Decision Uncertainty Research Articles

‘Shades of grey’: a focus group study on diagnostic uncertainty among general practitioners using point-of-care ultrasound

Background Point-of-care ultrasound (POCUS) has long been a diagnostic tool in family medicine, although most Norwegian general practitioners (GPs) who use POCUS, scans infrequently. The broad scope of family medicine, the relatively low prevalence of illnesses and infrequent use of POCUS imply that GPs may experience diagnostic uncertainty regularly. Aim To explore how GPs perceived and managed diagnostic uncertainty when using POCUS. Design and setting A qualitative focus group study among Norwegian GPs using POCUS. Methods Four focus group discussions were conducted. Total number of participants were 21. The interview guide was piloted, the focus group discussions were audio-recorded and transcribed, and Systematic Text Condensation, a thematic cross-case analysis, was used to analyse the data. Results Diagnostic uncertainty using POCUS was considered as aligning to the general clinical uncertainties in family medicine, but there were also POCUS-specific uncertainties in clinical decision-making. We generated six themes: emotional, cognitive, and ethical uncertainty using POCUS, communicating uncertainty to patients, interaction with specialists when using POCUS, and coping strategies of participants. POCUS results were the only results the participants sometimes withheld when communicating with other specialists. POCUS itself stimulated a renewed interest in family medicine. Scanning enough patients was the recommended coping strategy. Conclusion POCUS-using GPs experienced diagnostic uncertainty when using POCUS that aligned with other diagnostic uncertainties they experienced in everyday practice. However, they did not treat the results like other findings, as the GPs at times withheld their POCUS findings when interacting with secondary care specialists. This requires further investigation.

A three-stage framework for optimal site selection of hybrid offshore wind-photovoltaic-wave-hydrogen energy system: A case study of China

The integration of offshore wind-photovoltaic-wave-hydrogen production is a sustainable energy development concept that could smooth the intermittency of renewable energy generation, reduce energy abandonment, and improve energy efficiency. However, the successful deployment of a hybrid offshore wind-photovoltaic-wave-hydrogen energy system is premised on the selection of an appropriate location, which necessitates a robust framework to support this decision. This study proposes a three-stage decision framework based on Geographic Information System and multi-criteria decision-making to determine the optimal location from macro and micro perspectives. To ensure the effectiveness of site selection, it develops a comprehensive criteria system, including exclusion criteria and evaluation criteria, encompassing geographical, resource, economy, risk, and sustainable potential aspects. Then, the spatial simulation of alternative sites is conducted according to exclusion criteria to rule out infeasible alternative sites. The multi-criteria decision-making in the third stage is performed in a rough-fuzzy environment to eliminate the uncertainty in decision-making. Finally, a case study is conducted on Hainan Island, China. The results show that the western sea area of Dongfang City is the optimal location to deploy the hybrid offshore wind-photovoltaic-wave-hydrogen energy system. The proposed framework offers a reference for practical offshore renewable energy project implementation.

Integrated fuzzy decision-making methodology with intuitionistic fuzzy numbers: An application for disaster preparedness in clinical laboratories

Society is on constant alert due to the increasing frequency and severity of Seasonal Respiratory Diseases (SRDs), posing significant challenges from both a humanitarian and public health perspective. The recent COVID-19 pandemic has tested the capacity of clinical laboratories to address seasonal infections, epidemic outbreaks, and critical emergencies. This scenario has led to operational burdens, primarily from resource limitations, a lack of proactive planning, and the low adaptation to unforeseen circumstances. Coupling different data-driven approaches considering multi-criteria weighting, interdependence assessment, and outranking are critical for devising effective interventions upgrading the operability of clinical labs during SRDs. Nonetheless, a deep literature review revealed there are no studies using these hybridized approaches when addressing this problem. Consequently, this article proposes the application of an innovative hybrid Multicriteria Decision-Making (MCDM) methodology that integrates the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), and Combined Compromise Solution (CoCoSo) to assess the disaster preparedness of clinical laboratories during SRDs. Initially, we applied IF-AHP to assign the relative weights to criteria and sub-criteria, considering the inherent hesitation and uncertainty in decision-making. Subsequently, IF-DEMATEL was utilized to analyze the interrelationships between criteria, providing insights into the interrelations among clinical lab disaster management drivers. Finally, the CoCoSo method was applied to estimate each lab’s Preparedness Index (PI) and detect response gaps when coping with SRDs. The suggested methodology was validated across nine clinical laboratories in Colombia during the most recent respiratory pandemic. This study contributes to the healthcare sector authorities by identifying key criteria and sub-criteria affecting the response of clinical labs, the elicitation of main response drivers in clinical labs when facing SRDs, and the calculation of a multidimensional indicator representing the preparedness of the labs. This work also enriches the literature by applying the IF-AHP, IF-DEMATEL, and CoCoSo approach to a challenging case study requiring a multi-method data-driven application. Furthermore, it suggests future directions to improve the proposed framework in other related contexts.

