Deep Models for Medical Knowledge Engineering: (v. 1 Medical Artificial Intelligence S.)

Medical expert systems led the way in the first generation of expert systems, so it is not surprising that medical expert systems have taken a leading role in the second generation, i.e. deep, expert systems. The aim of this volume is to give an accurate picture of current research on Deep Model approaches directly applicable to the medical field and to present this picture in the context of recent findings. Being a collection of research papers, it is mainly addressed to Artificial Intelligence in Medicine (AIM) researchers, cognitive scientists and medics interested in AIM work. However the volume could provide useful text material for an advanced course in Medical Knowledge Engineering or Medical Informatics. Specifying what characterizes a shallow system is not difficult, namely a knowledge-base of association between data about the problem and (sub)solutions for the problem. By implication a deep system is one which has something over and above a mere associational knowledge-base. Most researchers agree on this point. Where disagreement begins to surface is with regard to what constitutes this something else, this desirable quality, that a deep system should have over an associational system. Deepness is a simple concept to grasp intuitively but it is not so easy to formalise in the context of computer systems; it is a broad, multi-dimensional concept, and this book aims to present different points of view about what constitutes deepness.

Table of Contents:
Improving Diagnostic Efficiency in KARDIO: Abstractions, Constraint Propagation and Model Compilation (I. Mozetic, B. Pfahringer). Utilizing Detailed Anatomical Knowledge for Hypothesis Formation and Hypothesis Testing in Rheumatological Decision Support (W. Horn). Qualitative Models in Medical Diagnosis (L. Ironi, M. Stefanelli, G. Lazola). Monitoring Diseases with Empirical and Model Generated Histories (E. Coiera). Reduced Constraint Models (S. van Denneheuvel et al.). A Causal-Functional Model for Medical Knowledge Representation (P. Barahona). Knowledge Representation by Extended Linear Models (S. Andreassen). Validation of a Causal Probabilistic Medical Knowledge Base for Diagnostic Reasoning (W.J. Long et al.). Making Deepness Explicit (J. Washbrook, E. Keravnou). Some Causal Models are Deeper than Others (T. Bylander). Second Generation Knowledge Acquisition Methods and their Application to Medicine (N. Lavrac, I. Mozetic). Structure and Significance of Analogical Reasoning (J.A. Campbell, J. Wolstencroft). Cases and the Elucidation of Deep Knowldege (J.A. Campbell, J. Wolstencroft). Reflections on the Nature of Expertise in Medicine (H.G. Schmidt, H.P.A. Boshuizen, G.R. Norman). Machine Depth versus Psychological Depth: a Lack of Equivalence (V.L. Patel et al.). Deep Knowledge in Human Medical Expertise (K.J. Gilhooly, S. Simpson).