Maarten van der Heijden

An autonomous mobile system for the management of COPD

INTRODUCTION Managing chronic disease through automated systems has the potential to both benefit the patient and reduce health-care costs. We have developed and evaluated a disease management system for patients with chronic obstructive pulmonary disease (COPD). Its aim is to predict and detect exacerbations and, through this, help patients self-manage their disease to prevent hospitalisation. MATERIALS The carefully crafted intelligent system consists of a mobile device that is able to collect case-specific, subjective and objective, physiological data, and to alert the patient by a patient-specific interpretation of the data by means of probabilistic reasoning. Collected data are also sent to a central server for inspection by health-care professionals. METHODS We evaluated the probabilistic model using cross-validation and ROC analyses on data from an earlier study and by an independent data set. Furthermore a pilot with actual COPD patients has been conducted to test technical feasibility and to obtain user feedback. RESULTS Model evaluation results show that we can reliably detect exacerbations. Pilot study results suggest that an intervention based on this system could be successful.

Learning Bayesian networks for clinical time series analysis

INTRODUCTION Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status. METHODS Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set. RESULTS The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.

Managing COPD exacerbations with telemedicine

Managing chronic disease through automated systems has the potential to both benefit the patient and reduce health-care costs. We are developing and evaluating a monitoring system for patients with chronic obstructive pulmonary disease which aims to detect exacerbations and thus help patients manage their disease and prevent hospitalisation. We have carefully drafted a system design consisting of an intelligent device that is able to alert the patient, collect casespecific, subjective and objective, physiological data, offer a patient-specific interpretation of the collected data by means of probabilistic reasoning, and send data to a central server for inspection by health-care professionals. A first pilot with actual COPD patients suggests that an intervention based on this system could be successful.

Annotation of URLs: more than the sum of parts

Recently a number of studies have demonstrated that search engine logfiles are an important resource to determine the relevance relation between URLs and query terms. We hypothesized that the queries associated with a URL could also be presented as useful URL metadata in a search engine result list, e.g. for helping to determine the semantic category of a URL. We evaluated this hypothesis by a classification experiment based on the DMOZ dataset. Our method can also annotate URLs that have no associated queries.

Describing disease processes using a probabilistic logic of qualitative time

BACKGROUND Clinical knowledge about progress of diseases is characterised by temporal information as well as uncertainty. However, precise timing information is often unavailable in medicine. In previous research this problem has been tackled using Allens qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty. OBJECTIVES It is investigated whether and how Allens temporal algebra can be extended to handle uncertainty to better fit available knowledge and data of disease processes. METHODS To bridge the gap between probability theory and qualitative time reasoning, methods from probabilistic logic are explored. The relation between the probabilistic logic representation and dynamic Bayesian networks is analysed. By studying a typical, and clinically relevant problem, the detection of exacerbations of chronic obstructive pulmonary disease (COPD), it is determined whether the developed probabilistic logic of qualitative time is medically useful. RESULTS The probabilistic logic extension of Allens temporal algebra, called Qualitative Time CP-logic provides a tool to model disease processes at a natural level of abstraction and is sufficiently powerful to reason with imprecise, uncertain knowledge. The representation of the COPD disease process gives evidence that the framework can be applied functionally to a clinical problem. CONCLUSION The combination of qualitative time and probabilistic logic offers a useful framework for modelling knowledge and data to describe disease processes in clinical medicine.

Using query logs and click data to create improved document descriptions

Logfiles of search engines are a promising resource for data mining, since they provide raw data associated to users and web documents. In this paper we focus on the latter aspect and explore how the information in logfiles could be used to improve document descriptions. A pilot experiment demonstrated that document descriptors extracted from the queries that are associated with documents by clicks provide useful semantic information about documents in addition to document descriptors extracted from the full text of the web pages.

Hybrid time Bayesian networks

Capturing heterogeneous dynamic systems in a probabilistic model is a challenging problem. A single time granularity, such as employed by dynamic Bayesian networks, provides insufficient flexibility to capture the dynamics of many real-world processes. The alternative is to assume that time is continuous, giving rise to continuous time Bayesian networks. Here the problem is that the level of temporal detail is too precise to match available probabilistic knowledge. In this paper, we present a novel class of models, called hybrid time Bayesian networks, which combine discrete-time and continuous-time Bayesian networks. The new formalism allows us to more naturally model dynamic systems with regular and irregularly changing variables. We also present a mechanism to construct discrete-time versions of hybrid models and an EM-based algorithm to learn the parameters of the resulting BNs. Its usefulness is illustrated by means of a real-world medical problem. Hybrid time Bayesian networks are defined.This new class of models allows reasoning with random variables that evolve regularly or irregularly.A discrete-time characterization of these new models is given.As an application of hybrid time Bayesian networks a medical example is modeled.

Mining Hierarchical Pathology Data Using Inductive Logic Programming

Considerable amounts of data are continuously generated by pathologists in the form of pathology reports. To date, there has been relatively little work exploring how to apply machine learning and data mining techniques to these data in order to extract novel clinical relationships. From a learning perspective, these pathology data possess a number of challenging properties, in particular, the temporal and hierarchical structure that is present within the data. In this paper, we propose a methodology based on inductive logic programming to extract novel associations from pathology excerpts. We discuss the challenges posed by analyzing these data and discuss how we address them. As a case study, we apply our methodology to Dutch pathology data for discovering possible causes of two rare diseases: cholangitis and breast angiosarcomas.

Intelligent Disease Self-Management with Mobile Technology

Cost-effective mobile healthcare must consider not only technological performance but also the division of responsibilities between the patient and care provider, the context of the patients condition, and ways to implement patient decision support and tailored interaction.

Hybrid Time Bayesian Networks

Capturing heterogeneous dynamic systems in a probabilistic model is a challenging problem. A single time granularity, such as employed by dynamic Bayesian networks, provides insufficient flexibility to capture the dynamics of many real-world processes. The alternative is to assume that time is continuous, giving rise to continuous time Bayesian networks. Here the problem is that the level of temporal detail is too precise to match available probabilistic knowledge. In this paper, we present a novel class of models, called hybrid time Bayesian networks, which combine discrete-time and continuous-time Bayesian networks. The new formalism allows us to more naturally model dynamic systems with regular and irregularly changing variables. Its usefulness is illustrated by means of a real-world medical problem.