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Motor vehicles are composed of a large number of parts, and planning the maintenance activities of different parts is a crucial decision that affects system reliability, operation costs, and capacity requirements of service providers. We propose a systematic method to determine the critical parts that should be handled with extra preventive maintenance (PM) and prepare alternative PM plans with different levels of cost and capacity usage. Our method uses a multicriteria decision-making approach to determine the critical parts and conducts statistical reliability analysis with failure data and expert knowledge to create the maintenance plans. We use the proposed method in a case study to determine optional PM packages that would support regular PM practices in the after-sales service of a large motor vehicle manufacturer. The main aim of the case study is to increase the satisfaction of customers who are more sensitive to failures, such as carriers of food and medical supplies. The results show that the optional PM packages can decrease the cost of failures while obeying the capacity limitation of the company.

Members of honor cultures value engaging in moral behaviors and managing their social image to maintain their honor. These two goals reflect reputation and integrity concerns, which also have bearing on gender roles. In the current study, we examined a) evaluations of women and men described as diagnosed with COVID-19 and as either hiding or disclosing their diagnosis, b) the moderating role of honor concerns (reputation and integrity) and the gender of the infected person in these evaluations, and c) the relationship between honor concerns and individuals’ own disclosure preferences among participants living in Türkiye, a country that exemplifies an honor culture. Findings revealed that participants with stronger reputation concerns evaluated a woman’s hiding behavior more favorably than that of a man’s. Moreover, higher integrity concerns were associated with lower levels of participants’ own preference to hide a diagnosis for both men and women, whereas reputation concerns were positively associated with a preference for hiding a diagnosis among men only. Furthermore, a content analysis of participants’ open-ended explanations of their views on women’s and men’s motivation to hide a diagnosis revealed further evidence for the gendered nature of reputation concerns. Our findings point to the importance of prioritizing integrity concerns (and downplaying reputation concerns) in public health campaigns in honor cultures.

BACKGROUND Identifying and managing serious spinal pathology (SSP), such as cauda equina syndrome or spinal infection, is challenging. Traditional red flag questioning is increasingly criticised, and improving decision-making is being actively researched. OBJECTIVE We aimed to improve serious pathology identification by constructing and validating a decision support tool using Artificial Intelligence (AI) that combines current evidence and expert knowledge. METHODS A modified RAND appropriateness procedure, including variable, structure and probability elicitation was deployed to build a Bayesian AI model of reasoning elicited from 16 experts over 3 rounds. The causal model was designed to predict the likelihood of a patient with a particular presentation having an SSP. An established framework directed a 4-part validation that included comparison of the model with consensus statements, practice guidelines and recent research. Clinical cases were entered into the model and the results compared to clinical judgement from spinal experts. RESULTS The model included 38 variables in three domains of risk factors (10 variables), signs & symptoms (17 variables) and judgement factors (11). Comparison with the evidence showed the model is typically consistent but needs changes to e.g., 2 of 11 judgement factors. Case analysis showed cauda-equina-syndrome, space-occupying-lesion, cancer and inflammatory condition identification performed well across validation domains. Fracture performed less well, but with well-defined reasons for the erroneous results. CONCLUSIONS A knowledge-based AI system for decision support for SSP was constructed. The tool can be completed in a time period compatible with a patient contact and shows encouraging validity. Further work to improve the existing model and include treatment decision making is needed alongside prospective validation. The prototype tool is ready to be taken forward for refinement and clinical testing. INTERNATIONAL REGISTERED REPORT RR2-10.2196/21804

