The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency,... Show moreThe research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels. If either of these levels are inadequate, donors are deferred from donation. Deferral due to low hemoglobin levels occurs on-site, meaning that donors have already traveled to the blood bank and then have to return home without donating, which is demotivating for the donor and inefficient for the blood bank. A large part of this dissertation therefore has the objective to develop a prediction model for donors' hemoglobin levels, based on historical measurements and donor characteristics.The prediction model that was developed reduces the deferral rate by approximately 60\% (from 3\% to 1\% for women, and from 1\% to 0.4\% for men), showing the potential of using data to enhance blood bank policy efficiency. Additionally, the model predictions were made explainable, providing the blood bank with insights into why specific predictions are made. These insights increase our understanding of the relationships between donor characteristics and hemoglobin levels. If this prediction model would be implemented in practice, the explanations could also be shared with the donor to help them understand why they are (not) invited to donate, which could also contribute to donor satisfaction and retention.In a collaborative effort with blood banks in Australia, Belgium, Finland and South Africa, the same prediction model was applied on data from each blood bank. Despite differences in blood bank policies and donor demographics, the models found similar associations with the predictor variables in all countries. Differences in performance could mostly be attributed to differences in deferral rates, with blood banks with higher deferral rates obtaining higher model accuracy.Beyond hemoglobin prediction models, additional research questions are explored. One study aims to identify determinants of ferritin levels in donors through repeated measurements, and linking these to environmental variables. Another study involves modeling the pharmacokinetics of antibodies in COVID-19 recovered donors, and finding relationships between patient characteristics, symptoms, and antibody levels over time.In summary, the research in this dissertation shows the potential within the wealth of data collected by blood banks. The proposed data-driven donation strategies not only decrease deferral rates but also increase donor retention and understanding. This comprehensive approach allows Sanquin to provide more personalised feedback to donors regarding their iron status, ultimately optimising the blood donation process and contributing to the overall efficacy of blood banking systems. Show less
Archaeologists are creating ever-increasing amounts of textual data. So much in fact, that manual reading and inspection has become practically impossible. By leveraging computational approaches,... Show moreArchaeologists are creating ever-increasing amounts of textual data. So much in fact, that manual reading and inspection has become practically impossible. By leveraging computational approaches, it is possible to extract relevant information from this big data, allowing for more efficient research and new analyses. In this chapter, methods and techniques to extract information from archaeological texts through Machine Learning are introduced and discussed, with a focus on practical examples. After reading the chapter, you should have a clear grasp on the possibilities of text mining in archaeology, the current state of research, and enough information to start your own text analyses. Show less
In the past years access to EU law has been significantly enhanced via services such as EUR-Lex. This development not only allows for easy retrieval of individual legal acts, but for collecting... Show moreIn the past years access to EU law has been significantly enhanced via services such as EUR-Lex. This development not only allows for easy retrieval of individual legal acts, but for collecting information about the evolution of EU law in the aggregate as well. This contribution argues that by charting and analysing the evolution of the body of EU law over time, we can understand better the nature and development of the EU as a political system. The text examines the legislative productivity of the EU over the past 15 years as an illustration. Further, it showcases recent examples of the use of novel data-analytic techniques to analyse the body of EU law for the purposes of understanding the EU legal system, the institutions, and the polity that produced the legal acts. The contribution concludes by arguing that it is important to transmit basic facts and insights about the evolution of EU law and law-making to the general public as well, in order to counter the threat of Euroscepticism and perceptions of democratic deficit in the EU. Show less
Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and Setting: We... Show moreObjective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified.Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study.Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. (C) 2020 The Authors. Published by Elsevier Inc. Show less
Cammel, S.A.; Vos, M.S. de; Soest, D. van; Hettne, K.M.; Boer, F.; Steyerberg, E.W.; Boosman, H. 2020
Background Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language... Show moreBackground Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for quality improvement. Methods This retrospective study used routinely collected patient experience data from two hospitals. A data-driven NLP approach was used. Free-text responses were categorized into topics, subtopics (i.e. n-grams) and labelled with a sentiment score. The indicator 'impact', combining sentiment and frequency, was calculated to reveal topics to improve, monitor or celebrate. The topic modelling architecture was tested on data from a second hospital to examine whether the architecture is transferable to another hospital. Results A total of 38,664 survey responses from the first hospital resulted in 127 topics and 294 n-grams. The indicator 'impact' revealed n-grams to celebrate (15.3%), improve (8.8%), and monitor (16.7%). For hospital 2, a similar percentage of free-text responses could be labelled with a topic and n-grams. Between-hospitals, most topics (69.7%) were similar, but 32.2% of topics for hospital 1 and 29.0% of topics for hospital 2 were unique. Conclusions In both hospitals, NLP techniques could be used to categorize patient experience free-text responses into topics, sentiment labels and to define priorities for improvement. The model's architecture was shown to be hospital-specific as it was able to discover new topics for the second hospital. These methods should be considered for future patient experience analyses to make better use of this valuable source of information. Show less
Wynants, L.; Smeden, M. van; McLernon, D.J.; Timmerman, D.; Steyerberg, E.W.; Calster, B. van; Topic Grp Evaluating Diagnosti 2019
