Identifying Bayesian optimal experiments for uncertain biochemical pathway models.

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Navigating the path towards successful implementation of the EU HTA Regulation: key takeaways from the 2023 Spring Convention of the European Access Academy.

BACKGROUND: The European Regulation on Health Technology Assessment (EU HTA R), effective since January 2022, aims to harmonize and improve the efficiency of common HTA across Member States (MS), with a phased implementation from January 2025. At "midterms" of the preparation phase for the implementation of the Regulation our aim was to identify and prioritize tangible action points to move forward. METHODS: During the 2023 Spring Convention of the European Access Academy (EAA), participants from different nationalities and stakeholder backgrounds discussed readiness and remaining challenges for the Regulation's implementation and identified and prioritized action points. For this purpose, participants were assigned to four working groups: (i) Health Policy Challenges, (ii) Stakeholder Readiness, (iii) Approach to Uncertainty and (iv) Challenges regarding Methodology. Top four action points for each working group were identified and subsequently ranked by all participants during the final plenary session. RESULTS: Overall "readiness" for the Regulation was perceived as neutral. Prioritized action points included the following: Health Policy, i.e. assess adjustability of MS laws and health policy processes; Stakeholders, i.e. capacity building; Uncertainty, i.e. implement HTA guidelines as living documents; Methodology, i.e. clarify the Population, Intervention, Comparator(s), Outcomes (PICO) identification process. CONCLUSIONS: At "midterms" of the preparation phase, the focus for the months to come is on executing the tangible action points identified at EAA's Spring Convention. All action points centre around three overarching themes: harmonization and standardization, capacity building and collaboration, uncertainty management and robust data. These themes will ultimately determine the success of the EU HTA R in the long run.

Uncertainties experienced by nursing professionals who contracted Covid-19: a priori, everyone is human.

OBJECTIVE: To analyze the uncertainties experienced by nursing professionals who contracted COVID-19. METHODS: This qualitative research was conducted with 20 nursing professionals who fell ill from COVID-19. Data collection was carried out through semi-structured interviews; the data were then organized using thematic analysis and discussed in the context of Merle Mishel's Reconceptualized of Uncertainty in Illness Theory. RESULTS: The antecedents of the disease had a strong influence on how nursing professionals who contracted COVID-19 perceived uncertainty. The media coverage of the increasing number of cases, the collapse of the healthcare system, and the high mortality rate contributed to associating the disease with fear and panic. FINAL CONSIDERATIONS: Viewing it from the perspective of the disease's antecedents, the illness of a nursing professional from COVID-19 underscores that before being professionals, they are human beings just like anyone else, undergoing adversities and facing the possibilities associated with being ill.

Intolerance of Uncertainty, Job Satisfaction and Work Performance in Turkish Healthcare Professionals: Mediating Role of Psychological Capital.

Objective: Psychological capital refers to internal resources including self-efficacy, hope, optimism and resilience to overcome adverse life events. The current study sought to examine the mediating role of psychological capital in the relationship between intolerance of uncertainty and job satisfaction and work performance in healthcare professionals. Methods: Participants were 302 healthcare professionals [48% females; M(SD) age = 34.0 (7.5)] and completed measures of intolerance of uncertainty, psychological capital, work performance, and job satisfaction. Results: The findings indicated that intolerance of uncertainty was negatively correlated with psychological capital, work performance, and job satisfaction, whereas psychological capital was positively correlated with job satisfaction and work performance. More importantly, the findings revealed that these relationships were mediated by psychological capital. Conclusion: The results provide several contributions that help to understand the role of psychological capital in the relationship between intolerance to uncertainty and job satisfaction and work performance.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

Exploring patient experiences of surveillance for pancreatic cystic neoplasms: a qualitative study.

