Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a...

Projected estimators for robust semi-supervised classification

For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure proposed in this work does not rely on...

Nuclear discrepancy for single-shot batch active learning

Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Instead of selecting randomly what data to annotate, active learning strategies aim to select data so as to get a good predictive model with as little labeled samples as possible. Single-shot batch active learners select all samples to be labeled in a...

A brief prehistory of double descent

Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data

Background: Both patients and physicians may choose to delay initiation of dopamine replacement therapy in Parkinson's disease (PD) for various reasons. We used observational data to estimate the effect of earlier treatment in PD. Observational data offer a valuable source of evidence, complementary to controlled trials. Method: We studied...

Complexe neurologische aandoeningen in de langdurige zorg: Een verkenning van aantallen, patiëntkenmerken en indicaties

Veel patiënten met een complexe neurologische aandoening, zoals de ziekte van Parkinson, multiple sclerose of restverschijnselen van niet-aangeboren hersenletsel, doen vroeg of laat in hun ziekteproces een beroep op de langdurige zorg. Dat deze mensen een andere zorgbehoefte hebben dan de algemene verpleeghuispopulatie is bekend, maar van hun...

Demystifying machine learning for mortality prediction

Effect of genetic variation in CYP450 on Gonadal impairment in a European cohort of female childhood cancer survivors, based on a candidate gene approach: Results from the pancareLIFE study

Background: Female childhood cancer survivors (CCSs) carry a risk of therapy-related gonadal dysfunction. Alkylating agents (AA) are well-established risk factors, yet inter-individual variability in ovarian function is observed. Polymorphisms in CYP450 enzymes may explain this variability in AA-induced ovarian damage. We aimed to evaluate...

ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility

We present ReproducedPapers.org : an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest...

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients...

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study

Background: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model...

The association of comorbidity with Parkinson's disease-related hospitalizations

Introduction: Unplanned hospital admissions associated with Parkinson's disease could be partly attributable to comorbidities. Methods: We studied nationwide claims databases and registries. Persons with newly diagnosed Parkinson's disease were identified based on the first Parkinson's disease-related reimbursement claim by a medical...

Density of Patient-Sharing Networks: Impact on the Value of Parkinson Care

Background: Optimal care for Parkinson’s disease (PD) requires coordination and collaboration between providers within a complex care network. Individual patients have personalised networks of their own providers, creating a unique informal network of providers who treat (‘share’) the same patient. These ‘patient-sharing networks’ differ in...

The effect of cardiovascular risk on disease progression in de novo Parkinson's disease patients: An observational analysis

Background: Currently available treatment options for Parkinson's disease are symptomatic and do not alter the course of the disease. Recent studies have raised the possibility that cardiovascular risk management may slow the progression of the disease. Objectives: We estimated the effect of baseline cardiovascular risk factors on the...

Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide...

The future of artificial intelligence in intensive care: moving from predictive to actionable AI

Artificial intelligence (AI) research in the intensive care unit (ICU) mainly focuses on developing models (from linear regression to deep learning) to predict out-<br/>comes, such as mortality or sepsis [1, 2]. However, there is another important aspect of AI that is typically not framed as AI (although it may be more worthy of the name), which...

Also for k-means: more data does not imply better performance

Arguably, a desirable feature of a learner is that its performance gets better with an increasing amount of training data, at least in expectation. This issue has received renewed attention in recent years and some curious and surprising findings have been reported on. In essence, these results show that more data does actually not...

Explaining Two Strange Learning Curves

Learning curves illustrate how generalization performance of a learner evolves with more training data. While this is a useful tool to characterize learners, not all learning curve behavior is well understood. For instance, it is sometimes assumed that the more training data provided, the better the learner performs. However, counter-examples...