Case-based reasoning in the health sciences: What's next?

By I. Bichindaritz, C. Marling. 2006

230 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, background): “CBR has been used in important real-world applications [24, 4], but is fundamentally limited, in that it does not learn the underlying complex structure of data in an unsupervised fashion and may not scale to datasets with high-dimensional feature spaces (as discussed in [29]).”
ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism (2017, methodology): “They have extensively been used in case-based reasoning (CBR) to improve the interpretability of the models [1, 4, 25].”
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases (2017, methodology): “They have extensively been used in case-based reasoning (CBR) to improve the interpretability of the models [34,35,36].”
Examples are not enough, learn to criticize! Criticism for Interpretability (2016, background): “A popular research program along these lines is case-based reasoning (CBR) (Aamodt and Plaza, 1994), which has been successfully applied to real-world problems (Bichindaritz and Marling, 2006).”
Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models (2018, background): “, [17, 22, 28, 34], Rule Lists [3, 36]), scoring systems [14, 32, 37], case based models [1, 4, 13], and etc.”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “CBR has been used in important real-world applications [91, 19], but it does not learn the underlying complex structure of data in an unsupervised fashion and may not scale to datasets with high-dimensional feature spaces (as discussed in [123]).”
iForest: Interpreting Random Forests via Visual Analytics (2019, methodology): “This aligns with the analogical decision making process of humans [29, 40], which has been widely adopted in various real-world applications, such as the medical [8] and the ﬁnancial [33] domains.”