INKBLOT: A neurological diagnostic decision support system integrating causal and anatomical knowledge
Citro G, Banks G, Cooper G. INKBLOT: A neurological diagnostic decision support system integrating causal and anatomical knowledge. Artificial Intelligence in Medicine 10 (1997) 257-267. PMID: 9232188
As an initial step in the diagnostic process, human neurologists often use anatomical localization to constrain the set of diagnostic hypotheses deserving further consideration. We describe an automated system, INKBLOT-l, which uses anatomical localization in much the same way as human neurologists. Given a set of manifestations, INKBLOT-l generates a set of hypothetical localizations relative to a coordinate system of nested cubes and then uses these lsocalization(s) to explain the manifestations. We trace the reasoning mechanism utilized by INKBLOT-l for a particular set of symptoms and show how INKBLOT-l is able to generate novel hypotheses that explain the observed manifestations. In doing this, INKBLOT-l demonstrates capabilities not demonstrated by previously described systems. 0 1997 Elsevier Science B.V.
Keywords: Neurological diagnosis; Anatomical localization; Model-based diagnosis; Decision support