A goal-based weighting for composite indicators constructed through Ordered Weighted Averaging (OWA) operator.

The Ordered Weighted Averaging (OWA) operator is a multicriteria method that has conquered space among researchers in the composite indicators field. Typically, OWA operator weights are defined by the decision maker. This type of weighting is highly criticized, as decision-makers are susceptible to errors and bias in judgment. Some methods have been used to define OWA operator weights objectively. However, none of them is concerned about the quality of the composite indicator. This paper introduces a method that defines the weights of the OWA operator based on two quality parameters of the composite indicator: the ability to capture the concept of the multidimensional phenomenon and the informational loss. The method can be implemented in Microsoft Excel Solver and has a high degree of flexibility and applicability in problems of a multidimensional nature and a high degree of appropriation by researchers and practitioners in the area.•Defines weights that maximize the ability of the composite indicator to capture the concept of the multidimensional phenomenon.•Considers restrictions to limit the informational loss of the composite indicator or emphasize positive or negative aspects of the multidimensional phenomenon.•Offers flexibility in setting the objective and constraints of the optimization algorithm.

A qualitative model of the HIV care continuum in Vancouver, Canada.

A team of health care stakeholders and researchers collaboratively developed a qualitative model and graphic representation of the continuum of HIV care in Vancouver to inform delivery of antiretroviral therapy and other HIV health services. The model describes the patient journey through the HIV care continuum, including states of infection, health services, and care decisions. We used a Unified Modelling Language (UML) activity diagram to capture patient and provider activities and to guide the construction of a UML state machine diagram. The state machine diagram captures model agent states in a formalism that facilitates the development of system dynamics or agent-based models. These quantitative models can be applied to optimizing the allocation of resources, and to evaluate potential strategies for improved patient care and system performance. The novel approach of combining UML diagrams we present provides a general method for modelling capacity ---management strategies within complex health systems.

Prediction of atherosclerosis using machine learning based on operations research.

BACKGROUND: Atherosclerosis is one of the major reasons for cardiovascular disease including coronary heart disease, cerebral infarction and peripheral vascular disease. Atherosclerosis has no obvious symptoms in its early stages, so the key to the treatment of atherosclerosis is early intervention of risk factors. Machine learning methods have been used to predict atherosclerosis, but the presence of strong causal relationships between features can lead to extremely high levels of information redundancy, which can affect the effectiveness of prediction systems. OBJECTIVE: We aim to combine statistical analysis and machine learning methods to reduce information redundancy and further improve the accuracy of disease diagnosis. METHODS: We cleaned and collated the relevant data obtained from the retrospective study at Affiliated Hospital of Nanjing University of Chinese Medicine through data analysis. First, some features that with too many missing values are filtered out of the 34 features, leaving 25 features. 49% of the samples were categorized as the atherosclerosis risk group while the rest 51% as the control group without atherosclerosis risk under the guidance of relevant experts. We compared the prediction results of a single indicator that had been medically proven to be highly correlated with atherosclerosis with the prediction results of multiple features to fully demonstrate the effect of feature information redundancy on the prediction results. Then the features that could distinguish whether have atherosclerosis risk or not were retained by statistical tests, leaving 20 features. To reduce the information redundancy between features, after drawing inspiration from graph theory, machine learning combined with optimal correlation distances was then used to screen out 15 significant features, and the prediction models were evaluated under the 15 features. Finally, the information of the 5 screened-out non-significant features was fully utilized by ensemble learning to improve the prediction superiority for atherosclerosis. RESULTS: Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), which is used to measure the predictive performance of the model, was 0.84035 and Kolmogorov-Smirnov (KS) value was 0.646. After feature selection model based on optimal correlation distance, the AUC value was 0.88268 and the KS value was 0.688, both of which were improved by about 0.04. Finally, after ensemble learning, the AUC value of the model was further improved by 0.01369 to 0.89637. CONCLUSIONS: The optimal distance feature screening model proposed in this paper improves the performance of atherosclerosis prediction models in terms of both prediction accuracy and AUC metrics. Code and models are available at https://github.com/Cesartwothousands/Prediction-of-Atherosclerosis.

A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network.

