Probabilistic assessment of wind power plant energy potential through a copula-deep learning approach in decision trees.

In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.

Methods for Including Adverse Events in Economic Evaluations: Suggestions for Improvement.

OBJECTIVE: Inclusion of relevant effectiveness and safety outcomes in economic evaluation of health technologies is required to aid efficient healthcare decisions. Our objective was to identify the key issues related to the inclusion of adverse events (AEs) in economic evaluation and explore perspectives for good practice recommendations to handle these issues. METHODS: We focused on the frequently encountered methodological issues related to the integration of AEs in economic evaluations of health technologies. We distinguished the following elements: the incorporation of AEs in decision models, the terminology of AEs, the estimation of AEs consequences in terms of quality of life (QoL) and costs, and the exploration of the uncertainty related to the impact of AEs on the economic results. RESULTS: We illustrated and discussed each of the identified issues by giving health technology assessment examples. We focused on the extent to which the integration of AEs in decision models can be improved by dealing with the lack of relevant real-world safety data, estimating the consequences of AEs (eg, for costs and QoL loss), exploring the impacts of AEs that are not adequately captured in current measurement of health-related QoL, and identifying the need for development of a good terminology of relevant types of AEs to be incorporated in economic evaluation. CONCLUSION: Based on a reflection the key methodological issues related to the incorporation of adverse drug events in economic evaluations, we suggested several recommendations to serve a starting point for health technology assessment agencies and researchers to develop good research practices in this field.

A revised diffusion model for conflict tasks.

The recently developed diffusion model for conflict tasks (DMC) Ulrich et al. (Cognitive Psychology, 78, 148-174, 2015) provides a good account of data from all standard conflict tasks (e.g., Stroop, Simon, and flanker tasks) within a common evidence accumulation framework. A central feature of DMC's processing dynamics is that there is an initial phase of rapid accumulation of distractor evidence that is then selectively withdrawn from the decision mechanism as processing continues. We argue that this assumption is potentially troubling because it could be viewed as implying qualitative changes in the representation of distractor information over the time course of processing. These changes suggest more than simple inhibition or suppression of distractor information, as they involve evidence produced by distractor processing "changing sign" over time. In this article, we (a) develop a revised DMC (RDMC) whose dynamics operate strictly within the limits of inhibition/suppression (i.e., evidence strength can change monotonically, but cannot change sign); (b) demonstrate that RDMC can predict the full range of delta plots observed in the literature (i.e., both positive-going and negative-going); and (c) show that the model provides excellent fits to Simon and flanker data used to benchmark the original DMC at both the individual and group level. Our model provides a novel account of processing differences across Simon and flanker tasks. Specifically, that they differ in how distractor information is processed on congruent trials, rather than incongruent trials: congruent trials in the Simon task show relatively slow attention shifting away from distractor information (i.e., location) while complete and rapid attention shifting occurs in the flanker task. Our new model highlights the importance of considering dynamic interactions between top-down goals and bottom-up stimulus effects in conflict processing.

Challenges to corporate social responsibility adoption for sustainability: A picture fuzzy approach.

Corporate social responsibility (CSR) has become crucial to businesses seeking to adopt sustainable development. However, there are several challenges to CSR adoption for sustainability that the present research aims to identify and evaluate, in addition to assessing the EU's response to these challenges. To this end, a novel picture fuzzy SWARA-TOPSIS method is proposed to rank EU countries after identifying the challenges that they face through a literature review. The results indicate that out of eight identified challenges, "Leadership Mindset and Corporate Commitment" is the most significant issue to CSR adoption for sustainability. Additionally, Italy shows the best performance in adopting CSR for sustainability, while Malta shows the worst. These results are discussed, and policy implications are presented. Integr Environ Assess Manag 2023;19:1254-1275. © 2023 SETAC.

Multicriteria decision frontiers for prescription anomaly detection over time.

Health care prescription fraud and abuse result in major financial losses and adverse health effects. The growing budget deficits of health insurance programs and recent opioid drug abuse crisis in the United States have accelerated the use of analytical methods. Unsupervised methods such as clustering and anomaly detection could help the health care auditors to evaluate the billing patterns when embedded into rule-based frameworks. These decision models can aid policymakers in detecting potential suspicious activities. This manuscript proposes an unsupervised temporal learning-based decision frontier model using the real world Medicare Part D prescription data collected over 5 years. First, temporal probabilistic hidden groups of drugs are retrieved using a structural topic model with covariates. Next, we construct combined concentration curves and Gini measures considering the weighted impact of temporal observations for prescription patterns, in addition to the Gini values for the cost. The novel decision frontier utilizes this output and enables health care practitioners to assess the trade-offs among different criteria and to identify audit leads.