BACKGROUND: Pancreatic cystic neoplasms (PCN) are considered premalignant conditions to pancreatic adenocarcinoma with varying degrees of cancerous potential. Management for individuals who do not require surgical treatment involves surveillance to assess for cancerous progression. Little is known about patients' experience and the impact of living with surveillance for these lesions. AIMS: To explore the experiences of patients living with surveillance for PCNs. METHODS: Semi-structured qualitative interviews were conducted with patients under surveillance for pancreatic cystic neoplasms in the UK. Age, gender, time from surveillance and surveillance method were used to purposively sample the patient group. Data were analysed using reflexive thematic analysis. RESULTS: A PCN diagnosis is incidental and unexpected and for some, the beginning of a disruptive experience. How patients make sense of their PCN diagnosis is influenced by their existing understanding of pancreatic cancer, explanations from clinicians and the presence of coexisting health concerns. A lack of understanding of the diagnosis and its meaning for their future led to an overarching theme of uncertainty for the PCN population. Surveillance for PCN could be seen as a reminder of fears of PCN and cancer, or as an opportunity for reassurance. CONCLUSIONS: Currently, individuals living with surveillance for PCNs experience uncertainty with a lack of support in making sense of a prognostically uncertain diagnosis with no immediate treatment. More research is needed to identify the needs of this population to make improvements to patient care and reduce negative experiences.

Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties.

Goal-directed navigation requires continuously integrating uncertain self-motion and landmark cues into an internal sense of location and direction, concurrently planning future paths, and sequentially executing motor actions. Here, we provide a unified account of these processes with a computational model of probabilistic path planning in the framework of optimal feedback control under uncertainty. This model gives rise to diverse human navigational strategies previously believed to be distinct behaviors and predicts quantitatively both the errors and the variability of navigation across numerous experiments. This furthermore explains how sequential egocentric landmark observations form an uncertain allocentric cognitive map, how this internal map is used both in route planning and during execution of movements, and reconciles seemingly contradictory results about cue-integration behavior in navigation. Taken together, the present work provides a parsimonious explanation of how patterns of human goal-directed navigation behavior arise from the continuous and dynamic interactions of spatial uncertainties in perception, cognition, and action.

Embracing epistemic uncertainty: a risk evaluation method for pollutants in stormwater.

In this study, we show that pollutants of emerging concern are, by nature, prone to the emergence of epistemic uncertainty. We also show that the current uncertainty quantification methods used for pollutant modelling rely almost exclusively on parameter uncertainty, which is not adequate to tackle epistemic uncertainty affecting the model structure. We, therefore, suggest a paradigm shift in the current pollutant modelling approaches by adding a term explicitly accounting for epistemic uncertainties. In a proof-of-concept, we use this approach to investigate the impact of epistemic uncertainty in the fluctuation of pollutants during wet-weather discharge (input information) on the distribution of mass of pollutants (output distributions). We found that the range of variability negatively impacts the tail of output distributions. The fluctuation time, associated with high covariance between discharge and concentration, is a major driver for the output distributions. Adapting to different levels of epistemic uncertainty, our approach helps to identify critical unknown information in the fluctuation of pollutant concentration. Such information can be used in a risk management context and to design smart monitoring campaigns.

Quantifying uncertainty in trans-membrane stresses and moments in simulation.

The lateral stress profile of a lipid bilayer constitutes a valuable link between molecular simulation and mesoscopic elastic theory. Even though it is frequently calculated in simulations, its statistical precision (or that of observables derived from it) is often left unspecified. This omission can be problematic, as uncertainties are prerequisite to assessing statistical significance. In this chapter, we provide a comprehensive yet accessible overview of the statistical error analysis for the lateral stress profile. We detail two relatively simple but powerful techniques for generating error bars: block-averaging and bootstrapping. Combining these methods allows us to reliably estimate uncertainties, even in the presence of both temporal and spatial correlations, which are ubiquitous in simulation data. We illustrate these techniques with simple examples like stress moments, but also more complex observables such as the location of stress profile extrema and the monolayer neutral surface.

Illness uncertainty and dysphagia in Chinese oral cancer patients: the mediation effect of catastrophic cognition.