Metformin in the Diabetes Prevention Program 3-year trial: The cost-effectiveness that never was.

To highlight oversimplified quality-adjusted life year (QALY) calculations and incremental cost-effectiveness ratios (ICERs) regarding lifestyle metformin and placebo as flaws in the trial-based (Diabetes Prevention Program) cost-effectiveness analysis. We revised the QALY calculations to conform to convention and calculated appropriate ICERs using both original and revised QALYs results. We used several additional health economics tools to present results, showing the consistency of each method and the added value of each. We presented net monetary benefits, assessed decision uncertainty and calculated net losses. We also used graphics, making correct interpretation clearer and allowing illustration and calculation of technical and economic inefficiencies of treatments that were not cost-effective. ICERs with either QALY calculation indicated that metformin was technically and economically inefficient and not cost-effective. There was virtually no decision uncertainty. All methods employed pointed to an identical conclusion. In contrast to original claims, although lifestyle was cost-effective in diabetes prevention, metformin was not, and acting otherwise imposes significant monetary and health costs. Various available tools of economic evaluation would probably have prevented the original misinterpretation by the authors had they been used. The varied tools implemented here illustrate with a common example their consistency and value to the field, showing how to depict the results in various ways.

An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.

How Do Paediatricians Manage Comfort with Uncertainty in Clinical Decision-Making.

While healthcare practice is inherently characterised by uncertainty, there is a paucity of formal curricular training to support comfort with uncertainty (CWU) in postgraduate training. Indeed, some evidence suggests medical training inherently conflicts with CWU in emphasizing pedagogies focussing on "fixing" the problem. While referral patterns increase significantly, dealing with uncertainty has direct implications for patient referral rates and use of valuable healthcare resources. Paediatricians in Ireland were invited to participate. Face-to-face interviews were conducted after participants watched videos of varied clinician-patient interactions.. Two researchers independently analysed the collected data using thematic analysis. Triangulation and member checking was performed to ensure validity of findings. A reflection journal documented the research journey. Thirty four paediatricians participated. Five themes were identified: the interplay between quality of information, uncertainty and decision-making, confidence in clinical assessment and first-hand patient evaluation, anxiety and fear experienced by medical professionals when dealing with complex and serious conditions, strategies employed by medical professionals in managing their own uncertainty and the impact of societal and parental expectations on medical decision-making. These are moderated by a number of factors, most significantly the child's caregivers' comfort with doctors reassurance (CDR). Enacted management will diverge from the consultant's clinical plan when the caregiver's CDR cannot be satisfactorily supported. Clinician CWU in the paediatric context is inextricably linked to caregiver CDR. The complexities and central importance of social context in understanding CWU has important implications for how we develop educational activities to support clinician CWU and patient/care-giver CDR. This may translate to efficient use of limited resources in healthcare settings.