Background Cervical spine immobilisation increases the difficulty of tracheal intubation. Many intubation devices have been evaluated in this setting, but their relative performance remains uncertain. Methods MEDLINE, EMBASE, and the Cochrane Library were searched to identify randomised trials comparing two or more intubation devices in adults with cervical spine immobilisation. After critical appraisal, a random-effects network meta-analysis was used to pool and compare device performance. The primary outcome was the probability of first-attempt intubation success (first-pass success). For relative performance, the Macintosh direct laryngoscopy blade was chosen as the reference device. Results We included 80 trials (8039 subjects) comparing 26 devices. Compared with the Macintosh, McGrath™ (odds ratio [OR]=11.5; 95% credible interval [CrI] 3.19–46.20), C-MAC D Blade™ (OR=7.44; 95% CrI, 1.06–52.50), Airtraq™ (OR=5.43; 95% CrI, 2.15–14.2), King Vision™ (OR=4.54; 95% CrI, 1.28–16.30), and C-MAC™ (OR=4.20; 95% CrI=1.28–15.10) had a greater probability of first-pass success. This was also true for the GlideScope™ when a tube guide was used (OR=3.54; 95% CrI, 1.05–12.50). Only the Airway Scope™ had a better probability of first-pass success compared with the Macintosh when manual-in-line stabilisation (MILS) was used as the immobilisation technique (OR=7.98; 95% CrI, 1.06–73.00). Conclusions For intubation performed with cervical immobilisation, seven devices had a better probability of first-pass success compared with the Macintosh. However, more studies using MILS (rather than a cervical collar or other alternative) are needed, which more accurately represent clinical practice. Clinical trial registration PROSPERO 2019 CRD42019158067 (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=158067).

Celecoxib (CXB) is a Biopharmaceutical Classification System (BCS) Class II molecule with high permeability that is practically insoluble in water. Because of the poor water solubility, there is a wide range of absorption and limited bioavailability following oral administration. These unfavorable properties can be improved using dry co-milling technology, which is an industrial applicable technology. The purpose of this study was to develop and optimize CXB nanoformulations prepared by dry co-milling technology, with a quality by design approach to maintain enhanced solubility, dissolution rate, and oral bioavailability. The resulting co-milled CXB composition using povidone (PVP), mannitol (MAN) and sodium lauryl sulfate (SLS) showed the maximum solubility and dissolution rate in physiologically relevant media. Potential risk factors were determined with an Ishikawa diagram, important risk factors were selected with Plackett-Burman experimental design, and CXB compositions were optimized with Central Composite design (CCD) and Bayesian optimization (BO). Physical characterization, intrinsic dissolution rate, solubility, and stability experiments were used to evaluate the optimized co-milled CXB compositions. Dissolution and permeability studies were carried out for the resulting CXB nanoformulation. Oral pharmacokinetic studies of the CXB nanoformulation and reference product were performed in rats. The results of in vitro and in vivo studies show that the CXB nanoformulations have enhanced solubility (over 4.8-fold (8.6 ± 1.06 µg/mL vs. 1.8 ± 0.33 µg/mL) in water when compared with celecoxib pure powder), and dissolution rate (at least 85% of celecoxib is dissolved in 20 min), and improved oral pharmacokinetic profile (the relative bioavailability was 145.2%, compared to that of Celebrex®, and faster tmax 3.80 ± 2.28 h vs. 6.00 ± 3.67 h, indicating a more rapid absorption rate).

Reliability prediction is crucial for aircraft maintenance and spare part inventory decisions. These predictions are made based on operational data collected by fleet operators or design life estimates provided by aircraft suppliers. Purely data-driven predictions have limited use especially when the fleet is young, hence the data is scarce. In this case, design life estimates are used for predicting reliability often by assuming a constant failure rate. This strong assumption is not necessarily valid for all components. This paper proposes a Bayesian Network (BN) modelling framework that systematically combines design life estimates, operational data, and expert judgement for reliability prediction of aircraft subsystems. The proposed BN adjusts the design life estimates based on expert judgement regarding supplier and manufacturing quality and revises it based on operational data. We used the BN to predict the reliability of a large aircraft fleet by using failure and maintenance data provided by a large fleet operator. We compared the predictive performance of the BN to using only data-driven approaches and to using only design life estimates provided by the aircraft supplier. The BN model provides consistently accurate reliability predictions compared to design-life estimates and purely data-driven approaches especially when the available data is scarce.