Predicting COVID-19-Related Health Care Resource Utilization Across a Statewide Patient Population: Model Development Study.

BACKGROUND: The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. OBJECTIVE: This study aims to inform the feasibility of leveraging broad, statewide datasets for population health-driven decision-making by developing robust analytical models that predict COVID-19-related health care resource utilization across patients served by Indiana's statewide Health Information Exchange. METHODS: We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). RESULTS: For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). CONCLUSIONS: This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.

Sugar sweetened beverages attributable disease burden and the potential impact of policy interventions: a systematic review of epidemiological and decision models.

BACKGROUND: Around 184,000 deaths per year could be attributable to sugar-sweetened beverages (SSBs) consumption worldwide. Epidemiological and decision models are important tools to estimate disease burden. The purpose of this study was to identify models to assess the burden of diseases attributable to SSBs consumption or the potential impact of health interventions. METHODS: We carried out a systematic review and literature search up to August 2018. Pairs of reviewers independently selected, extracted, and assessed the quality of the included studies through an exhaustive description of each model's features. Discrepancies were solved by consensus. The inclusion criteria were epidemiological or decision models evaluating SSBs health interventions or policies, and descriptive SSBs studies of decision models. Studies published before 2003, cost of illness studies and economic evaluations based on individual patient data were excluded. RESULTS: We identified a total of 2766 references. Out of the 40 included studies, 45% were models specifically developed to address SSBs, 82.5% were conducted in high-income countries and 57.5% considered a health system perspective. The most common model's outcomes were obesity/overweight (82.5%), diabetes (72.5%), cardiovascular disease (60%), mortality (52.5%), direct medical costs (57.35%), and healthy years -DALYs/QALYs- (40%) attributable to SSBs. 67.5% of the studies modelled the effect of SSBs on the outcomes either entirely through BMI or through BMI plus diabetes independently. Models were usually populated with inputs from national surveys -such us obesity prevalence, SSBs consumption-; and vital statistics (67.5%). Only 55% reported results by gender and 40% included children; 30% presented results by income level, and 25% by selected vulnerable groups. Most of the models evaluated at least one policy intervention to reduce SSBs consumption (92.5%), taxes being the most frequent strategy (75%). CONCLUSIONS: There is a wide range of modelling approaches of different complexity and information requirements to evaluate the burden of disease attributable to SSBs. Most of them take into account the impact on obesity, diabetes and cardiovascular disease, mortality, and economic impact. Incorporating these tools to different countries could result in useful information for decision makers and the general population to promote a deeper implementation of policies to reduce SSBs consumption. PROSPERO PROTOCOL NUMBER: CRD42020121025 .

A decision-making model to optimize the impact of community-based health programs.

Hospitals and clinics are increasingly interested in building partnerships with community-based organizations to address the social determinants of health. Choosing among community-based health programs can be complex given that programs may have different effectiveness levels and implementation costs. This study develops a decision-making model that can be used to evaluate multiple key factors that would be relevant in resource allocation decisions related to a set of community-based health programs. The decision-making model compares community-based health programs by considering funding limitations, program duration, and participant retention until program completion. Specifically, the model allows decision makers to select the optimal mix of community-based health programs based on the profiles of the population given the above constraints. The model can be used to improve resource allocation in communities, ultimately contributing to the long-term goal of strengthening cross-sector partnerships and the integration of services to improve health outcomes.

BAIT: A New Medical Decision Support Technology Based on Discrete Choice Theory.

We present a novel way to codify medical expertise and to make it available to support medical decision making. Our approach is based on econometric techniques (known as conjoint analysis or discrete choice theory) developed to analyze and forecast consumer or patient behavior; we reconceptualize these techniques and put them to use to generate an explainable, tractable decision support system for medical experts. The approach works as follows: using choice experiments containing systematically composed hypothetical choice scenarios, we collect a set of expert decisions. Then we use those decisions to estimate the weights that experts implicitly assign to various decision factors. The resulting choice model is able to generate a probabilistic assessment for real-life decision situations, in combination with an explanation of which factors led to the assessment. The approach has several advantages, but also potential limitations, compared to rule-based methods and machine learning techniques. We illustrate the choice model approach to support medical decision making by applying it in the context of the difficult choice to proceed to surgery v. comfort care for a critically ill neonate.

