In the rapidly evolving field of Machine Learning , selecting the most appropriate model for a given dataset is crucial. Understanding the characteristics of a dataset can significantly influence the outcomes of predictive modeling efforts, making the study of the properties of the dataset an essential component of data science. This study investigates the possibilities of using simulated human data for personalized applications, specifically for testing clustering approaches. In particular, the study focuses on the relationship between dataset characteristics and the selection of the optimal classification model for clusters of datasets. The results of this study provide critical insights for researchers and practitioners in machine learning, emphasizing the importance of dataset characteristics and variability in building and selecting robust models for diverse data conditions. The use of human simulation data provide valuable insights but requires further refinement to capture the full variability of real-world conditions.
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As artificial intelligence (AI) reshapes hiring, organizations increasingly rely on AI-enhanced selection methods such as chatbot-led interviews and algorithmic resume screening. While AI offers efficiency and scalability, concerns persist regarding fairness, transparency, and trust. This qualitative study applies the Artificially Intelligent Device Use Acceptance (AIDUA) model to examine how job applicants perceive and respond to AI-driven hiring. Drawing on semi-structured interviews with 15 professionals, the study explores how social influence, anthropomorphism, and performance expectancy shape applicant acceptance, while concerns about transparency and fairness emerge as key barriers. Participants expressed a strong preference for hybrid AI-human hiring models, emphasizing the importance of explainability and human oversight. The study refines the AIDUA model in the recruitment context and offers practical recommendations for organizations seeking to implement AI ethically and effectively in selection processes.
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In recent decades, the number of cases of knee arthroplasty among people of working age has increased. The integrated clinical pathway ‘back at work after surgery’ is an initiative to reduce the possible cost of sick leave. The evaluation of this pathway, like many clinical studies, faces the challenge of small data sets with a relatively high number of features. In this study, we investigate the possibility of identifying features that are important in determining the duration of rehabilitation, expressed in the return-to-work period, by using feature selection tools. Several models are used to classify the patient’s data into two classes, and the results are evaluated based on the accuracy and the quality of the ordering of the features, for which we introduce a ranking score. A selection of estimators are used in an optimization step, reorganizing the feature ranking. The results show that for some models, the proposed optimization results in a better ordering of the features. The ordering of the features is evaluated visually and identified by the ranking score. Furthermore, for all models, higher accuracy, with a maximum of 91%, is achieved by applying the optimization process. The features that are identified as relevant for the duration of the return-to-work period are discussed and provide input for further research.
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How does a specific lung cancer become resistant towards medication.The occurrence of a chromosomal translocation resulting in a ROS1 gene fusion in lung cancer is relatively rare with around 1-2% of all cases. Both Dutch (Stichting Merels Wereld) and world-wide (ROS1ders) patient advocacy groups work hard to raise awareness and bring researchers together to close the knowledge gap on ROS1 driven oncogenesis and increase the optionsfor treatment. A notorious hurdle is to achieve durable responses due to development of resistance.Ongoing mutations occurring in tumour cells lead to a heterogeneous genomic landscape and will result in outgrowth of the fastest growing tumour cell population resistant to the applied drug. The currently known resistance mechanisms can be divided in on-target (i.e. mutations in the kinasedomain of ROS1) and off-target (providing ROS1 independent growth support) mechanisms. The currently available drugs target the ROS1-fusion gene positive lung cancer cells. In addition, some of the drugs also target cancer cells with specific ROS1 resistance mutations allowing effective sequentialtreatment upon disease progression. Selection of the most optimal treatment is largely a ‘trial and error’ approach. Patients and their doctors ask for better prediction of the most effective follow-up treatment upon development of resistance. Medical Life Science & Diagnostics can help to improvetreatment by developing cell culture models which mimic the situation in resistant tumour cells.Understanding the impact of specific mutations on disease behaviour will aid in the development of patient-tailored therapeutic approaches, ultimately improving patient outcomes.
While several governmental and research efforts are set upon mobility-as-a-service (MaaS), most of them are driven by individual travel behavior and potential usage. Scholars argue that this is a too narrow perspective when evaluating government projects because choices individuals make in a private setting might not accurately reflect their preferences towards public policy. Participatory Value Evaluation (PVE) is a novel evaluation framework specifically designed to alleviate this issue by analyzing preferences on the allocation of public budgets. Thus, based on PVE, this project aims at assessing different features of MaaS-services (e.g. enhancing mobility of the elderly and the poor, complementing public transport, etc.) from a social desirability perspective and compare them with investments in alternative social projects. Specifically, it aims at establishing the citizen value of MaaS as compared to social investments in green/recreational areas or transport infrastructure (e.g. bike or bus lanes), and eliciting trade-offs between different features of them. The project includes the selection of different investment projects (and their features) that are politically relevant in Rotterdam. It also includes a qualitative assessment on the way individuals evaluate different social projects and their features and a quantitative assessment based on choice models that allow eliciting trade-offs between different attributes and projects. Finally, policy recommendations are provided based on these results. They allow conceiving investments projects to maximize the societal benefits as well as to construct optimal investment portfolios. This information is to be used as a complement of the evaluation of projects on the basis of individual preferences.
