, the proposed system combines deep learning models with feature extraction technique. Model visualization and XAI approaches like feature importance analysis from SHAP values offer clear insights into the model's decision-making process. Evaluation criteria like specificity and accuracy are used to assess the models' performance. This framework's objective is to create simple seizure detection systems that assist early epilepsy patient identification and individualized treatment plans. This research aims in opening the door to improved patient care and treatment by developing the field of epilepsy seizure detection.

Large-Scale Group Decision Making with Dual Feedback from Community Residents Based on the Organizational Invisible Field

In China, communities function as grassroots self-governing bodies, and the enhancement of public participation in community governance has remained a central focus of study. This paper applies the Large-Scale Group Decision-Making (LSGDM) method to the process of community self-governance and proposes a dual feedback group consensus decision-making model that takes into account the unique social relations among residents. Firstly, the concept of the Organizational Invisible Field—formed in communities by intangible social capital such as positional power and interpersonal relationships within the organization—is introduced. The definition of Invisible Field Force is utilized to measure the influence of these forms of capital on social relationships. Subsequently, drawing on field dynamic theory, the process by which residents’ preferences within the organization are shaped by the action of Invisible Field Force is explored. Secondly, acknowledging that invisible relationships can be affected by dynamic interactions during the decision-making process, the Invisible Field Force change model is constructed. Building on this, a dual feedback consensus coordination mechanism—encompassing both in-organization members and all residents—is designed. Finally, the validity and utility of the model are verified through case studies and sensitivity analyses.

Research on the influence of Spillover effect on dual-channel supply chain under the background of live-streaming marketing

In the context of live streaming marketing, it is crucial for members of the supply chain to allocate resources not only to enhance product quality through quality improvement efforts but also towards marketing initiatives aimed at boosting sales. This paper focuses on a dual-channel supply chain comprising a brand vendor and a streamer and investigates the spillover effect of the live streaming channel on traditional channels and product goodwill. By analyzing the Stackelberg game and differential game, this study compares the optimal equilibrium strategy and equilibrium profit under two decentralized models: the brand vendor-dominated decentralized model and the streamer-dominated decentralized model, as well as the centralized decision-making model. Additionally, this paper presents the optimal strategy and profit function of the product goodwill trajectory for the brand vendor, streamer, and the entire supply chain, considering different spillover effects using numerical simulation. The analysis reveals that the level of quality improvement consistently increases over time, while the variation in product goodwill is more diverse. The optimal pricing of products is influenced by the market share of the channel with goods under the decision of the two decentralized models. Risk-averse brands tend to collaborate with waist and tail streamers to gain control over bargaining power, while risk-seeking brands often choose top streamers to distribute their goods when the market share of goods channels is significant. The overall profitability of the supply chain is influenced by consumers’ preferences for quality and marketing.

Human-like Decision Making for autonomous lane change driving: a hybrid inverse reinforcement learning with game-theoretical vehicle interaction model

The development of automated driving vehicles aims to provide safer, comfortable, and more efficient mobility options. However, the decision-making control of autonomous vehicles still embraces limitations on human performance mimicry. These limitations become particularly evident in complex and unfamiliar driving scenarios, where weak decisionmaking abilities and poor adaptation of vehicle behaviour are prominent issues. This paper proposes a game-theoretic decisionmaking algorithm for human-like driving in the vehicle lane change scenario. Firstly, an inverse reinforcement learning (IRL) model is used to quantitatively analyse the lane change trajectories of the natural driving dataset, establishing the human-like human cost function. Subsequently, joint safety, comfort to build the comprehensive decision cost function. Use the combined decision cost function to conduct a non-cooperative game of vehicle lane changing decision to solve the optimal decision of host vehicle lane changing. The host vehicle lane-changing decision problem is formulated as a Stackelberg game optimization problem. To verify the feasibility and effectiveness of the algorithm proposed in this study, a lane change test scenario has been established. Firstly, we analyse the human-like decision-making model derived by the maximum entropy inverse reinforcement learning algorithm to verify the effectiveness and robustness of the IRL algorithm. Secondly, the human-like game decisionmaking algorithm in this paper is validated by conducting an interactive lane-changing experiment with obstacle vehicles of different driving styles. The experimental results prove that the human-like driving decision-making model proposed in this study can make lane-changing behaviours in line with human driving patterns in lane-changing scenarios of expressway.

Patient specific numerical hemodynamics for postoperative risk assessment: series case study of EC-IC cerebral bypass

Abstract The study is devoted to the hemodynamics during cerebral vascular bypass surgery for the treatment of cerebral aneurysms in two patients. The location, morphological characteristics and treatment approaches of the patients were similar, but different outcomes were observed as a result of the performed microsurgical procedures . Computational approach was used to analyze the hemodynamic differences of aneurysms, treated via extra-intra cranial (EC-IC) cerebral bypass shunt. The paper presents a new criterion based on the energy parameters of healthy compartment of cerebral circulation. The applied approach demonstrates a new effective method of preoperative risk modelling for medical decision-making.