PURPOSE: Dysphagia, a serious symptom of oral cancer, is also the most common. Further, patients who are more uncertain regarding their illness tend to catastrophize, which may affect their rehabilitation and long-term survival rate. Considering this relationship, this study aimed to investigate the occurrence of dysphagia in Chinese patients with oral cancer and explore the correlation between catastrophic cognition, illness uncertainty, and dysphagia. METHODS: Applying a cross-sectional design, convenience sampling was used to recruit 180 patients with oral cancer. Advanced statistical methods were employed to analyze the mediating effects of catastrophic cognition on illness uncertainty and dysphagia. RESULTS: Chinese patients with oral cancer had a mean dysphagia score of 52.88 ± 10.95. Catastrophic cognition and illness uncertainty in patients with oral cancer were significantly positively correlated (r = 0.447, P < 0.001). There was a significant negative correlation between dysphagia score and catastrophic cognition (r = -0.385, P < 0.001), and between dysphagia and illness uncertainty (r = -0.522, P < 0.001). Bootstrapping results indicated that the mediating effect of catastrophic cognition between illness uncertainty and dysphagia was -0.07 (95% CI: [-0.15, -0.03]) and significant, and the mediation effect accounted for 15.6% of the total effect. CONCLUSIONS: Chinese patients with oral cancer have poor swallowing function. Results suggest that catastrophic cognition partially mediated the relationship between illness uncertainty and dysphagia in patients with oral cancer. Medical staff can improve patients' swallowing function by reducing the level of catastrophic cognition via decreasing the level of illness uncertainty.

Z-number network data envelopment analysis approach: A case study on the Iranian insurance industry.

The main aim of this research is to present an innovative method known as fuzzy network data envelopment analysis (FNDEA) in order to assess the performance of network decision-making units (DMUs) that possess a two-stage structure while taking into account the uncertainty of data. To attain this goal, we utilize various methodologies including the non-cooperative game (leader-follower) NDEA method, the concept of Z-number, credibility theory, and chance-constrained programming (CCP) to develop a model for the fuzzy NDEA approach. The FNDEA approach offers several advantages, such as the linearity of the presented FNDEA models, the ability to rank two-stage DMUs in situations of ambiguity, the provision of a unique efficiency decomposition method in an uncertain environment, and the capability to handle Z-information. To demonstrate the applicability and effectiveness of the proposed approach, we implement the Z-number network data envelopment analysis (ZNDEA) approach in assessing the performance of Iranian private insurance companies. The results of this implementation reveal that the proposed ZNDEA method is suitable and effective for measuring and ranking insurance companies in situations where data ambiguity is present.

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Objective.Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model's output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termedLocal Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation.Approach.This method leverages a trained model's localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood.Main results.(1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g. 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p< 0.05 vsp> 0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%.Significance. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.

Accelerating Polymer Discovery with Uncertainty-Guided PGCNN: Explainable AI for Predicting Properties and Mechanistic Insights.

Deep learning holds great potential for expediting the discovery of new polymers from the vast chemical space. However, accurately predicting polymer properties for practical applications based on their monomer composition has long been a challenge. The main obstacles include insufficient data, ineffective representation encoding, and lack of explainability. To address these issues, we propose an interpretable model called the Polymer Graph Convolutional Neural Network (PGCNN) that can accurately predict various polymer properties. This model is trained using the RadonPy data set and validated using experimental data samples. By integrating evidential deep learning with the model, we can quantify the uncertainty of predictions and enable sample-efficient training through uncertainty-guided active learning. Additionally, we demonstrate that the global attention of the graph embedding can aid in discovering underlying physical principles by identifying important functional groups within polymers and associating them with specific material attributes. Lastly, we explore the high-throughput screening capability of our model by rapidly identifying thousands of promising candidates with low and high thermal conductivity from a pool of one million hypothetical polymers. In summary, our research not only advances our mechanistic understanding of polymers using explainable AI but also paves the way for data-driven trustworthy discovery of polymer materials.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment.

