Implicitly Constrained Semi-Supervised Linear Discriminant Analysis

Semi-supervised learning is an important and active topic of research in pattern recognition. For classification using linear discriminant analysis specifically, several semi-supervised variants have been proposed. Using any one of these methods is not guaranteed to outperform the supervised classifier which does not take the additional unlabeled data into account. In this work we compare traditional Expectation Maximization type approaches for semi-supervised linear discriminant analysis with approaches based on intrinsic constraints and propose a new principled approach for semi-supervised linear discriminant analysis, using so-called implicit constraints. We explore the relationships between these methods and consider the question if and in what sense we can expect improvement in performance over the supervised procedure. The constraint based approaches are more robust to misspecification of the model, and may outperform alternatives that make more assumptions on the data, in terms of the log-likelihood of unseen objects.Comment: 6 pages, 3 figures and 3 tables. International Conference on Pattern Recognition (ICPR) 2014, Stockholm, Swede

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. Using a projection of the supervised estimate onto a set of constraints imposed by the unlabeled data, we find we can safely improve over the supervised solution in terms of this quadratic loss. Unlike other approaches to semi-supervised learning, the procedure does not rely on assumptions that are not intrinsic to the classifier at hand. It is theoretically demonstrated that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over the supervised solution. The characteristics of our approach are explicated using benchmark datasets to further understand the similarities and differences between the quadratic loss criterion used in the theoretical results and the classification accuracy often considered in practice.Comment: 13 pages, 2 figures, 1 tabl

Robust semi-supervised learning: projections, limits & constraints

In many domains of science and society, the amount of data being gathered is increasing rapidly. To estimate input-output relationships that are often of interest, supervised learning techniques rely on a specific type of data: labeled examples for which we know both the input and an outcome. The problem of semi-supervised learning is how to use, increasingly abundantly available, unlabeled examples, with unknown outcomes, to improve supervised learning methods. This thesis is concerned with the question if and how these improvements are possible in a "robust", or safe, way: can we guarantee these methods do not lead to worse performance than the supervised solution?We show that for some supervised classifiers, most notably, the least squares classifier, semi-supervised adaptations can be constructed where this non-degradation in performance can indeed be guaranteed, in terms of the surrogate loss used by the classifier. Since these guarantees are given in terms of the surrogate loss, we explore why this is a useful criterion to evaluate performance. We then prove that semi-supervised versions with strict non-degradation guarantees are not possible for a large class of commonly used supervised classifiers. Other aspects covered in the thesis include optimistic learning, the peaking phenomenon and reproducibility.COMMIT - Project P23LUMC / Geneeskunde Repositoriu

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 outcomes, 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 is the prediction of patient outcomes or events that would result from different actions, known as causal inference [3, 4]. This aspect of AI is crucial for decision-making in the ICU. To emphasize the importance of causal inference, we propose to refer to any data-driven model used for causal inference tasks as ‘actionable AI’, as opposed to ‘predictive AI’, and discuss how these models could provide meaningful decision support in the ICU

Risk Factors for Atrial Fibrillation

Atrial fibrillation is a common cardiac arrhythmia that is characterized by rapid disorganized atrial electrical activity resulting in absence of atrial contractions. It is diagnosed on the basis of typical findings on an electrocardiogram (ECG). The characteristic ECG findings are absence of P-waves, and an irregular heart rate. Symptoms of atrial fibrillation include palpitations, dyspnea, reduced exercise capacity, chest pain and dizziness, but it often goes without symptoms. Although atrial fibrillation is often asymptomatic it has serious consequences for the health of affected individuals and is a substantial burden for the health care system. Atrial fibrillation is associated with a higher risk of several serious complications. It is associated with a three to five fold higher risk of stroke. Furthermore, it is associated with a higher risk of dementia, heart failure, and it is associated with increased mortality independent of age sex and other cardiovascular risk factors. Also, it is associated with lower quality of life, even patients without symptoms have a lower perceived general health and gobal life satisfaction than healthy subjects. The prevalence and incidence of atrial fibrillation increase with age. It is estimated that the lifetime risk for development of atrial fibrillation is one in every four adults over 40 years of age. As Western populations are projected to age in the coming decades it is likely that there will be an increase in the number of affected individuals with several types of chronic disease. Several studies projected that the future number of adults with atrial fibrillation in the United States will have doubled by the year 2050.13-15 Not much is known about the potential rise in the number of individuals with atrial fibrillation in the Netherlands and in the European Union but since these populations are projected to age, an increase in the number of patients can be expected