BACKGROUND: Widely spread of the COVID-19 virus has put the whole world in jeopardy. At this moment, using new techniques to detect and treat this novel disease is of significance or maybe the first priority of many scientists and researchers throughout the world. PURPOSE: To present a new algorithm for detecting the novel coronavirus 2019 using chest CT images with high accuracy. MATERIALS AND METHODS: In this study, we looked at the newly-presented data and detection methods of this disease using chest CT; then, a new neural network algorithm was presented to recognize the COVID-19 symptoms. A mathematical model is used to enhance the accuracy of masking, and a high accuracy Hopfield Neural Network (HNN) is used for finding symptoms. A dataset of CT scans, including 12 pattern images, was trained by this neural network, and 295CT images from three different datasets were tested via the model. RESULTS: The sensitivity and specificity of the model for detecting COVID-19 in test data were 97.4% (149 of 153) and 98.6% (140 of 142) respectively. Also, the sensitivity and specificity of the model for detecting CAP (community-acquired pneumonia) in test data were 97.3% (106 of 109) and 99.5% (185 of 186) respectively, and, the sensitivity and specificity of the model for detecting non-pneumonia patients were 100% (33 of 33) and 98.5% (258 of 262) respectively. CONCLUSION: This new algorithm can potentially help detect the novel Coronavirus patients using CT images.

Virtual diabetic patient with physical activity dynamics.

BACKGROUND AND OBJECTIVE: Diabetes is a disease of impaired blood glucose regulation due to the absence or insufficient secretion of insulin hormone or insulin resistance induced in the human body. In literature, the impact of exercise is considered in few models based on the minimal representation of glucose dynamics along with the assumption that no endogenous insulin is produced in the body. Hence these models are not capable of describing diabetic behavior which is independent of exogenous insulin. This type of diabetes, known as type-2, affects almost 90% of the total diabetes population. In this article, a constraint-based comprehensive physiological model of blood glucose dynamics is aimed to build for filling up the gap in the literature. METHODS: For physiological comprehensiveness, the model is considered to consist of several compartments separately connected with a common compartment named 'plasma'. Plasma is the only accessible compartment and contains the state variables. Plasma variables are the integrated result of the net change in rates of metabolic processes and basal rates are influenced between two saturation constraints for an operating range of each plasma variable. The influence of a plasma variable on a metabolic rate is represented using a form of the hyperbolic tangent function. Validation is done by fitting the model with clinical experiments and continuous glucose monitoring data of a free-living environment. RESULTS: The proposed model generates an average correlation coefficient of 0.85 ± 0.13 on all simulated responses with the target in the fitting experiments. Besides this, the model can produce a spectrum of metabolic effects of plasma variables for showing more insight into glucose metabolism. CONCLUSIONS: A constraint-based comprehensive glucose regulation with exercise dynamics for modeling diabetes is pursued. The model doesn't consider age, gender, physical, and mental condition of the human body but can be applied in operation research by mathematical programming.

Medical heuristics and action-research: professionalism versus science.

BACKGROUND: Professional knowledge aims at improving practice. It reduces uncertainty in decision-making, improves effectiveness in action and relevance in evaluation, stimulates reflexivity, and subjects practice to ethical standards. Heuristics is an approach to problem-solving, learning, and discovery employing a practical methodology that, although not optimal, is sufficient for achieving immediate goals. This article identifies the desirable, heuristic particularities of research in professional, medical practice; and it identifies what distinguishes this research from scientific research. MAIN TEXT: We examine the limits of biomedical and sociological research to produce professional knowledge. Then, we derive the heuristic characteristics of professional research from a meta-analysis of two action-research projects aimed at securing access to essential generic drugs in Senegal and improving physicians' self-assessment and healthcare coordination in Belgium. To study healthcare, biomedical sciences ignore how clinical decisions are implemented. Decisions are built into an articulated knowledge system, such as (clinical) epidemiology, where those studied are standardisable - while taking care of patients is an idiosyncratic, value-based, person-to-person process that largely eludes probabilistic methodologies. Social sciences also reach their limits here because descriptive, interpretative methods cannot help with gesture and speech quality, while the management of the patient's suffering and risks makes each of them unique. Research into medical professionalism is normative as it is intended to formulate recommendations. Scientific data and descriptions are useful to the practitioner randomly, only from the similarities in the environment of the authors and their readers. Such recommendations can be conceived of as strategies, i.e., multi-resource and multi-stage action models to improve clinical and public health practice. Action learning and action-research are needed to design and implement these strategies, because their complexity implies trial and error. To validate a strategy, repeated experiences are needed. Its reproducibility assumes the description of the context. To participate in medical action-research, the investigator needs professional proficiency - a frequent difficulty in academic settings. CONCLUSION: Some criteria to assess the relevance of publicly funded clinical and public health research can be derived from the difference between scientific and professional knowledge, i.e. the knowledge gained with real-life experience in the field.

