A model of inexact reasoning in medicine

By Edward H. Shortliffe, Bruce G. Buchanan. 1990

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Citations in the corpus

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence (2018, background): “, medical [31] and feedback for teaching programs [19, 32, 9]).”
Grounding Visual Explanations (2018, methodology): “Early textual explanation models are applied to medical images [25] and developed as a feedback for teaching programs [12,27,3].”
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (2016, background): “htly over MCB [15], the VQA 2016 challenge winner, and is more efﬁcient to train and test. 2. Related Work Explanations. Early textual explanation models span a variety of applications (e.g., medical [32] and feedback for teaching programs [19, 33, 10]) and are generally template based. More recentl”
Knowledge-Based Artificial Neural Networks (1994, background): “Mycin-like certainty factors [52] could be integrated into the rules-to-network translator [13, 21].”
Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea (2010, methodology): “The CF was originally defined as a function of probability by Shortliffe and Buchanan (1975) and modified by Heckerman (1986) as follows:”
Causability and explainability of artificial intelligence in medicine (2019, methodology): “MYCIN was never used in clinical routine, maybe because of its stand-alone character and the high effort in maintaining its knowledge base.”
Toward Normative Expert Systems: Part I The Pathfinder Project (1992, methodology): “A well-known ad hoc method for managing uncertainty is the certainty factor (CF) model [38].”
Selecting the right objective measure for association analysis (2004, methodology): “Other variants of this measure include Piatetsky-Shapiro’s rule-interest [5], certainty factor [27], collective strength [28] and added value [29].”
Contemporary Approaches to Artificial General Intelligence (2007, methodology): “Rather than using probability theory, Wang uses his own form of uncertain logic – an approach that has been tried before, with fuzzy logic, certainty theory (see, for example, [ 50 ]) and so forth, but has never before been tried with such explicit AGI ambitions.”
Machine learning for detection and diagnosis of disease. (2006, result): “Such an approach mapped well to many problems in medical image and signal analysis and was in contrast to medical expert systems such as Mycin (4) and INTERNIST (5), which, in fact, were very difficult and time consuming to construct and were based on a set of rules and prior knowledge.”
Interpretable R-CNN (2017, background): “These early rule-based systems can generate theorem proving types of interpretable justifications [64, 68, 32, 41, 71], but were much less effective, too expensive to build and too brittle against the complexities of the real world.”
Fuzzy association rules: general model and applications (2003, background): “It employs certainty factors [15] and the new concept of very strong rule.”
Generating Visual Explanations (2016, background): “Methods such as [13] require expert-level explanations and decision processes.”