Dynamic Bayesian Networks to Assess Anthropogenic and Climatic Drivers of Saltwater Intrusion: A Decision Support Tool Toward Improved Management.

Saltwater intrusion (SWI) is a global coastal problem caused by aquifer overpumping, land-use change, and climate change impacts. Given the complex pathways that lead to SWI, coastal urban areas with poorly monitored aquifers are in need of probabilistic-based decision support tools that can assist in better understanding and predicting SWI, while exploring effective means for sustainable aquifer management. In this study, we develop a Bayesian Belief Network (BBN) to account for the complex interactions of climatic and anthropogenic processes leading to SWI, while relating the severity of SWI to associated socioeconomic impacts and possible adaptation strategies. The BBN is further expanded into a Dynamic Bayesian Network (DBN) to assess the temporal progression of SWI and account for the compounding uncertainties over time. The proposed DBN is then tested at a pilot coastal aquifer underlying a highly urbanized water-stressed metropolitan area along the Eastern Mediterranean coastline (Beirut, Lebanon). The results show that the future impacts of climate change are largely secondary when compared to the persistent water deficits. While both supply and demand management could halt the progression of salinity, the potential for reducing or reversing SWI is not evident. The indirect socioeconomic burden associated with aquifer salinity was observed to improve, albeit heterogeneously, with the application of various adaptation strategies; however, this was at a cost associated with the implementation and operation of these strategies. The proposed DBN acts as an effective decision support tool that can promote sustainable aquifer management in coastal regions through its robust representation of the main drivers of SWI and linking them to expected socioeconomic burdens and management options. Integr Environ Assess Manag 2021;17:202-220. © 2020 SETAC.

Cost-utility analysis of selective internal radiation therapy with Y-90 resin microspheres in hepatocellular carcinoma.

Background: The study assessed the cost-utility of selective internal radiation therapy (SIRT) with Y-90 resin microspheres versus sorafenib in UK patients with unresectable hepatocellular carcinoma ineligible for transarterial chemoembolization. Materials & methods: A lifetime partitioned survival model was developed for patients with low tumor burden (≤25%) and good liver function (albumin-bilirubin grade 1). Efficacy, safety and quality of life data were from a European Phase III randomized controlled trial and published studies. Resource use was from registries and clinical surveys. Results: Discounted quality-adjusted life-years were 1.982 and 1.381, and discounted total costs were £29,143 and 30,927, for SIRT and sorafenib, respectively. Conclusion: SIRT has the potential to be a dominant (more efficacious/less costly) or cost-effective alternative to sorafenib in patients with unresectable hepatocellular carcinoma.

Big Data, Decision Models, and Public Health.

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

Consistent patterns of distractor effects during decision making.

The value of a third potential option or distractor can alter the way in which decisions are made between two other options. Two hypotheses have received empirical support: that a high value distractor improves the accuracy with which decisions between two other options are made and that it impairs accuracy. Recently, however, it has been argued that neither observation is replicable. Inspired by neuroimaging data showing that high value distractors have different impacts on prefrontal and parietal regions, we designed a dual route decision-making model that mimics the neural signals of these regions. Here we show in the dual route model and empirical data that both enhancement and impairment effects are robust phenomena but predominate in different parts of the decision space defined by the options' and the distractor's values. However, beyond these constraints, both effects co-exist under similar conditions. Moreover, both effects are robust and observable in six experiments.

Uptake of methodological advances for synthesis of continuous and time-to-event outcomes would maximize use of the evidence base.

OBJECTIVE: The objective of the study is to establish how often continuous and time-to-event outcomes are synthesized in health technology assessment (HTA), the statistical methods and software used in their analysis and how often evidence synthesis informs decision models. STUDY DESIGN AND SETTING: This is a review of National Institute of Health Research HTA reports, National Institute for Health and Care Excellence (NICE) technology appraisals, and NICE guidelines reporting quantitative meta-analysis or network meta-analysis of at least one continuous or time-to-event outcome published from April 01, 2018 to March 31, 2019. RESULTS: We identified 47 eligible articles. At least one continuous or time-to-event outcome was synthesized in 51% and 55% of articles, respectively. Evidence synthesis results informed decision models in two-thirds of articles. The review and expert knowledge identified five areas where methodology is available for improving the synthesis of continuous and time-to-event outcomes: i) outcomes reported on multiple scales, ii) reporting of multiple related outcomes, iii) appropriateness of the additive scale, iv) reporting of multiple time points, and v) nonproportional hazards. We identified three anticipated barriers to the uptake and implementation of these methods: i) statistical expertise, ii) software, and iii) reporting of trials. CONCLUSION: Continuous and time-to-event outcomes are routinely reported in HTA. However, increased uptake of methodological advances could maximize the evidence base used to inform the decision making process.