Adoption of Lean Construction and AI/IoT Technologies in Iran’s Public Construction Sector: A Mixed-Methods Approach Using Fuzzy Logic

The construction sector in Iran faces substantial inefficiencies, including high material wastage, posing environmental and economic risks. This study investigated the adoption of Lean Construction (LC) practices and AI/IoT technologies in Iran’s public construction sector using a mixed-methods approach. This research examined the organizational, technical, and infrastructural factors across four key provinces—Tehran, Isfahan, Khorasan Razavi, and Fars—and employed fuzzy logic to address the uncertainties in adoption decisions. Data from 28 key stakeholder interviews were analyzed using Python 3.9, with libraries such as Pandas 1.3.3, NumPy 1.21.2, and skfuzzy 0.4.2 for the statistical analysis and NVivo 12 for the thematic coding. The analysis revealed that organizational readiness and leadership support were the critical drivers of adoption, particularly in Isfahan and Khorasan Razavi, which exhibited the highest adoption likelihood scores (0.5000). Tehran and Fars showed slightly lower scores due to regulatory barriers and financial limitations. The findings highlight the need for targeted leadership training, regulatory reforms, and infrastructure investments to accelerate the adoption of these technologies. This study aligned with the Sustainable Development Goals (SDG 9: Industry, Innovation, and Infrastructure and SDG 11: Sustainable Cities and Communities) by offering practical recommendations for advancing sustainable practices in Iran’s construction sector. The insights provided have broader implications for other developing economies facing similar challenges, contributing to global efforts toward sustainable development.

Penentuan Penentuan Prioritas Lokasi Fasharkan TNI AL dengan Metode Fuzzy Analytical Hierarchy Process dan Geographic Information System

Based on the regulation of the Navy Chief of Staff No. Per Kasal/39/V/2009 dated May 26, 2009 concerning the development policy of the Navy towards minimum essential force, namely the preparation of the Navy's combat power is directed to achieve minimum essential force or abbreviated as MEF. According to the Chief of Staff of the Navy Number Kep/1771 / XII / 2013 concerning the Administrative Guidebook for the Standardization of TNI AL Bases (PUM-7.03) that a TNI AL Main Base (LANTAMAL) must have a class A Fasharkan, whose ability is to be able to carry out maintenance and repair up to the depot level for all types of KRI, both ship buildings, machinery, sewaco, electricity and ship armaments. There are many studies of location prioritization using the Analytical Hierarchy Process (AHP) method, but the results have uncertainty or decision uncertainty. So this research was conducted using the Fuzzy AHP method to overcome uncertainty or decision uncertainty. The Fuzzy AHP method is used for calculations to select alternatives. After the alternative is selected, then verification of location feasibility is carried out using the Geographic Information System (GIS) display. From the calculation using the FAHP method, Lantamal IX Ambon was selected as an alternative with a weight value of 0.251630342. While verifying the feasibility of the location using GIS, the Tawiri location was chosen as a more feasible land compared to other land options.

Evolutionary Perspective on Tourists’ Conformity to the Minority: The Effect of Mating Motive

Tourists tend to conform to the majority when faced with uncertainty in decision-making, which poses a considerable challenge to the promotion of new, less popular tourism offerings. This study delves into the heuristic promotion of less popular tourism offerings by using the concept of mating motives from the field of evolutionary psychology. Findings show that tourists driven by mating motives are likely to follow the minority, rather than the majority, when making purchase decisions—particularly in the context of souvenir shopping, destination selection, and gift giving—with uniqueness seeking playing a mediating role. Although purchase risk can increase the tendency of tourists to conform to the majority, it does not mitigate the effect of mating motives. The findings contribute to the promotion of less popular tourism offerings by highlighting tourists’ uniqueness seeking. This study broadens knowledge on tourist conformity behavior from the theoretical perspective of evolutionary psychology.

Focal Unspecific Bone Uptake on [18F]PSMA-1007 PET: Evaluation Analog PROMISE Criteria and Validation via PET/CT Follow-Up.