Gamification in engineering education - An empirical assessment on learning and game performance.

Gamification, recently considered as a science, takes advantage of the benefits of games to induce desirable behaviors in a given "normal" activity. When applied in education, it is an approach to motivate and engage students in their learning process by incorporating game design principles. This paper presents the design and deployment of a game conducted in parallel with two groups of the engineering course "operations research". The game design is theoretically supported, and unlike literature, the game proposed has the main distinguishing features: (a) it is carried out in parallel with the standard course, where player participation is optional, with extrinsic motivators regarding the final grade, (b) it lasts the entire semester, and it was applied to different groups with the same instructor, (c) in addition to academic performance, it has other social relatedness desired outcomes, (d) it combines the use of specific game elements divided in three types of activities: Mastery, related to the core topics of the class, Institutional, related to the university life and community, and Teamwork activities; and (e) it uses a WhatsApp chat group as the common communication platform. The assessment is twofold: the effects on learning, measured in two indicators, failure rate and average grade; and the perception of the game itself. Statistical results present empirical evidence of the positive effects of gamification on academic performance and other desired behaviors of social relatedness, such as a sense of belonging and teamwork.

Benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths.

In this paper, the benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths is presented (AC-VRP-SPDVCFP). This problem is a specific multi-attribute variant of the well-known Vehicle Routing Problem, and it has been originally built for modelling and solving a real-world newspaper distribution problem with recycling policies. The whole benchmark is composed by 15 instances comprised by 50-100 nodes. For the design of this dataset, real geographical positions have been used, located in the province of Bizkaia, Spain. A deep description of the benchmark is provided in this paper, aiming at extending the details and experimentation given in the paper A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy (Osaba et al.) [1]. The dataset is publicly available for its use and modification.

Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review.

BACKGROUND: Business process modelling is increasingly used not only by the companies' management but also by scientists dealing with process models. Process modeling is seldom done without decision-making nodes, which is why operational research methods are increasingly included in the process analyses. OBJECTIVE: This systematic literature review aimed to provide a detailed and comprehensive description of the relevant aspects of used operational research techniques in Business Process Model and Notation (BPMN) model. METHODS: The Web Of Science of Clarivate Analytics was searched for 128 studies of that used operation research techniques and business process model and notation, published in English between 1 January 2004 and 18 May 2020. The inclusion criteria were as follows: Use of Operational Research methods in conjunction with the BPMN, and is available in full-text format. Articles were not excluded based on methodological quality. The background information of the included studies, as well as specific information on the used approaches, were extracted. RESULTS: In this research, thirty-six studies were included and considered. A total of 11 specific methods falling into the field of Operations Research have been identified, and their use in connection with the process model was described. CONCLUSION: Operational research methods are a useful complement to BPMN process analysis. It serves not only to analyze the probability of the process, its economic and personnel demands but also for process reengineering.

Selección de hospital destino para el traslado de urgencia de pacientes / Selecting the Destination Hospital to Carry Emergency Patients / Escolha de hospital alvo para traslado de urgência de pacientes

Resumen El tiempo total de atención prehospitalaria (APH) es el tiempo que transcurre desde que ingresa la llamada al operador hasta que la ambulancia queda disponible para atender otra emergencia. Esta investigación pretende demostrar que la selección del hospital destino afecta de manera significativa el tiempo total de APH, lo que influye en la supervivencia del paciente que es trasladado y en el tiempo de liberación del recurso (ambulancias). En consecuencia, se propone una técnica de selección de hospital destino que incluye dimensiones relacionadas con el paciente (diagnóstico, especialidad y asegurador) y el hospital (ocupación y cercanía). Se evalúa su desempeño por medio de una simulación de eventos discretos y se concluye que la técnica propuesta obtiene un mejor tiempo de APH en el 73% de los casos estudiados, con una reducción media entre 40 y 80 minutos, en comparación con la técnica más comúnmente usada (selección hospital más cercano).