OBJECTIVES Estimating the likely success of limb revascularization in patients with lower-extremity arterial trauma is central to decisions between attempting limb salvage and amputation. However, the projected outcome is often unclear at the time these decisions need to be made, making them difficult and threatening sound judgement. The objective of this study was to develop and validate a prediction model that can quantify an individual patient's risk of failed revascularization. METHODS A BN prognostic model was developed using domain knowledge and data from the US joint trauma system. Performance (discrimination, calibration, and accuracy) was tested using ten-fold cross validation and externally validated on data from the UK Joint Theatre Trauma Registry. BN performance was compared to the mangled extremity severity score. RESULTS Rates of amputation performed because of nonviable limb tissue were 12.2% and 19.6% in the US joint trauma system (n = 508) and UK Joint Theatre Trauma Registry (n = 51) populations respectively. A 10-predictor BN accurately predicted failed revascularization: area under the receiver operating characteristic curve (AUROC) 0.95, calibration slope 1.96, Brier score (BS) 0.05, and Brier skill score 0.50. The model maintained excellent performance in an external validation population: AUROC 0.97, calibration slope 1.72, Brier score 0.08, Brier skill score 0.58, and had significantly better performance than mangled extremity severity score at predicting the need for amputation [AUROC 0.95 (0.92-0.98) vs 0.74 (0.67-0.80); P < 0.0001]. CONCLUSIONS A BN (https://www.traumamodels.com) can accurately predict the outcome of limb revascularization at the time of initial wound evaluation. This information may complement clinical judgement, support rational and shared treatment decisions, and establish sensible treatment expectations.

A common way to model decision maker (DM) preferences in multiple criteria decision making problems is through the use of utility functions. The elicitation of the parameters of these functions is a major task that directly affects the validity and practical value of the decision making process. This paper proposes a novel Bayesian method that estimates the weights of criteria in linear additive utility functions by asking the DM to rank or select the best alternative in groups of decision alternatives. Our method computes the entire probability distribution of weights and utility predictions based on the DM’s answers. Therefore, it enables the DM to estimate the expected value of weights and predictions, and the uncertainty regarding these values. Additionally, the proposed method can estimate the weights by asking the DM to evaluate few groups of decision alternatives since it can incorporate various types of inputs from the DM in the form of rankings, constraints and prior distributions. Our method successfully estimates criteria weights in two case studies about financial investment and university ranking decisions. Increasing the variety of inputs, such as using both ranking of decision alternatives and constraints on the importance of criteria, enables our method to compute more accurate estimations with fewer inputs from the DM.

This paper is one in a series of analyses of the Dutch Simonshaven murder case, each using a different modeling approach. We adopted a Bayesian network (BN)-based approach which requires us to determine the relevant hypotheses and evidence in the case and their relationships (captured as a directed acyclic graph) along with explicit prior conditional probabilities. This means that both the graph structure and probabilities had to be defined using subjective judgments about the causal, and other, connections between variables and the strength and nature of the evidence. Determining if a useful BN could be quickly constructed by a small group using the previously established idioms-based approach which provides a generic method for translating legal cases into BNs, was a key aim. The model described was built by the authors during a workshop dedicated to the case at the Isaac Newton Institute, Cambridge, in September 2016. The total effort involved was approximately 26 h (i.e., an average of 6 h per author). With the basic assumptions described in the paper, the posterior probability of guilt once all the evidence is entered is 74%. The paper describes a formal evaluation of the model, using sensitivity analysis, to determine how robust the model conclusions are to key subjective prior probabilities over a full range of what may be deemed "reasonable" from both defense and prosecution perspectives. The results show that the model is reasonably robust-pointing not only generally to a reasonably high posterior probability of guilt but also generally below the 95% threshold expected in criminal law. Given the constraints on building a complex model so quickly, there are inevitably weaknesses; hence, the paper describes these and how they might be addressed, including how to take account of supplementary case information not known at the time of the workshop.

Estimating the likely success of limb revascularization in patients with lower-extremity arterial trauma is central to decisions between attempting limb salvage and amputation. However, the projected outcome is often unclear at the time these decisions need to be made, making them difficult and threatening sound judgement. The objective of this study was to develop and validate a prediction model that can quantify an individual patient's risk of failed revascularization.A BN prognostic model was developed using domain knowledge and data from the US joint trauma system. Performance (discrimination, calibration, and accuracy) was tested using ten-fold cross validation and externally validated on data from the UK Joint Theatre Trauma Registry. BN performance was compared to the mangled extremity severity score.Rates of amputation performed because of nonviable limb tissue were 12.2% and 19.6% in the US joint trauma system (n = 508) and UK Joint Theatre Trauma Registry (n = 51) populations respectively. A 10-predictor BN accurately predicted failed revascularization: area under the receiver operating characteristic curve (AUROC) 0.95, calibration slope 1.96, Brier score (BS) 0.05, and Brier skill score 0.50. The model maintained excellent performance in an external validation population: AUROC 0.97, calibration slope 1.72, Brier score 0.08, Brier skill score 0.58, and had significantly better performance than mangled extremity severity score at predicting the need for amputation [AUROC 0.95 (0.92-0.98) vs 0.74 (0.67-0.80); P0.0001].A BN (https://www.traumamodels.com) can accurately predict the outcome of limb revascularization at the time of initial wound evaluation. This information may complement clinical judgement, support rational and shared treatment decisions, and establish sensible treatment expectations.

