Patient condition modeling in remote patient management : hospitalization prediction

In order to maintain and improve the quality of care without exploding costs, healthcare systems are undergoing a paradigm shift from patient care in the hospital to patient care at home. Remote patient management (RPM) systems offer a great potential in reducing hospitalization costs and worsening of symptoms for patients with chronic diseases, e.g., heart failure and diabetes. Different types of data collected by RPM systems provide an opportunity for personalizing information services, and alerting medical personnel about the changing conditions of the patient. In this work we focus on a particular problem of patient modeling that is the hospitalization prediction. We consider the problem definition, our approach to this problem, highlight the results of the experimental study and reflect on their use in decision making

White-box optimization from historical data

Contains fulltext : 122450.pdf (preprint version ) (Open Access)BENELEARN 2013: Proceedings of the 22nd Belgian-Dutch Conference on Machine Learning, Nijmegen, 3 june 201

Efficient Identification of Timed Automata: Theory and practice

This thesis contains a study in a subfield of artificial intelligence, learning theory, machine learning, and statistics, known as system (or language) identification. System identification is concerned with constructing (mathematical) models from observations. Such a model is an intuitive description of a complex system. One of the main nice properties of models is that they can be visualized and inspected in order to provide insight into the different behaviors of a system. In addition, they can be used to perform different calculations, such as making predictions, analyzing properties, diagnosing errors, performing simulations, and many more. Models are therefore extremely useful tools for understanding, interpreting, and modifying different kinds of systems. Unfortunately, it can be very difficult to construct a model by hand. This thesis investigates the difficulty of automatically identifying models from observations. Observations of some process and its environment are given. These observations form sequences of events. Using system identification, we try to discover the logical structure underlying these event sequences. A well-known model of such a logical structure is the deterministic finite state automaton (DFA). A DFA is a language model. Hence, its identification (or inference) problem has been well studied in the grammatical inference field. Knowing this, we want to take an established method to learn a DFA and apply it to our event sequences. However, when observing a system there often is more information than just the sequence of symbols (events): the time at which these symbols occur is also available. A DFA can be used to model this time information implicitly. A disadvantage of such an approach is that it can result in an exponential blowup of both the input data and the resulting size of the model. In this thesis, we propose a different method that uses the time information directly in order to produce a timed model. We use a well-known DFA variant that includes the notion of time, called the timed automaton (TA). TAs are commonly used to model and reason about real-time systems. A TA models the timed information explicitly, i.e., using numbers. Because numbers use a binary representation of time, such an explicit representation can result in exponentially more compact models than an implicit representation. Therefore, also the time, space, and data required to identify TAs can be exponentially smaller than the time, space, and data required to identify DFAs. This efficiency argument is our main reason we are interested in identifying TAs. The work in this thesis makes four major contributions to the state-of-the-art on this topic: 1. It contains a thorough theoretical study of the complexity of identifying TAs from data. 2. It provides an algorithm for identifying a simple TA from labeled data, i.e., from event sequences for which it is known to which type of system behavior they belong. 3. It extends this algorithm to the setting of unlabeled data, i.e., from event sequences with unknown behaviors. 4. It shows how to apply this algorithm to the problem of identifying a real-time monitoring system. These contributions are of importance for anyone who is interested in identifying timed systems. Most importantly, both in our theoretical work and in our experiments we show that identifying a TA by using the time information directly is more efficient than identifying an equivalent DFA. In addition, our techniques can be applied to many interesting problems due to their generality. Examples are gaining insight into a real-time process, recognizing different process behaviors, identifying process models, and analyzing black-box systems.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc

SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting

Sequence clustering in a streaming environment is challenging because it is computationally expensive, and the sequences may evolve over time. K-medoids or Partitioning Around Medoids (PAM) is commonly used to cluster sequences since it supports alignment-based distances, and the k-centers being actual data items helps with cluster interpretability. However, offline k-medoids has no support for concept drift, while also being prohibitively expensive for clustering data streams. We therefore propose SECLEDS, a streaming variant of the k-medoids algorithm with constant memory footprint. SECLEDS has two unique properties: i) it uses multiple medoids per cluster, producing stable highquality clusters, and ii) it handles concept drift using an intuitive Medoid Voting scheme for approximating cluster distances. Unlike existing adaptive algorithms that create new clusters for new concepts, SECLEDS follows a fundamentally different approach, where the clusters themselves evolve with an evolving stream. Using real and synthetic datasets, we empirically demonstrate that SECLEDS produces high-quality clusters regardless of drift, stream size, data dimensionality, and number of clusters. We compare against three popular stream and batch clustering algorithms. The state-of-the-art BanditPAM is used as an offline benchmark. SECLEDS achieves comparable F1 score to BanditPAM while reducing the number of required distance computations by 83.7%. Importantly, SECLEDS outperforms all baselines by 138.7% when the stream contains drift. We also cluster real network traffic, and provide evidence that SECLEDS can support network bandwidths of up to 1.08 Gbps while using the (expensive) dynamic time warping distance.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

Adversarially Robust Decision Tree Relabeling

Decision trees are popular models for their interpretation properties and their success in ensemble models for structured data. However, common decision tree learning algorithms produce models that suffer from adversarial examples. Recent work on robust decision tree learning mitigates this issue by taking adversarial perturbations into account during training. While these methods generate robust shallow trees, their relative quality reduces when training deeper trees due the methods being greedy. In this work we propose robust relabeling, a post-learning procedure that optimally changes the prediction labels of decision tree leaves to maximize adversarial robustness. We show this can be achieved in polynomial time in terms of the number of samples and leaves. Our results on 10 datasets show a significant improvement in adversarial accuracy both for single decision trees and tree ensembles. Decision trees and random forests trained with a state-of-the-art robust learning algorithm also benefited from robust relabeling.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

Learning Decision Trees with Flexible Constraints and Objectives Using Integer Optimization

We encode the problem of learning the optimal decision tree of a given depth as an integer optimization problem. We show experimentally that our method (DTIP) can be used to learn good trees up to depth 5 from data sets of size up to 1000. In addition to being efficient, our new formulation allows for a lot of flexibility. Experiments show that we can use the trees learned from any existing decision tree algorithms as starting solutions and improve the trees using DTIP. Moreover, the proposed formulation allows us to easily create decision trees with different optimization objectives instead of accuracy and error, and constraints can be added explicitly during the tree construction phase. We show how this flexibility can be used to learn discrimination-aware classification trees, to improve learning from imbalanced data, and to learn trees that minimise false positive/negative errors.Accepted author manuscriptCyber Securit

Efficient Training of Robust Decision Trees Against Adversarial Examples

Recently it has been shown that many machine learning models are vulnerable to adversarial examples: perturbed samples that trick the model into misclassifying them. Neural networks have received much attention but decision trees and their ensembles achieve state-of-the-art results on tabular data, motivating research on their robustness. Recently the first methods have been proposed to train decision trees and their ensembles robustly [4, 3, 2, 1] but the state-of-the-art methods are expensive to run.Cyber Securit

Optimal Decision Tree Policies for Markov Decision Processes

Interpretability of reinforcement learning policies is essential for many real-world tasks but learning such interpretable policies is a hard problem. Particularly, rule-based policies such as decision trees and rules lists are difficult to optimize due to their non-differentiability. While existing techniques can learn verifiable decision tree policies, there is no guarantee that the learners generate a policy that performs optimally. In this work, we study the optimization of size-limited decision trees for Markov Decision Processes (MPDs) and propose OMDTs: Optimal MDP Decision Trees. Given a user-defined size limit and MDP formulation, OMDT directly maximizes the expected discounted return for the decision tree using Mixed-Integer Linear Programming. By training optimal tree policies for different MDPs we empirically study the optimality gap for existing imitation learning techniques and find that they perform sub-optimally. We show that this is due to an inherent shortcoming of imitation learning, namely that complex policies cannot be represented using size-limited trees. In such cases, it is better to directly optimize the tree for expected return. While there is generally a trade-off between the performance and interpretability of machine learning models, we find that on small MDPs, depth 3 OMDTs often perform close to optimally.Cyber Securit

Estimating prediction certainty in decision trees

Contains fulltext : 122408.pdf (preprint version ) (Closed access