Regular physical activity is considered to be an important component of a healthy lifestyle that decreases the risk of coronary heart disease, diabetes mellitus type 2, hypertension, colon and breast cancer, obesity and other debilitating conditions. Physical activity can also improve functional capacity and therefore also the quality of life in older adults. Despite all these favorable aspects, a substantial part of the Dutch older adult population is still underactive or even sedentary. To change this for the better, the Groningen Active Living Model (GALM) was developed.Aim of GALM is to stimulate recreational sports activities in sedentary and underactive older adults in the 55-65 age band. After a door-to-door visit as part of an intensive recruitment phase, a fitness test was conducted followed by the GALM recreational sports program. This program was based on principles from evolutionary-biological play theory and insights fromsocial cognitive theory. The program was versatile in nature (e.g. softball, dance, self-defense, swimming, athletics, etc.) in two main ways: a) to improve compliance with the program different sports were offered, which was reported to be more appealing for older adults; b) by aiming at more components of motor fitness (e.g. strength, flexibility, speed, endurance and coordination). Between 1997 and 2005 more than 552,000 persons were visited door-to-door, over 55,700 were tested, and 41,310 participated in the GALM recreational sports program. The aim of the present thesis is to determine the effects of participation in the GALM recreational sports program on physical activity, health and fitness outcomes.Chapter 2 describes the effectiveness of the GALM recruitment in selecting and recruiting sedentary and underactive older adults. Three municipalities in the Netherlands were selected, and in every municipality four neighborhoods were included. Two of each of the four neighborhoods were randomly assigned as intervention and the others as control neighborhoods. In total, 8,504 persons were mailed and received a home visit. During this home visit the GALM recruitment questionnaire was collected on which the selection between sedentary/underactive and physically active older adults was based. Ultimately we succeeded inincluding 12.3% (315 of the 2,551 qualifying) of the older adults, 79.4% of whom could be indeed considered sedentary or underactive. The cost of successfully recruiting an older adult was estimated at $84.To assess the effects of a physical activity intervention on health and fitness and explain the results, it is necessary to know program characteristics regarding frequency, intensity, time and content of the activities. With respect to the GALM recreational sports activity program, the only unknown characteristic was intensity. Chapter 3 describes the intensity of this program systematically. Using heart rate monitors, data of 97 persons (mean age 60.1 yr) were collected in three municipalities. The mean intensity of all 15 GALM sessions was 73.7% of the predicted maximal heart rate. Six percent of the monitored heart rate time could be classified as light, 33% as moderate and 61% as hard. In summary, the GALM recreational sports program meets the 1998 ACSM recommendations for intensity necessary to improve cardiorespiratory fitness.Chapters 4 and 5 describe the effects of 6 and 12 months of participation in the GALM recreational sports program, and 181 persons were followed over time. Results after 6 months revealed only few significant between-group differences favoring the intervention group (i.e. sleep, diastolic blood pressure, perceived fitness score and grip strength). Changes in energyexpenditure for leisure-time physical activities (EELTPA) showed an increase in both study groups. From 6 to 12 months a decrease in EELTPA occurred in the intervention group and an increase in the control group. The significant positive time effects for the health outcomes (diastolic blood pressure, BMI, percentage of body fat) that were found after 6 months were diminishedfrom 6 to 12 months. However, the energy expenditure for recreational sports activities (EERECSPORT) demonstrated a continuous increase over 12 months. Parallel to this, significant main effects for time were found in performance-based fitness outcomes (i.e. simple reaction time, leg strength, flexibility of hamstrings and lower back, and aerobic endurance). After 12 months only a significant between-group difference for flexibility of the hamstrings andlower back was found, favoring the control group. In conclusion, a short-term increase in EELTPA was found with accompanying improvements in health outcomes that more or less disappeared in 6 to 12 months. In the long term, results showed a continuous increase in EERECSPORT and performance-based fitness. This latter increase is probably a reflection of the significantimprovement over time in EERECSPORT and the fact that recreational sports activities are of a higher intensity.Aerobic endurance is regarded as the most important component of motor fitness that is relevant for older adults to function independently. In Chapter 6, the development in aerobic endurance after 18 months of participation in the GALM recreational sports program was assessed by means of changes in heart rate during fixed submaximal exercise. Since both groups were comparable regarding changes in energy expenditure for physical activity after 6 months and testing confirmed this, both groups were combined and considered as one group. Multilevel analyses were conducted and models for change were developed. A significant decrease in heart rate over time was found at all walking speeds (4, 5, 6 and 7 km/h). The average decrease in heart rate was 5.5, 6.0, 10.0 and 9.0 beats/min for the 4, 5, 6 and 7 km/h walking speeds, respectively. The relative decrease varied from 5.1 to 7.4% relative to average heart rates at baseline. These results illustrate that participation in the GALM recreational sports program has a positive significant effect on aerobic endurance, and that the participants are able to perform at submaximal intensity more easily.Based on the overall results it can be concluded that this study contributes to the field in how to effectively recruit sedentary and underactive older adults and stimulate them to become and stay active in recreational sports activities. As far as we know, this recruitment in combination with the recreational sport program is not only unique but also effective toward increasing performance-based fitness in the long term. Short-term effects were found in other leisure-time activities and health outcomes. To further stimulate other leisure-time and probably health outcomes besides the favorable effects that were already seen, additional interventions that pay more attention to behavioral change in terms of how to integrate other activities besides sports activities are recommended.
