Peter J. Lucas

Peter J. Lucas
University of Twente | UT · Department of Computer Science

MD, PhD
Research and supervising students.

About

312
Publications
79,534
Reads
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4,065
Citations
Introduction
Peter J. Lucas currently works at the Datascience Department, EEMCS Faculty of the University of Twente. He has more than 35 years of experience as an AI researcher in areas such as intelligent systems, computer-based reasoning, decision support systems, model-based reasoning and diagnosis, Bayesian networks, machine learning, eHealth. At the moment most of my research is on probabilistic graphical models, statistical machine learning, and clinical statistical model building.
Additional affiliations
March 2010 - present
Leiden University
Position
  • Professor (Full)
January 2008 - January 2016
Radboud University
Position
  • Head of Department
May 2002 - present
Radboud University
Description
  • http://www.mbsd.cs.ru.nl/

Publications

Publications (312)
Preprint
Purpose Preoperative risk stratification of newly diagnosed endometrial carcinoma (EC) patients has been hindered by only moderate prediction performance for many years. Recently ENDORISK, a Bayesian network model, showed high predictive performance. It was the aim of this study to validate ENDORISK by applying the model to a population-based case...
Article
The development and validation of Clinical Decision Support Models (CDSM) based on Bayesian networks (BN) is commonly done in a collaborative work between medical researchers providing the domain expertise and computer scientists developing the decision support model. Although modern tools provide facilities for data-driven model generation, domain...
Article
Full-text available
The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consis...
Conference Paper
Full-text available
Introduction/Background* Preoperative risk stratification of newly diagnosed endometrial carcinoma (EC) patients has been hindered by only moderate prediction performance for many years. Recently ENDORISK, a Bayesian network (BN) model using easily accessible biomarkers, showed increased predictive performance when compared to current guidelines. I...
Article
Full-text available
Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The analysis of many real-world use cases, that in principle can be modeled by BNs, suffer however from the computational complexity of inference. Inference methods based on Weighted Model Counting (WMC) reduces the cost of inference by exploiting patterns exhibite...
Chapter
The discovery of subsets of data that are characterized by models that differ significantly from the entire dataset, is the goal of exceptional model mining. With the increasing availability of temporal data, this task has clear relevance in discovering deviating temporal subprocesses that can bring insight into industrial processes, medical treatm...
Preprint
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Concerns about the practicality and effectiveness of using Contact Tracing Apps (CTA) to reduce the spread of COVID19 have been well documented and, in the UK, led to the abandonment of the NHS CTA shortly after its release in May 2020. One of the key non-technical obstacles to widespread adoption of CTA has been concerns about privacy. We present...
Article
Full-text available
Background Bayesian networks (BNs) are machine-learning–based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cance...
Preprint
Full-text available
Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. The introduction of a new CTA alone will n...
Preprint
Full-text available
Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. However, very few groups have taken a holi...
Conference Paper
Full-text available
Introduction/Background The presence of pelvic and/or para-aortic lymph node metastasis (LNM) is one of the most important prognostic factors for poor outcome in endometrial carcinoma (EC). Current risk stratification for lymphadenectomy is mainly based on preoperative tumor grade, results in over- and undertreated of approximately 25% and 15% of t...
Article
Full-text available
Background: Many patients with chronic obstructive pulmonary disease (COPD) suffer from exacerbations, a worsening of their respiratory symptoms that warrants medical treatment. Exacerbations are often poorly recognized or managed by patients, leading to increased disease burden and health care costs. Objective: This study aimed to examine the e...
Article
Background: Recently, mobile devices, such as smartphones, have been introduced into healthcare research to substitute paper diaries as data-collection tools in the home environment. Such devices support collecting patient data at different time points over a long period, resulting in clinical time-series data with high temporal complexity, such a...
Chapter
Unsupervised learning is often used to obtain insight into the underlying structure of medical data. In this paper, we show that unsupervised methods, in particular hidden Markov models, can go beyond this by guiding the generation of clinical outcome measures and hypotheses, which play a crucial role in medical research. The usage of the data-driv...
Chapter
The current availability of large volumes of health care data makes it a promising data source to new views on disease interaction. Most of the times, patients have multiple diseases instead of a single one (also known as multimorbidity), but the small size of most clinical research data makes it hard to impossible to investigate this issue. In thi...
Article
Full-text available
Background: To support patients with COPD in their self-management of symptom worsening, we developed Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS), an innovative software application that provides automated treatment advice without the interference of a health care professional. Exacerbation detection is based on 12 symp...
