Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Inducing decision trees from medical decision processes
KR4HC'10 Proceedings of the ECAI 2010 conference on Knowledge representation for health-care
Automatic generation of clinical algorithms within the state-decision-action model
Expert Systems with Applications: An International Journal
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The practice of medicine is becoming increasingly evidence-based and clinical practice guidelines (CPGs) are necessary for advancing evidence-based medicine (EBM). We hypothesize that machine learning methods can play an important role in learning CPGs automatically from data . Automatically induced CPGs can then be used for further manual refinement and deployment, for automated guideline compliance checking, for better understanding of disease processes, and for improved physician education. We discuss why learning CPGs is a special form of computational causal discovery and why simply predictive (i.e., non-causal) methods may not be appropriate for this task.