BACKGROUND Low back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with consistent demonstration of increasing persistent pain and disability. Previous decision support tools for LBP management have focused on a subset of factors owing to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian network, which will entail constructing a clinical reasoning model elicited from experts. OBJECTIVE This paper proposes a method for conducting a modified RAND appropriateness procedure to elicit the knowledge required to construct a Bayesian network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure. METHODS We propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialties, such as orthopedics, rheumatology, and sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face-to-face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face-to-face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian network. Stage 4 is a rudimentary validation of the Bayesian network. RESULTS Ethical approval has been obtained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of three remote activities and two in-person meetings was required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms, even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a Bayesian network. The use of the internal pilot was recognized as being a methodological necessity. CONCLUSIONS We have proposed a method to construct Bayesian networks that are representative of expert clinical reasoning for a musculoskeletal condition in this case. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process to model clinical reasoning for a range of conditions. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/21804

Background Low back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with consistent demonstration of increasing persistent pain and disability. Previous decision support tools for LBP management have focused on a subset of factors owing to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian network, which will entail constructing a clinical reasoning model elicited from experts. Objective This paper proposes a method for conducting a modified RAND appropriateness procedure to elicit the knowledge required to construct a Bayesian network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure. Methods We propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialties, such as orthopedics, rheumatology, and sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face-to-face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face-to-face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian network. Stage 4 is a rudimentary validation of the Bayesian network. Results Ethical approval has been obtained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of three remote activities and two in-person meetings was required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms, even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a Bayesian network. The use of the internal pilot was recognized as being a methodological necessity. Conclusions We have proposed a method to construct Bayesian networks that are representative of expert clinical reasoning for a musculoskeletal condition in this case. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process to model clinical reasoning for a range of conditions. International Registered Report Identifier (IRRID) DERR1-10.2196/21804

OBJECTIVE The aim of this study was to develop and validate a risk prediction tool for trauma-induced coagulopathy (TIC), to support early therapeutic decision-making. BACKGROUND TIC exacerbates hemorrhage and is associated with higher morbidity and mortality. Early and aggressive treatment of TIC improves outcome. However, injured patients that develop TIC can be difficult to identify, which may compromise effective treatment. METHODS A Bayesian Network (BN) prediction model was developed using domain knowledge of the causal mechanisms of TIC, and trained using data from 600 patients recruited into the Activation of Coagulation and Inflammation in Trauma (ACIT) study. Performance (discrimination, calibration, and accuracy) was tested using 10-fold cross-validation and externally validated on data from new patients recruited at 3 trauma centers. RESULTS Rates of TIC in the derivation and validation cohorts were 11.8% and 11.0%, respectively. Patients who developed TIC were significantly more likely to die (54.0% vs 5.5%, P < 0.0001), require a massive blood transfusion (43.5% vs 1.1%, P < 0.0001), or require damage control surgery (55.8% vs 3.4%, P < 0.0001), than those with normal coagulation. In the development dataset, the 14-predictor BN accurately predicted this high-risk patient group: area under the receiver operating characteristic curve (AUROC) 0.93, calibration slope (CS) 0.96, brier score (BS) 0.06, and brier skill score (BSS) 0.40. The model maintained excellent performance in the validation population: AUROC 0.95, CS 1.22, BS 0.05, and BSS 0.46. CONCLUSIONS A BN (http://www.traumamodels.com) can accurately predict the risk of TIC in an individual patient from standard admission clinical variables. This information may support early, accurate, and efficient activation of hemostatic resuscitation protocols.