Article
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In this paper we investigate the task of parameter learning of Bayesian networks and, in particular, we deal with the prior uncertainty of learning using a Bayesian framework. Parameter learning is explored in the context of Bayesian inference and we subsequently introduce Bayes, con- strained Bayes and robust Bayes parameter learning methods. Baye...
Article
In many problems involving multivariate time series, hidden Markov models (HMMs) are often employed to model complex behavior over time. HMMs can, however, require large number of states, what can lead to poor problem insight and model overfitting, especially when limited data is available. In this paper, we further investigate the family of asymme...
Article
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Learning Bayesian network structures from data is known to be hard, mainly because the number of candidate graphs is super-exponential in the number of variables. Furthermore, using observational data alone, the true causal graph is not discernible from other graphs that model the same set of conditional independencies. In this paper, it is investi...
Article
Recent work on weighted model counting has been very successfully applied to the problem of probabilistic inference in Bayesian networks. The probability distribution is encoded into a Boolean normal form and compiled to a target language, in order to represent local structure expressed among conditional probabilities more efficiently. We show that...
Article
For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a conseque...
Article
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 net...
Conference Paper
Full-text available
Probabilistic logics, especially those based on logic programming (LP), are gaining popularity as modelling and reasoning tools, since they combine the power of logic to represent knowledge with the ability of probability theory to deal with uncertainty. In this paper, we propose a hybrid extension for probabilistic logic programming, which allows...
Article
Abstract Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. We focus on a class of probabilistic logic based on Sato's distribution semantics, which extends logic programming with probabili...
Conference Paper
Full-text available
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...
Article
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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 patient’s condition, and ways to implement patient decision support and tailored interaction. In this paper we discuss four foundational aspects of m-health for disease...
Chapter
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We are currently confronted with a trend of increased pressure on health care, with associated increasing financial costs, due to an aging society and the expected increase in the prevalence of disability and chronic disease. Finding measures for cost reduction, without sacrificing quality of care, is a significant healthcare challenge. Computing t...
Chapter
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Healthcare and medicine are, and have always been, very knowledge-intensive fields. Healthcare professionals use knowledge of the structure (molecular biology, cell biology, histology, gross anatomy) and functioning of the human body as well as knowledge of methods and means, some of them described by clinical guidelines, to diagnose and manage dis...
Chapter
The early medical diagnostic applications often had the form of rule-based expert systems and started to appear around the mid 1970s. Soon, it became apparent that developing reliable diagnostic systems required an understanding of the principles underlying diagnosis, which at the time were poorly understood.
Chapter
Biology and medicine are very rich knowledge domains in which already at an early stage in their scientific development it was realised that without a proper way to organise this knowledge they would inevitably turn into chaos. Early examples of organisation attempts are for example “De Rerum Natura (On the Nature of Things)” by Titus Lucretius Car...
Article
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In the context of home-based healthcare monitoring systems, it is desirable that the results obtained from biochemical tests - tests of various body fluids such as blood and urine - are objective and automatically generated to reduce the number of man-made errors. The authors present the StripTest reader - an innovative smartphone-based interpreter...
Article
Approaches for extending logic to deal with uncertainty immanent to many real-world problems are often on the one side purely qualitative, such as modal logics, or on the other side quantitative, such as probabilistic logics. Research on combinations of qualitative and quantitative extensions to logic which put qualitative constraints on probabilit...
Article
Full-text available
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 th...
Conference Paper
Full-text available
Performing safety and security tasks requires the continuous gathering and interpretation of information about objects to detect and predict events of interest. Especially, reasoning about objects' identities and intentions is crucial, but requires making use of heterogeneous information inherent with uncertainty. This makes such tasks very challen...
Conference Paper
Full-text available
MoSHCA is a mHealth project designed to improve patient-doctor interaction and to promote the self-management of chronic diseases by the patients themselves. The number of people with a chronic disease is dramatically increasing worldwide. This is becoming a major obstacle for economic stability and growth and the sustainability of national health...
