Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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Coupling beams between shear walls are one of the key elements for energy dissipation in tall buildings. A representative mathematical model of coupling beam should represent flexure, shear and interface slip/extension mechanisms simultaneously. This goal can be achieved by using either detailed finite element models or by using macro models. This paper presents a review of various macro model alternatives for diagonally reinforced coupling beams in the literature. Three distinct methods have been reviewed in terms of their modeling techniques, the cyclic response overlap and the amount of cumulative plastic energy dissipated based on the results of previously performed tests. Through an analytical study, adequately accurate results can be captured by using macro models, although they are simpler in practice compared to sophisticated micro models. This study shows that, by modifying ultimate shear capacities where concrete material between diagonal bundles is adequately confined, it is possible to capture a more realistic result and a better approximation to the actual responses. It is also concluded that a simpler numerical model for diagonally reinforced coupling beams can be achieved by introducing linear part of slip/extension behavior into elastic part of the beam. It is observed, as a result of this study, that the ratio of effective stiffness to that of the gross cross-sectional one ranges from 0.04 to 0.14 in diagonally reinforced coupling beams depending on the aspect ratio and the beam strength parameters.
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Background: Behaviour change techniques (BCTs) can be employed to support a healthy lifestyle for people with intellectual disabilities. The aim of this study is to determine whether and which BCTs are used by direct support professionals (DSPs) for supporting healthy lifestyle behaviour of people with moderate to profound intellectual disabilities. Method: Direct support professionals (n = 18) were observed in their daily work using audio-visual recordings. To code BCTs, the Coventry Aberdeen London Refined (CALO-RE-NL) taxonomy was employed. Results: Direct support professionals used 33 BCTs out of 42. The most used BCTs were as follows: ‘feedback on performance’, ‘instructions on how to perform the behaviour’, ‘doing together’, ‘rewards on successful behaviour’, ‘reward effort towards behaviour’, ‘DSP changes environment’, ‘graded tasks’, ‘prompt practice’ and ‘model/demonstrate behaviour’. Conclusions: Although a variety of BCTs is used by DSPs in their support of people with moderate to profound intellectual disabilities when facilitating healthy lifestyle behaviour, they rely on nine of them.
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Background: Professional caregivers are important in the daily support of lifestyle change for adults with mild intellectual disabilities; however, little is known about which behaviour change techniques (BCTs) are actually used. This study aims to gain insight in their use for lifestyle behaviour change using video observations.Methods: Professional caregivers (N = 14) were observed in daily work supporting adults with mild intellectual disabilities. Videos were analysed using the Coventry Aberdeen London Refined (CALO-RE-NL) taxonomy and BCTs utilised were coded.Results: Twenty one out of 40 BCTs were used by professional caregivers. The BCTs ‘Information about others' approval’, ‘Identification as role model’, ‘Rewards on successful behaviour’, ‘Review behavioural goals’ and ‘Instructions on how to perform the behaviour’ were most employed.Conclusion: Professional caregivers used BCTs to support healthier lifestyle behaviour of adults with mild intellectual disabilities. However, most promising of them as defined previous by professionals were rarely used by professional caregivers.
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In this paper, a general approach for modeling airport operations is presented. Airport operations have been extensively studied in the last decades ranging from airspace, airside and landside operations. Due to the nature of the system, simulation techniques have emerged as a powerful approach for dealing with the variability of these operations. However, in most of the studies, the different elements are studied in an individual fashion. The aim of this paper, is to overcome this limitation by presenting a methodological approach where airport operations are modeled together, such as airspace and airside. The contribution of this approach is that the resolution level for the different elements is similar therefore the interface issues between them is minimized. The framework can be used by practitioners for simulating complex systems like airspace-airside operations or multi-airport systems. The framework is illustrated by presenting a case study analyzed by the authors.
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Dutch National Sports Organizations (NSFs) is currently experiencing financial pressures. Two indications for this are described in this paper i.e. increased competition in the sports sector and changes in subsidy division. Decreasing incomes from subsidies can be compensated with either increasing incomes from a commercial domain or increasing incomes from member contributions. This latter solution is gaining interest as a solution for the uncertainties. Many NSFs have therefore participated in a special marketing program in order to enlarge their marketing awareness and create a marketing strategy, in order to (re)win market share on the sports participation market and gain a more stable financial situation. This paper introduces my research related to the introduction of marketing techniques within NSFs and the change-over to become market oriented. An overview of existing literature about creating marketing strategies, their implementation, and market orientation is given. This outline makes obvious that the existing literature is not sufficient for studying the implementation of marketing techniques and market orientation within NSFs. Therefore, it shows the scientific relevance of my research. The paper concludes with the chosen research methodology.
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Epidemiological miner cohort data used to estimate lung cancer risks related to occupational radon exposure often lack cohort-wide information on exposure to tobacco smoke, a potential confounder and important effect modifier. We have developed a method to project data on smoking habits from a case-control study onto an entire cohort by means of a Monte Carlo resampling technique. As a proof of principle, this method is tested on a subcohort of 35,084 former uranium miners employed at the WISMUT company (Germany), with 461 lung cancer deaths in the follow-up period 1955–1998. After applying the proposed imputation technique, a biologically-based carcinogenesis model is employed to analyze the cohort's lung cancer mortality data. A sensitivity analysis based on a set of 200 independent projections with subsequent model analyses yields narrow distributions of the free model parameters, indicating that parameter values are relatively stable and independent of individual projections. This technique thus offers a possibility to account for unknown smoking habits, enabling us to unravel risks related to radon, to smoking, and to the combination of both.
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From the article: Though organizations are increasingly aware that the huge amounts of digital data that are being generated, both inside and outside the organization, offer many opportunities for service innovation, realizing the promise of big data is often not straightforward. Organizations are faced with many challenges, such as regulatory requirements, data collection issues, data analysis issues, and even ideation. In practice, many approaches can be used to develop new datadriven services. In this paper we present a first step in defining a process for assembling data-driven service development methods and techniques that are tuned to the context in which the service is developed. Our approach is based on the situational method engineering approach, tuning it to the context of datadriven service development. Published in: Reinhartz-Berger I., Zdravkovic J., Gulden J., Schmidt R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS 2019, EMMSAD 2019. Lecture Notes in Business Information Processing, vol 352. Springer. The final authenticated version of this paper is available online at https://doi.org/10.1007/978-3-030-20618-5_11.
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