Objective To describe the long-term outcomes of military lower-extremity vascular injuries, and the decision making of surgeons treating these injuries. Background Lower-extremity vascular trauma is an important cause of preventable death and severe disability, and decisions on amputation or limb salvage can be difficult. Additionally, the complexity of the condition is not amenable to controlled study, and there is limited data to guide clinical decision making and establish sensible treatment expectations during rehabilitation. Methods A cohort study of 554 US service members who sustained lower-extremity vascular injury in Iraq or Afghanistan (March 2003 to February 2012) was performed using the military's trauma registry, its electronic health record, patient interviews, and quality-of-life surveys. Long-term surgical and functional outcomes, and the timing and rationale of surgical decisions, were analyzed. Results Of 579 injured extremities, 49 (8.5%) underwent primary amputation and 530 (91.5%) an initial attempt at salvage. Ninety extremities underwent secondary amputation, occurring in the early (n = 60; 30 days) phases after injury. For salvage attempts, freedom from amputation 10 years after injury was 82.7% (79.1%-85.7%). Long-term physical and mental health outcomes were similar between service members who underwent reconstruction and those who underwent amputation. Conclusion This military experience provides data that will inform an array of military and civilian providers who care for patients with severe lower-extremity injury. While the majority salvage attempts endure, success is hindered by ischemia and necrosis during the acute stage and pain, dysfunction and infection in the later phases of recovery. Level of evidence Therapeutic/prognostic, level III.

While decision trees are a popular formal and quantitative method for determining an optimal decision from a finite set of choices, for all but very simple problems they are computationally intractable. For this reason, Influence Diagrams (IDs) have been used as a more compact and efficient alternative. However, most algorithmic solutions assume that all chance variables are discrete, whereas in practice many are continuous. For such ‘Hybrid’ IDs (HIDs) the current-state-of-the-art algorithms suffer from various limitations on the kinds of inference that can be performed. This paper presents a novel method that overcomes a number of these limitations. The method solves a HID by transforming it to a Hybrid Bayesian Network (HBN) and carrying out inference on this HBN using Dynamic Discretization (DD). It generates a simplified decision tree from the propagated HBN to compute and present the optimal decisions under different decision scenarios. To provide satisfactory performance the method uses ‘inconsistent evidence’ to model functional and structural asymmetry. By using the entire marginal probability distribution of the continuous utility and chance nodes, rather than expected values alone, our method also enhances decision analysis by offering the possibility to consider additional statistics other than expected utility, such as measures of risk. We illustrate our method by using the oil wildcatter example and its variations with continuous nodes. We also use a financial score to combine risk and return measures, for illustration.

In decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This paper proposes a novel approach for calculating EVPPI in hybrid influence diagram (HID) models (these are influence diagrams (IDs) containing both discrete and continuous nodes). The proposed approach transforms the HID into a hybrid Bayesian network and makes use of the dynamic discretization and the junction tree algorithms to calculate the EVPPI. This is an approximate solution (no feasible exact solution is possible generally for HIDs) but we demonstrate it accurately calculates the EVPPI values. Moreover, unlike the previously proposed simulation-based EVPPI methods, our approach eliminates the requirement of manually determining the sample size and assessing convergence. Hence, it can be used by decision-makers who do not have deep understanding of programming languages and sampling techniques. We compare our approach to the previously proposed techniques based on two case studies.

Preparing university course timetables is a challenging task as many constraints and requirements from the university and lecturers must be satisfied without overlapping courses for different student groups. Although many mathematical optimization models have been proposed to automate this task, a wider use of these models have been limited as deep technical understanding of mathematical and computer programming are required in order to use and implement them. This paper proposes a simple and flexible course timetabling application that is based on a weighted binary goal programming model with a powerful solver. Our application enables the users to modify and run this model by using a simple web and spreadsheet interface. Consequently, the model does not require deep technical understanding of the underlying models from its users even though it is based on a complex mathematical model. The web application and the underlying optimization model is illustrated by using a case study of an undergraduate program of industrial engineering.

We focus on project cost, benefit and risk analysis.We propose a modelling framework that uses a hybrid and dynamic Bayesian network(BN).BN offers unique features of analysing risk scenarios and budget policies.It uses uncertainty and variability of risk and economic factors in its predictions.The framework is illustrated by a case study of agricultural development projects. Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.