Article
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Although the course of single diseases can be studied using traditional epidemiologic techniques, these methods cannot capture the complex joint evolutionary course of multiple disorders. In this study, multilevel temporal Bayesian networks were adopted to study the course of multimorbidity in the expectation that this would yield new clinical insi...
Conference Paper
Multimorbidity, i.e., the presence of multiple diseases within one person, is a significant health-care problem for western societies: diagnosis, prognosis and treatment in the presence of of multiple diseases can be complex due to the various interactions between diseases. To better understand the co-occurrence of diseases, we propose Bayesian net...
Article
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...
Article
Objective: Large health care datasets normally have a hierarchical structure, in terms of levels, as the data have been obtained from different practices, hospitals, or regions. Multilevel regression is the technique commonly used to deal with such multilevel ...
Chapter
This chapter discusses design considerations for industrial systems and processes when embedded systems allow to intelligently influence the system in real-time. It is shown that in such embedded systems the capability to adapt themselves to changing environments and/or to different operating conditions has to be exploited. If properly done, almost...
Chapter
Any conclusion about a system’s hidden behaviour based on the observation of findings emanating from this behaviour is inherently uncertain. If one wishes to take action only when these conclusions give rise to effects within guaranteed bounds of uncertainty, this uncertainty needs to be represented explicitly. Bayesian networks offer a probabilist...
Conference Paper
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. In this paper, we propose a new probabilistic constraint logic programming language, which combines constraint logic programming with prob...
Article
For many problem domains, such as medicine, chain graphs are more attractive than Bayesian networks as they support representing interactions between variables that have no natural direction. In particular, interactions between variables that result from certain feedback mechanisms can be represented by chain graphs. Using qualitative abstractions...
Chapter
Full-text available
Medical protocols and guidelines can be looked upon as concurrent programs, where the patient’s state dynamically changes over time. Methods based on verification and model-checking developed in the past have been shown to offer insight into the correctness of guidelines and protocols by adopting a logical point of view. However, there is uncertain...
Conference Paper
In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the informatio...
Article
Full-text available
Multimorbidity, i.e., the presence of multiple diseases within one person, is a significant health-care problem for western societies: diagnosis, prognosis and treatment in the presence of of multiple diseases can be complex due to the various interactions between diseases. A literature review reveals that there is a variety of definitions that des...
Conference Paper
Full-text available
For modelling diseases in medicine, chain graphs are more attractive than directed graphs, i.e., Bayesian networks, as they support representing interactions between diseases that have no natural direction. In particular, representation by chain graphs is preferred over Bayesian networks as they have the ability to capture equilibrium models. Using...
Conference Paper
Applying artificial intelligence techniques to management of chronic diseases - smart monitoring - has great potential to improve chronic disease care. Probabilistic models offer powerful methods for automatic data interpretation, and thus play a potentially large role in mobile, personalised care. In particular in the context of disease monitoring...
Conference Paper
Full-text available
In this paper we present innovative research for the automatic interpretation of biochemical test strip color by a smartphone using image processing techniques. Urinalysis is the current application for these techniques. Our mobile application captures images of the color pads on strips using the camera phone, then analyzes automatically the images...
Conference Paper
Temporal indeterminacy, the lack of specific knowledge about the timing of events, occurs often in temporal reasoning in practical applications and is connected to the concept of time granularity. Although logical properties of granularities have been described by several researchers in the literature, the implications of temporal indeterminacy and...
Conference Paper
Full-text available
Censoring is a typical problem of data gathering and recording. Specialized techniques are needed to deal with censored (regression) data. Gaussian processes are Bayesian nonparametric models that provide state-of-the-art performance in regression tasks. In this paper we propose an extension of Gaussian process regression models to data in which so...
Conference Paper
Full-text available
We present symfer, a software framework for probabilistic inference algorithms. Each inference algorithm (like variable elimination, junction tree propagation, recursive conditioning) is represented as a symbolic manipulation of factor algebra expressions. In combination with the readability and terseness of Python code, this uniform representation...
Chapter
Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a no...