597 research outputs found
An Introduction to Knowledge Representation and Reasoning in Healthcare
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Conformance Verification of Clinical Guidelines in Presence of Computerized and Human-Enhanced Processes
No full textClinical Guidelines (CGs) capture medical evidence and describe standardized high quality health processes. Their adoption increases the quality of the service offered by health departments, with direct advantage for treated patients. However, their application in real cases is often tempered by a number of factors like the context, the specific case itself, administrative processes, and the involved personnel. In this chapter we analyse the issues related to the problem of representing CGs in a formal way, and to reason about the differences between what is prescribed by CGs, and what is observed during their application/execution. Our approach is based on a general, abstract framework that should be flexible enough to cope with the raised issues. Possible technical solutions are also presented and their limits discussed
Foundations of Biomedical Knowledge Representation : Methods and Applications
No full textItem does not contain fulltextXIV, 362 p
Predicting adverse drug events from electronic medical records
No full textLearning from electronic medical records (EMR) poses many challenges from a knowledge representation point of view. This chapter focuses on how to cope with two specific challenges: the relational nature of EMRs and the uncertain
dependence between a patient's past and future health status. We discuss three different approaches for allowing standard propositional learners to incorporate relational information. We evaluate these approaches on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication.sponsorship: JD is partially supported by the Research Fund K.U.Leuven (CREA/11/015 and OT/11/051), EU FP7 Marie Curie Career Integration Grant (#294068) and FWO-Vlaanderen (G.0356.12). VSC is funded by ERDF through Programme COMPETE and by the Portuguese Government through FCT Foundation for Science and Technology projects LEAP (PTDC/EIA-CCO/112158/2009) and ADE (PTDC/EIA-EIA/121686/2010). MC, PP, EB and DP gratefully acknowledge the support of NIGMS grant R01GM097618-01.status: Publishe
Modeling dynamic processes with memory by higher order temporal models
No full textMost practical uses of Dynamic Bayesian Networks (DBNs) involve temporal influences of the first order, i.e., influences between neighboring time steps. This choice is a convenient approximation influenced by the existence of efficient algorithms for first order models and limitations of available tools. In this paper we focus on the question whether constructing higher time-order models is worth the effort when the underlying system's memory goes beyond the current state. We present the results of an experiment in which we successively introduce higher order DBNs models monitoring woman’s monthly cycle and measure the accuracy of these models in estimating the fertile period around the day of ovulation. We show that higher order models are more accurate than first order models. However, we have also observed over-fitting and a resulting decrease in accuracy when the time order chosen is too high
Treatment of disease: The role of knowledge representation for treatment selection
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Prediction and prognosis of health and disease
No full textMedical prognosis is defined as the prediction of the probable course and outcome of a disease. This prediction should facilitate understanding patterns of disease progression as well as its management
Bayesian model-based diagnosis
No full textAbstractModel-based diagnosis concerns using a model of the structure and behaviour of a system or device in order to establish why the system or device is malfunctioning. Traditionally, little attention has been given to the problem of dealing with uncertainty in model-based diagnosis. Given the fact that determining a diagnosis for a problem almost always involves uncertainty, this situation is not entirely satisfactory. This paper builds upon and extends previous work in model-based diagnosis by supplementing the well-known model-based framework with mathematically sound ways for dealing with uncertainty. The resulting method integrates logical reasoning with probabilistic reasoning, and reasoning about the structure and behaviour of a system with reasoning by taking stochastic independence assumptions into account
Foundations of Biomedical Knowledge Representation:Methods and Applications
No full textMedicine and health care are currently faced with a significant rise in their complexity. This is partly due to the progress made during the past three decades in the fundamental biological understanding of the causes of health and disease at the molecular, (sub)cellular, and organ level. Since the end of the 1970s, when knowledge representation and reasoning in the biomedical field became a separate area of research, huge progress has been made in the development of methods and tools that are finally able to impact on the way medicine is being practiced.Even though there are huge differences in the techniques and methods used by biomedical researchers, there is now an increasing tendency to share research results in terms of formal knowledge representation methods, such as ontologies, statistical models, network models, and mathematical models. As there is an urgent need for health-care professionals to make better decisions, computer-based support using this knowledge is now becoming increasingly important. It may also be the only way to integrate research results from the different parts of the spectrum of biomedical and clinical research.The aim of this book is to shed light on developments in knowledge representation at different levels of biomedical application, ranging from human biology to clinical guidelines, and using different techniques, from probability theory and differential equations to logic. The book starts with two introductory chapters followed by 18 contributions organized in the following topical sections: diagnosis of disease; monitoring of health and disease and conformance; assessment of health and personalization; prediction and prognosis of health and disease; treatment of disease; and recommendations
Foundations of Biomedical Knowledge Representation:Methods and Applications
No full textMedicine and health care are currently faced with a significant rise in their complexity. This is partly due to the progress made during the past three decades in the fundamental biological understanding of the causes of health and disease at the molecular, (sub)cellular, and organ level. Since the end of the 1970s, when knowledge representation and reasoning in the biomedical field became a separate area of research, huge progress has been made in the development of methods and tools that are finally able to impact on the way medicine is being practiced.Even though there are huge differences in the techniques and methods used by biomedical researchers, there is now an increasing tendency to share research results in terms of formal knowledge representation methods, such as ontologies, statistical models, network models, and mathematical models. As there is an urgent need for health-care professionals to make better decisions, computer-based support using this knowledge is now becoming increasingly important. It may also be the only way to integrate research results from the different parts of the spectrum of biomedical and clinical research.The aim of this book is to shed light on developments in knowledge representation at different levels of biomedical application, ranging from human biology to clinical guidelines, and using different techniques, from probability theory and differential equations to logic. The book starts with two introductory chapters followed by 18 contributions organized in the following topical sections: diagnosis of disease; monitoring of health and disease and conformance; assessment of health and personalization; prediction and prognosis of health and disease; treatment of disease; and recommendations
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