Micro simulations on the effects of ageing-related policy measures: The Social Affairs Department of the Netherlands Ageing and Pensions Model

his paper describes a newly extended version of the dynamic micro simulation model SADNAP (Social Affairs Department of the Netherlands Ageing and Pensions model). SADNAP is being developed for calculating the financial and economic implications of the ageing of the population and of the ageing-related policy measures that are being proposed to cope with ageing. The model uses administrative datasets of Dutch public pension payments and entitlements for both public and private pensions. SADNAP has already been in use since 2007 for forecasting the state pension expenditures and for analysing the budgetary effects of policy changes. The model has been extended in order to give a broader assessment of policy alternatives by providing insight into other important evaluation indicators like income redistribution and the retirement decision of workers. For the modelling of income redistribution a new micro data source with individual data on private pensions is combined with differentiation of mortality rates in order to gain a better insight in the income at the individual level within the population of pensioners. For the modelling of the retirement decision an option value model is developed in which key parameters vary at the individual level in order to benefit from the micro simulation approach. These extensions greatly enhance the performance of SADNAP. Besides the financial implications, additional insight can now be provided into the effects of policy measures on a set of key indicators. In this paper both extensions are described in detail and a complete baseline projection of all key indicators is discussed.Microsimulation, ageing, pensions, retirement

Microsimulation as a Decision Making Tool In Social Security Policy

Butter, F.A.G. den [Promotor]Gradus, R.H.J.M. [Promotor

Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports

The way we analyse clinical texts has undergone major changes over the last years. The introduction of language models such as BERT led to adaptations for the (bio)medical domain like PubMedBERT and ClinicalBERT. These models rely on large databases of archived medical documents. While performing well in terms of accuracy, both the lack of interpretability and limitations to transfer across languages limit their use in clinical setting. We introduce a novel light-weight graph-based embedding method specifically catering radiology reports. It takes into account the structure and composition of the report, while also connecting medical terms in the report through the multi-lingual SNOMED Clinical Terms knowledge base. The resulting graph embedding uncovers the underlying relationships among clinical terms, achieving a representation that is better understandable for clinicians and clinically more accurate, without reliance on large pre-training datasets. We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification. For disease classification our model is competitive with its BERT-based counterparts, while being magnitudes smaller in size and training data requirements. For image classification, we show the effectiveness of the graph embedding leveraging cross-modal knowledge transfer and show how this method is usable across different languages

Variational Knowledge Distillation for Disease Classification in Chest X-Rays

Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be further improved, however, by also employing Electronic Health Records (EHRs), which contain rich information on patients and findings from clinicians. It is challenging to incorporate this information into disease classification due to the high reliance on clinician input in EHRs, limiting the possibility for automated diagnosis. In this paper, we propose \textit{variational knowledge distillation} (VKD), which is a new probabilistic inference framework for disease classification based on X-rays that leverages knowledge from EHRs. Specifically, we introduce a conditional latent variable model, where we infer the latent representation of the X-ray image with the variational posterior conditioning on the associated EHR text. By doing so, the model acquires the ability to extract the visual features relevant to the disease during learning and can therefore perform more accurate classification for unseen patients at inference based solely on their X-ray scans. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs. The results show that the proposed variational knowledge distillation can consistently improve the performance of medical image classification and significantly surpasses current methods

Probabilistic Integration of Object Level Annotations in Chest X-ray Classification

Medical image datasets and their annotations are not growing as fast as their equivalents in the general domain. This makes translation from the newest, more data-intensive methods that have made a large impact on the vision field increasingly more difficult and less efficient. In this paper, we propose a new probabilistic latent variable model for disease classification in chest X-ray images. Specifically we consider chest X-ray datasets that contain global disease labels, and for a smaller subset contain object level expert annotations in the form of eye gaze patterns and disease bounding boxes. We propose a two-stage optimization algorithm which is able to handle these different label granularities through a single training pipeline in a two-stage manner. In our pipeline global dataset features are learned in the lower level layers of the model. The specific details and nuances in the fine-grained expert object-level annotations are learned in the final layers of the model using a knowledge distillation method inspired by conditional variational inference. Subsequently, model weights are frozen to guide this learning process and prevent overfitting on the smaller richly annotated data subsets. The proposed method yields consistent classification improvement across different back-bones on the common benchmark datasets Chest X-ray14 and MIMIC-CXR. This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.</p

Psychometric properties of the Dutch version of the Evidence-Based Practice Attitude Scale (EBPAS).

BackgroundThe Evidence-Based Practice Attitude Scale (EBPAS) was developed in the United States to assess attitudes of mental health and welfare professionals toward evidence-based interventions. Although the EBPAS has been translated in different languages and is being used in several countries, all research on the psychometric properties of the EBPAS within youth care has been carried out in the United States. The purpose of this study was to investigate the psychometric properties of the Dutch version of the EBPAS.MethodsAfter translation into Dutch, the Dutch version of the EBPAS was examined in a diverse sample of 270 youth care professionals working in five institutions in the Netherlands. We examined the factor structure with both exploratory and confirmatory factor analyses and the internal consistency reliability. We also conducted multiple linear regression analyses to examine the association of EBPAS scores with professionals' characteristics. It was hypothesized that responses to the EBPAS items could be explained by one general factor plus four specific factors, good to excellent internal consistency reliability would be found, and EBPAS scores would vary by age, sex, and educational level.ResultsThe exploratory factor analysis suggested a four-factor solution according to the hypothesized dimensions: Requirements, Appeal, Openness, and Divergence. Cronbach's alphas ranged from 0.67 to 0.89, and the overall scale alpha was 0.72. The confirmatory factor analyses confirmed the factor structure and suggested that the lower order EBPAS factors are indicators of a higher order construct. However, Divergence was not significantly correlated with any of the subscales or the total score. The confirmatory bifactor analysis endorsed that variance was explained both by a general attitude towards evidence-based interventions and by four specific factors. The regression analyses showed an association between EBPAS scores and youth care professionals' age, sex, and educational level.ConclusionsThe present study provides strong support for a structure with a general factor plus four specific factors and internal consistency reliability of the Dutch version of the EBPAS in a diverse sample of youth care professionals. Hence, the factor structure and reliability of the original version of the EBPAS seem generalizable to the Dutch version of the EBPAS

Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models

Medical Visual Question Answering (VQA) is an important challenge, as it would lead to faster and more accurate diagnoses and treatment decisions. Most existing methods approach it as a multi-class classification problem, which restricts the outcome to a predefined closed-set of curated answers. We focus on open-ended VQA and motivated by the recent advances in language models consider it as a generative task. Leveraging pre-trained language models, we introduce a novel method particularly suited for small, domain-specific, medical datasets. To properly communicate the medical images to the language model, we develop a network that maps the extracted visual features to a set of learnable tokens. Then, alongside the question, these learnable tokens directly prompt the language model. We explore recent parameter-efficient fine-tuning strategies for language models, which allow for resource- and data-efficient fine-tuning. We evaluate our approach on the prime medical VQA benchmarks, namely, Slake, OVQA and PathVQA. The results demonstrate that our approach outperforms existing methods across various training settings while also being computationally efficient.</p

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