Abstract Total time of prehospital care (PHC) is the time elapsing from the inbound call up to the moment when the ambulance is available for serve in another emergency event. This research aims to show that selecting the destination hospital impacts significantly the total PHC time, which influences the survival of the patient being transported as well as the time to make the resource available again (the ambulance). Consequently, a technique for selecting the destination hospital is proposed herein including some dimensions related both to the patient (diagnosis, specialty and insurance company) and to the hospital (occupancy and closeness). The performance was evaluated based on a simulation of discrete events. It is concluded that the proposed technique provides a better PHC time in 73% of the studied cases, with a mean decrease between 40 and 80 minutes as compared to the most commonly used technique (selecting the closest hospital).

Resumo O tempo total de atendimento pré-hospitalar (APH) é o tempo decorrente desde que a ligação for feita para o telefonista até a ambulância se disponibilizar para atender outra emergência. Esta pesquisa visa demostrar que a escolha do hospital alvo afeta significativamente o tempo total de APH, o que influi na sobrevida do paciente trasladado e no tempo de liberação do recurso (ambulâncias). Consequentemente, propõe-se uma técnica de escolha de hospital alvo que inclui dimensões relacionadas com paciente (diagnóstico, especialidade e assegurador) e hospital (ocupação e proximidade). Avalia-se o desempenho por meio de simulação de eventos discretos e conclui-se que a técnica proposta obtém melhor tempo de APH em 73% dos casos estudados com redução media entre 40 e 80 minutos, em comparação com a técnica mais comumente usada (escolha hospital mais próximo).

Alarming prevalence and clustering of modifiable noncommunicable disease risk factors among adults in Bhutan: a nationwide cross-sectional community survey.

BACKGROUND: Bhutan is currently facing a double burden of non-communicable (NCDs) and communicable diseases, with rising trends of NCDs. The 2014 STEPS survey identified high prevalence of several NCD risk factors; however, associations with socio-demographic characteristics as well as clustering of risk factors were not assessed. This study aimed to determine the distribution and clustering of modifiable NCD risk factors among adults in Bhutan and their demographic and social determinants. METHODS: This was secondary analysis of data from NCD Risk Factors WHO STEPS Survey 2014 in Bhutan. A weighted analysis was conducted to calculate the prevalence of NCD risk factors, and associations were explored using weighted log-binomial regression models. RESULTS: This study included 2822 Bhutanese aged 18-69 years; 52% were 18-39 years, 62% were female, and 69% were rural resident. Prevalence of high salt intake, unhealthy diet and tobacco use were 99, 67 and 25% respectively. Raised blood pressure was the commonest (36%) modifiable biological risk factor followed by overweight (33%). The median NCD risk factors per person was 3 (Inter Quartile Range: 2-4); 52.5%% had > = 3 risk factors. A statistically significant difference was found between male vs. female in alcohol consumption(aPR 0.71, 95% CI: 0.53-0.97), low physical activity(aPR 2.06, 95% CI: 1.54-2.75), impaired fasting glycaemia(aPR 1.24, 95% CI: 1.01-1.52), and being overweight(aPR 1.46, 95% CI: 1.31-1.63). Low physical activity was more common among those with secondary and above education level vs. those without any formal education(aPR 1.71, 95% CI: 1.24-2.35), and among those residing in urban areas vs. those in rural(aPR 3.43, 95% CI: 2.27-5.18). Older participants and urban residents were more likely to have > = 3 NCD risk factors compared to younger(aPR 1.46, 95% CI: 1.35-1.58) and rural residents(aPR 1.21, 95% CI: 1.10-1.32). CONCLUSION: Lifestyle modifications at the population level are urgently required in Bhutan as several NCD risk factors such as high salt intake, unhealthy diet, overweight, and high blood pressure were alarmingly high and frequently clustered. Moreover there is a need to consider policy and socio-political and economic factors that have undermined global and national progress to address the rise of NCDs and their risk factors in Bhutan as elsewhere.

Experience in Applying Queuing Mathematical Model in Hospital Electronic Triage System / 医疗卫生装备

Objective To Describe the queuing mathematical model and analyze the reasonable hospital application pattern of electronics voluntary system.Methods By scientifically arranging medical care personnel and equipment,patients queuing process was optimized.Results We successfully applied the outpatient real-time computer information management system of electronics voluntary based on this mathematical model.Conclusion It is a possible way to optimize the queuing process and improve the efficiency in electronic triage system based on queuing theory.