Background: Focal unspecific bone uptake (UBU) is common in [18F]PSMA-1007 PET/CT, yet its clinical significance remains unclear, causing uncertainty in treatment decisions. Material and Methods: We retrospectively analyzed 99 prostate cancer patients (age 69 ± 7) who underwent [18F]PSMA-1007 PET/CT scans (3 MBq/kg; uptake time 70 ± 14 min) for staging and follow-up (after 13.0 ± 7.2 months). Semiquantitative assessment using the miPSMA score, analogous to the PROMISE criteria, evaluated the prevalence of UBU and bone metastases. Results: In the initial PET/CT scan, 56 patients had 230 lesions classified as UBU. A total of 19 patients were found to have bone metastases and UBU, while 24 patients had no focal bone uptake. UBU distribution was as follows: ribs (50%), spine (30%), pelvis (15%), and other sites (5%). There were no significant differences in age, Gleason score, injected tracer dose, uptake time, SUVpeak of UBU, or SUVmean in the spleen and parotid gland between patients with and without UBU. Follow-up showed stable miPSMA-score and CT appearance in 44/56 patients with UBU (79%), minor changes in 5/56 patients (8%), and new bone metastases in 7/56 patients (12%). Patient-specific analysis indicated at least one bone metastasis initially classified as UBU in 3/56 patients (5%) and new bone metastases in 4/56 patients (7%). In total, 4 of the 24 patients (17%) without initial focal uptake developed osseous metastases at follow-up. Conclusions: No significant differences were found between patients with or without UBU. Only a small portion of UBU (2%) evolved into metastases, a lower rate than the development of new osseous metastases, which appears to be independent of UBU.

Characterizing the progression from mild cognitive impairment to dementia: a network analysis of longitudinal clinical visits

BackgroundWith the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare.ObjectiveWe aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, of the participants in Mayo Clinic Study of Aging (MCSA) from initial mild cognitive impairment (MCI) diagnosis to dementia onset through network analysis.MethodsWe used the data from the MCSA, a prospective population-based cohort study of cognitive aging, and examined the changing association among variables (i.e., participants’ health conditions) from the first visit of MCI diagnosis to the visit of dementia onset using network analysis. The number of participants for this study are 97 with the number of visits ranging from 2 visits (30 months) to 7 visits (105 months). We identified the network communities among variables from three-fold collection of instances: (i) the first MCI diagnosis, (ii) progression to dementia, and (iii) dementia diagnosis. We determine the variables that play a significant role in the dementia onset, aiming to identify and prioritize specific variables that prominently contribute towards developing dementia. In addition, we explore the sex-specific impact of variables in relation to dementia, aiming to investigate potential differences in the influence of certain variables on dementia onset between males and females.ResultsWe found correlation among certain variables, such as neuropsychiatric symptoms and chronic conditions, throughout the progression from MCI to dementia. Our findings, based on patterns and changing variables within specific communities, reveal notable insights about the time-lapse before dementia sets in, and the significance of progression of correlated variables contributing towards dementia onset. We also observed more changes due to certain variables, such as cognitive and functional scores, in the network communities for the people who progressed to dementia compared to those who does not. Most changes for sex-specific analysis are observed in clinical dementia rating and functional activities questionnaire during MCI onset are followed by chronic diseases, and then by NPI-Q scores.ConclusionsNetwork analysis has shown promising potential to capture significant longitudinal changes in health conditions, spanning from the MCI diagnosis to dementia progression. It can serve as a valuable analytic approach for monitoring the health status of individuals in cognitive impairment assessment. Furthermore, our findings indicate a notable sex difference in the impact of specific health conditions on the progression of dementia.

Empowering decentralized identity systems for Web 3.0 in complex spherical fuzzy knowledge

The Web 3.0 network system, the next generation of the world wide web, incorporates new technologies and algorithms to enhance accessibility, decentralization, and security, mimicking human comprehension and enabling more personalized user interactions. The key component of this environment is decentralized identity management (DIM), embracing an identity and access management strategy that empowers computing devices and individuals to manage their digital personas. Aggregation operators (AOs) are valuable techniques that facilitate combining and summarizing a finite set of imprecise data. It is imperative to employ such operators to effectively address multicriteria decision-making (MCDM) issues. Yager operators have a significant extent of adaptability in managing operational environments and exhibit excellent effectiveness in addressing decision-making (DM) uncertainties. The complex spherical fuzzy (CSF) model is more effective in capturing and reflecting the known unpredictability in a DM application. This research endeavors to enhance the DM scenario of the Web 3.0 environment using Yager aggregation operators within the CSF environment. We present two innovative aggregation operators, namely complex spherical fuzzy Yager-ordered weighted averaging (CSFYOWA) and complex spherical fuzzy Yager-ordered weighted geometric (CSFYOWG) operators. We elucidate some structural characteristics of these operators and come up with an updated score function to rectify the drawbacks of the existing score function in the CSF framework. By utilizing newly proposed operators under CSF knowledge, we develop an algorithm for MCDM problems. In addition, we adeptly employ these strategies to handle the MCDM scenario, aiming to identify the optimal approach for ensuring the privacy of digital identity or data in the evolving landscape of the Web 3.0 era. Moreover, we undertake a comparative study to highlight the veracity and proficiency of the proposed techniques compared to the previously designed approaches.