Objective.Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes.Approach.Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models.Main results.Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons.Significance.In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.

Examining the role of pre-visit anxiety on patient uncertainty and breast cancer patient-provider communication.

OBJECTIVE: Effective communication between cancer patients and providers is critical for addressing psychological distress, reducing uncertainty, and promoting patient well-being. This is particularly relevant during medical appointments that may elicit uncertainty, such as surgical consultations for newly diagnosed women with breast cancer. This study aimed to evaluate how pre-appointment anxiety and illness uncertainty affect patient-provider communication in breast cancer surgical consultations and subsequent post-appointment well-being. Breast cancer patient anxiety has been studied as an outcome of provider communication, though less is known about the extent to which preexisting anxiety or uncertainty act as antecedents to effective patient-provider communication. METHODS: This study analyzed videorecorded breast cancer surgical consultations (N = 51) and corresponding patient surveys to understand how pre-appointment anxiety influences pre-appointment patient uncertainty, patient-provider communication during the appointment, and subsequent post-appointment uncertainty. RESULTS: The proposed model achieved good fit to the data such that more pre-appointment anxiety was associated with more pre-appointment uncertainty, more pre-appointment anxiety was associated with more empathic opportunities per minute, and more empathic opportunities were associated with less post-appointment uncertainty. CONCLUSIONS: Results indicate breast cancer patients with anxiety pre-appointment are at-risk for more illness uncertainty and are more likely to explicitly provide empathic opportunities. This supports the need for added attention to empathic opportunities to not only address patients emotionally but to also assess whether a patient may be at higher risk of having preexisting anxiety.

The evolution of social behaviors and risk preferences in settings with uncertainty.

Humans update their social behavior in response to past experiences and changing environments. Behavioral decisions are further complicated by uncertainty in the outcome of social interactions. Faced with uncertainty, some individuals exhibit risk aversion while others seek risk. Attitudes toward risk may depend on socioeconomic status; and individuals may update their risk preferences over time, which will feedback on their social behavior. Here, we study how uncertainty and risk preferences shape the evolution of social behaviors. We extend the game-theoretic framework for behavioral evolution to incorporate uncertainty about payoffs and variation in how individuals respond to this uncertainty. We find that different attitudes toward risk can substantially alter behavior and long-term outcomes, as individuals seek to optimize their rewards from social interactions. In a standard setting without risk, for example, defection always overtakes a well-mixed population engaged in the classic Prisoner's Dilemma, whereas risk aversion can reverse the direction of evolution, promoting cooperation over defection. When individuals update their risk preferences along with their strategic behaviors, a population can oscillate between periods dominated by risk-averse cooperators and periods of risk-seeking defectors. Our analysis provides a systematic account of how risk preferences modulate, and even coevolve with, behavior in an uncertain social world.

Uncertainty and Communication Preferences Among Patients Undergoing Lung Transplant Evaluation: A Mixed-Methods Study.

OBJECTIVE: Higher uncertainty is associated with poorer quality of life and may be impacted by clinician communication about the future. We determined how patients undergoing lung transplant evaluation experience uncertainty and communication about the future from clinicians. METHODS: We performed a convergent parallel mixed-methods study using a cross-sectional survey and semistructured interviews. Patients undergoing lung transplant evaluation at the University of Colorado and the University of Washington answered questions about future communication and completed the Mishel Uncertainty in Illness Scale-Adult (MUIS-A; range 33-165, higher scores indicate more uncertainty). Interviews were analyzed using content analysis. Integration of survey and interview results occurred during data interpretation. RESULTS: A total of 101 patients completed the survey (response rate: 47%). Twelve survey participants completed interviews. In the survey, most patients identified changing family roles as important (76%), which was infrequently discussed with clinicians (31%). Most patients (86%) worried about the quality of their life in the future, and 74% said that not knowing what to expect in the future prevented them from making plans. The mean MUIS-A score was 85.5 (standard deviation 15.3). Interviews revealed three themes: (1) uncertainty of the future distresses participants; (2) participants want practical information from clinicians; and (3) communication preferences vary among participants. CONCLUSION: Participants experienced distressing uncertainty and wanted information about the future. Communication topics that were important to participants were not always addressed by physicians. Clinicians should address how chronic lung disease and lung transplant can directly impact patients' lives and support patients to cope with uncertainty.

Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing.

BACKGROUND: Identification of human leukocyte antigen (HLA) types from DNA-sequenced human samples is important in organ transplantation and cancer immunotherapy and remains a challenging task considering sequence homology and extreme polymorphism of HLA genes. RESULTS: We present Orthanq, a novel statistical model and corresponding application for transparent and uncertainty-aware quantification of haplotypes. We utilize our approach to perform HLA typing while, for the first time, reporting uncertainty of predictions and transparently observing mutations beyond reported HLA types. Using 99 gold standard samples from 1000 Genomes, Illumina Platinum Genomes and Genome In a Bottle projects, we show that Orthanq can provide overall superior accuracy and shorter runtimes than state-of-the-art HLA typers. CONCLUSIONS: Orthanq is the first approach that allows to directly utilize existing pangenome alignments and type all HLA loci. Moreover, it can be generalized for usages beyond HLA typing, e.g. for virus lineage quantification. Orthanq is available under https://orthanq.github.io .

Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study.

The measurement of productivity change in decision-making units (DMUs) is crucial for assessing their performance and supporting efficient decision-making processes. In this paper, we propose a new approach for measuring productivity change using the Malmquist productivity index (MPI) within the context of two-stage network data envelopment analysis (TSNDEA) under data uncertainty. The two-stage network structure represents a realistic model for DMUs in various fields, such as insurance companies, bank branches, and mutual funds. However, traditional DEA models do not adequately address the issue of data uncertainty, which can significantly impact the accuracy of productivity measurements. To address this limitation, we integrate the MPI methodology with an uncertain programming framework to tackle data uncertainty in the productivity change measurement process. Our proposed approach enables the evaluation of productivity change by capturing both technical efficiency and technological progress over time. By incorporating fuzzy mathematical programming into the DEA framework, we model the inherent uncertainty in input and output data more effectively, enhancing the robustness and reliability of productivity measurements. The utilization of the proposed approach provides decision-makers with a comprehensive analysis of productivity change in DMUs, allowing for better identification of efficiency improvements or potential areas for enhancement. The findings from our study can enhance the decision-making process and facilitate more informed resource allocation strategies in real-world applications.

Dynamic adaptive decision support for strategic decision-making in healthcare organizations.

PURPOSE: The main objective of this study was to design a dynamic adaptive decision support model for healthcare organizations facing deep uncertainties by considering promising dynamic adaptive approaches. The main argument for this is that healthcare organizations have to make strategic decisions under deep uncertainty, but lack an approach to deal with this. DESIGN/METHODOLOGY/APPROACH: A Dynamic Adaptive Decision Support model (DADS) is designed using the Design Science Research methodology. The evaluation of an initial model leads, through two case studies on ongoing and strategic decision-making, to the final design of this needed model for healthcare organizations. FINDINGS: The research reveals the relevance of the designed dynamic and adaptive tool to support strategic decision-making for healthcare organizations. The final design of DADS innovates Decision Making under Deep Uncertainty (DMDU) approaches in an organizational context for ongoing and strategic decision-making. ORIGINALITY/VALUE: The designed model applies the Dynamic Adaptive Policy Pathways approach in an organizational context and more specifically in health care organizations. It further integrates Corporate Real Estate Management knowledge and experience to develop a most needed tool for decision-makers in healthcare. This is the first DADS designed for an organization facing deep uncertainties in a rapidly changing healthcare environment and dealing with ongoing and strategic decision-making.