Describing disease processes using a probabilistic logic of qualitative time

• Published in: Artificial intelligence in medicine Vol. 59; no. 3; pp. 143 - 155
• Main Authors: van der Heijden, Maarten, Lucas, Peter J.F
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2013; Elsevier
• Subjects: Applied sciences; Bayes Theorem; Biological and medical sciences; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease, asthma; Computer science; control theory; systems; Computerized management (medical records, files, hospital management); Computerized, statistical medical data processing and models in biomedicine; Disease Progression; Drug Resistance, Viral - genetics; Dynamic Bayesian networks; Exact sciences and technology; HIV Infections - drug therapy; Humans; Information retrieval. Graph; Internal Medicine; Knowledge representation; Logical, boolean and switching functions; Medical sciences; Other; Pneumology; Probabilistic logic; Probability; Pulmonary Disease, Chronic Obstructive - pathology; Temporal reasoning; Theoretical computing; Uncertainty; Probabilistic logic; Temporal reasoning; Chronic obstructive pulmonary disease; Dynamic Bayesian networks; Knowledge representation; Uncertainty; Stochastic logic; Probability theory; Qualitative reasoning; Logical programming; Abstraction; Modeling; Timed system; Temporal logic; Bridges; Time dependence; Uncertain system; Bayes network; Medical application
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2013.09.003

Abstract 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 Allen's qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty. Objectives It is investigated whether and how Allen's 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 Allen's 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.