The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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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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Het aantal digitale documenten in bedrijfsomgevingen neemt dermate toe dat men het beoordelen ervan niet kan overlaten aan experts. Met behulp van slimme software kan men de relevantie van elektronische documenten voorspellen. Een van de nieuwste technieken is predictive coding die de voorspelling doet aan de hand van een model.
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The Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) is a risk assessment instrument for adolescents that estimates the risk of multiple adverse outcomes. Prior research into its predictive validity is limited to a handful of studies conducted with the START:AV pilot version and often by the instrument’s developers. The present study examines the START:AV’s field validity in a secure youth care sample in the Netherlands. Using a prospective design, we investigated whether the total scores, lifetime history, and the final risk judgments of 106 START:AVs predicted inpatient incidents during a 4-month follow-up. Final risk judgments and lifetime history predicted multiple adverse outcomes, including physical aggression, institutional violations, substance use, self-injury, and victimization. The predictive validity of the total scores was significant only for physical aggression and institutional violations. Hence, the short-term predictive validity of the START:AV for inpatient incidents in a residential youth care setting was partially demonstrated and the START:AV final risk judgments can be used to guide treatment planning and decision-making regarding furlough or discharge in this setting.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
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Spontaneous speech is an important source of information for aphasia research. It is essential to collect the right amount of data: enough for distinctions in the data to become meaningful, but not so much that the data collection becomes too expensive or places an undue burden on participants. The latter issue is an ethical consideration when working with participants that find speaking difficult, such as speakers with aphasia. So, how much speech data is enough to draw meaningful conclusions? How does the uncertainty around the estimation of model parameters in a predictive model vary as a function of the length of texts used for training?
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Since 2016, it is mandatory for all future students at the department of Media, Information and Communication, to participate in a study choice test (SCT), prior their enrollment. However, the outcome is not binding and students are still entitled to enter the first year after receiving negative advice. With the help of a structural model, built for my PhD research, the predictive value of the SCT is tested by comparing the time it takes the students to finish all first year exams, their average grade point and attrition, against the results of the SCT. By using the structural model, various background variables are also measured, such as engagement, effort and commitment are also measured. By using the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA), the fit of the model is established. In addition, a comparison of the direct and indirect influence of the SCT will provide more knowledge about the correlations between the different variables, the SCT and ultimately student success.
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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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If brief and easy to use self report screening tools are available to identify frail elderly, this may avoid costs and unnecessary assessment of healthy people. This study investigates the predictive validity of three self-report instruments for identifying community-dwelling frail elderly.
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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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