Novel decision making approach for sustainable renewable energy resources with cloud fuzzy numbers

Decision making approaches depending on the assessments of individual decision makers produce inaccurate results due to the existence of multiple uncertainties. To model intrapersonal uncertainty, interpersonal uncertainty and randomness in decision making assessments, this research study proposes a novel approach by integrating linear and non-linear type of fuzzy numbers with cloud model theory using novel technique of computing entropy of these fuzzy numbers. A novel mathematical model known as cloud fuzzy numbers is introduced using the concepts of fuzzy numbers and cloud theory. The self-evaluated relative weights of experts are computed using a non-linear optimization method which is based on maximum deviation method and Lagrange multipliers of cloud fuzzy numbers. The new cloud fuzzy numbers are then combined with CODAS (combinative distance based assessment) approach that is based on the largest Euclidean and Taxicab distances for the selection of suitable criteria. Firstly, the linguistics evaluations are converted into the fuzzy numbers and then cloud fuzzy numbers using formulae of expectation and entropy ensuring that the obtained interval cloud values follows a normal distribution. Secondly, the cloud fuzzy weighted arithmetic averaging operator is used to aggregate cloud fuzzy numbers using the self-evaluated fuzzy weights Thirdly, the assessment score is determined to rank the alternatives by computing the distance between normalized weighted matrix and the negative ideal solution. Finally, a case study is discussed for the selection of best renewable energy resource in Turkey to elaborate the significance of the proposed research. The convergence and accuracy of the proposed model is proved with certain mathematical and theoretical results.

A neural code supporting prospective probabilistic reasoning for instrumental information demand in humans

When making adaptive decisions, we actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain prospectively estimates the information gains that are expected to accrue from various sources by integrating simpler quantities of prior certainty and the reliability (diagnosticity) of a source. We examine this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently in the rewards, decision uncertainty, and information diagnosticity. We show that, consistent with value of information theory, the participants’ bids are sensitive to prior certainty (the certainty about the correct choice before gathering information) and expected posterior certainty (the certainty expected after gathering information). Expected posterior certainty is decoded from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation is independent of instrumental rewards and spatially overlaps with distinct representations of prior certainty and expected information gains. The findings suggest that the posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to anticipating information gains during active gathering of instrumental information.

Detection and Coil Embolisation of Pelvic Vein Incompetence for Chronic Pain in the Female Pelvis: A Development Phase Economic Analysis

Unexplained chronic pain in the female pelvis (CPFP) affects 7% of people indicating female sex in the UK. Evidence suggests that pelvic venous incompetence (PVI) could explain CPFP and that coil embolisation could provide relief. The aims of this study were to indicate (1) the cost effectiveness of detecting and treating PVI in people experiencing unexplained CPFP, and (2) the maximum value of further research, suggesting suitable areas. A decision tree compared standard care (regular prescribed analgesia) with an intervention comprising transvaginal duplex ultrasound to screen for PVI, venography to confirm the diagnosis, and coil embolisation treatment. The population was people experiencing unexplained CPFP. A UK National Health Service perspective and 2021 - 22 price year were used. Ten years of health costs and health related quality of life (HRQoL) effects for eligible 40 year olds were simulated. Evidence reviews informed diagnostic accuracy, health service usage, and unit costs. A single centre randomised controlled trial informed all other parameters. Probabilistic analysis incorporated parameter uncertainty in cost effectiveness estimates. Deterministic sensitivity analysis indicated drivers of uncertainty. Value of information methods measured the value of eliminating all relevant uncertainties, given uptake predictions. The main outcome measures were incremental cost and quality adjusted life years (QALYs) for the intervention compared with analgesia, the incremental cost effectiveness ratio (ICER), and expected value of perfect information. The mean ICER for the intervention was £4 558/QALY gained, and the probability that the ICER was within the UK cost effectiveness threshold (£20 000/QALY gained) was 90%. The expected value of perfect information about all model parameters was £46 M. All deterministic sensitivity analysis scenarios met the threshold, except the smallest plausible HRQoL effect of (resolving) CPFP. Detecting and treating PVI causing CPFP appears cost effective, but more primary research would be valuable to reduce decision uncertainty. Uncertainty in the HRQoL estimate for unexplained CPFP appeared to contribute most to decision uncertainty.