Cost-Saving Opportunities with an Oral and Intravenous Once-Daily Aminomethylcycline Antibiotic for Hospitalized Patients with Community-Acquired Bacterial Pneumonia: Findings from Decision-Analytic Models.

BACKGROUND: The most frequently prescribed regimens for the treatment of hospitalized adults with suspected or documented community-acquired bacterial pneumonia (CABP), an acute bacterial infection of the pulmonary parenchyma, are ceftriaxone plus a macrolide, or a respiratory fluoroquinolone. Although these regimens are consistent with expert guidelines, there are growing concerns regarding their safety and efficacy. Omadacycline is a once-daily antibiotic with oral and intravenous (IV) formulations; it was recently approved in the United States for the treatment of adults with CABP. OBJECTIVE: To estimate the cost impact of shortening hospital stay or avoiding hospitalization when using a treatment with an IV and an oral formulation, such as omadacycline, versus an IV-only drug regimen, such as ceftriaxone plus a macrolide, in adults with CABP who are not candidates for respiratory fluoroquinolone therapy. METHODS: We developed 2 conceptual healthcare decision models to identify potential cost-saving opportunities in hospitalized adults with CABP who receive omadacycline versus ceftriaxone plus a macrolide. The early hospital discharge model examined the cost impact of shifting patients with CABP from inpatient treatment with ceftriaxone plus a macrolide to inpatient IV omadacycline treatment and early hospital discharge with oral omadacycline. The hospital-avoidance model examined the cost impact of omadacycline treatment in the outpatient setting in patients with CABP who have low disease severity. The models defined the upper range of the daily acquisition cost for omadacycline that conferred cost-savings relative to inpatient treatment with ceftriaxone plus a macrolide. RESULTS: In the early hospital discharge model, omadacycline showed cost-savings with a 2-day hospital stay reduction if the daily cost of omadacycline was ≤$836, almost twice its wholesale acquisition cost. In the hospital-avoidance model, the daily omadacycline thresholds that still conferred cost-savings relative to inpatient ceftriaxone plus a macrolide ranged from $1302 to $1334, based on a daily wholesale acquisition cost of $450 for omadacycline, depending on the potential use of the emergency department and an observation unit. CONCLUSION: The study findings show that the targeted use of omadacycline for the treatment of select patient populations with CABP could result in cost-savings relative to inpatient treatment with ceftriaxone plus a macrolide.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

INTRODUCTION: The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes. OBJECTIVE: The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.

OBJECTIVE: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs). STUDY DESIGN: In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. METHODS: We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease. RESULTS: Risk modeling with BNs has advantages over regression-based approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes's theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models. CONCLUSIONS: Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine.

Models of attention-deficit hyperactivity disorder.

One of the most notable aspects of the behavior of individuals with Attention Deficit Hyperactivity Disorder (ADHD) is increased variability in many aspects of their behavior, including response times and attentional focus. Among the many theories of ADHD is one that identifies its material cause as phasic malnutrition of the neurons required to maintain constancy of performance. Of the diverse predictions issuing from this theory, one concerns ubiquitous data: response times and their variance in decision tasks. This paper reviews that behavioral neuroenergetics theory and model, shows how they predict representative data, and suggests their relevance to researchers studying animal models of ADHD.

"Cannot Decide": The Fine Line Between Appropriate Inconclusive Determinations Versus Unjustifiably Deciding Not To Decide.

Inconclusive decisions, deciding not to decide, are decisions. We present a cognitive model which takes into account that decisions are an outcome of interactions and intersections between the actual data and human cognition. Using this model it is suggested under which circumstances inconclusive decisions are justified and even warranted (reflecting proper caution and meta-cognitive abilities in recognizing limited abilities), and, conversely, under what circumstances inconclusive decisions are unjustifiable and should not be permitted. The model further explores the limitations and problems in using categorical decision-making when the data are actually a continuum. Solutions are suggested within the forensic fingerprinting domain, but they can be applied to other forensic domains, and, with modifications, may also be applied to other expert domains.