Are ESG scores driven by financial information? Evidence from European banks

In recent years, investors' increasing focus on sustainable investments and the sustainability orientation of companies has led to parallel growth in the market for environmental, social and governance (ESG) performance and ESG rating agencies. However, even though ESG rating agencies have become very influential institutions, the literature has found that ESG performance ratings provided by different agencies often differ from each other. This causes consequences that should be considered, such as complex evaluation of companies' ESG performance and uncertainty in ESG investment decisions. Therefore, it is necessary to identify which determinants influence ESG performance. This study aims to identify the internal determinants of an ESG score using bank-specific balance sheet indicators such as capital and risk ratios. The analysis focuses on the European banking sector from 2018 to 2022. Banks mainly foster the transition to a more inclusive and sustainable economy. Moreover, after the recent financial crises, banks have increased their social responsibility practices, strengthening their credibility, trust and reputation. Generalised estimating equations with standard error robust to heteroscedasticity were used. The results reveal that the factors that most influence the ESG score provided by ESG rating agencies are bank size and liquidity risk exposure. The larger the size of the bank and the lower the exposure to liquidity risk, the higher the ESG score assigned.

Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.

The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.

Fuzzy Analytic Hierarchy Process (FAHP) Based Performance Assessment of Textile Industry in Tirupur

Abstract: This study explores the use of Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and prioritize criteria in supply chain management within the textile industry of Tirupur district. Utilizing a detailed questionnaire reviewed by experts with over 30 years of industry experience, the study conducted pairwise comparisons of key criteria: cost (C1), quality (C2), time delivery (C3), and management and organization (C4). By applying FAHP, which effectively handles uncertainties in decision-making, the study determines the relative importance of these criteria. The findings provide a structured method for improving decisionmaking in textile supply chain operations, integrating fuzzy logic with the traditional AHP framework to enhance the management and optimization of supply chain processes.

Clinical decision making under uncertainty: a bootstrapped counterfactual inference approach

BackgroundLearning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization.MethodWe develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization.ResultsThe efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making.ConclusionThis study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.

Machine learning embedded hybrid MCDM model to mitigate decision uncertainty in transport safety planning for OAS countries

Providing defensible decisions is a prerequisite for methodologies of multi-criteria decision-making (MCDM) activities, and this is especially true for socio-economic analysis in public sector. This study proposes an all-in-one MCDM model with machine learning algorithms. The model integrates the method based on the removal effects of criteria (MEREC), combined compromise solution (CoCoSo), and density-based spatial clustering of applications with noise (DBSCAN), i.e., MEREC–CoCoSo–DBSCAN. In particular, the uniform manifold approximation and projection (UMAP) is implanted in DBSCAN to reduce the data dimensionality, and the k-nearest neighbors (KNN) algorithm is embedded to determine the inflection points (ɛ) and minPts in the data. This counters the inherent model failure of DBSCAN in dealing with high-dimensional data and eliminates the requirement for manual intervention in the model procedure, thereby fully avoiding potential human error and automating the computing process. A case study on benchmarking transport safety systems for member countries of the Organization of American States (OAS) demonstrates the reliability, adaptability, and efficiency of the proposed model. It moreover reflects its feasibility in resolving real-life socio-economic issues by offering valuable insights and potential solutions in economic investment and funding allocation in regard to transport safety strategy. Overall, this study provides government officials, managers, and policymakers with a valuable tool for handling MCDM activities in socio-economic development with considerable practicality and credibility.