A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction

By Tyler H. McCormick, Cynthia Rudin, David Madigan. 2011

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Citations in the corpus

Learning Fair Rule Lists (2019, background): “are inherently interpretable [4, 9, 43, 57]. For instance, when they have small or of reasonable size [44], ∗Principal contributor. the following models can be considered as interpretable: rules sets [18, 43, 46, 54], rule lists [3, 59, 61], decision trees [9, 51] and scoring systems [39, 57, 64]. F”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, methodology): “The decision tree methods are representatives of the class of greedy and heuristic methods (e.g., [10,11,13,16,17,26–28, 31, 37, 38 , 51 ] ) that”
Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models (2018, background): “of research in interpretable machine learning in recent years. „e •rst is developing decision models that are interpretable. Previous work in this category include rule-based models (Rule Sets, e.g., [17, 22, 28, 34], Rule Lists [3, 36]), scoring systems [14, 32, 37], case based models [1, 4, 13], a”
Integer optimization methods for machine learning (2012, background): “, 2010], and ranking medical symptoms for the purpose of future symptom prediction [McCormick et al., 2011].”
Comparing different supervised machine learning algorithms for disease prediction (2019, methodology): “These electronic data are being utilised in a wide range of healthcare research areas such as the analysis of healthcare utilisation [10], measuring performance of a hospital care network [11], exploring patterns and cost of care [12], developing disease risk prediction model [13, 14], chronic disea”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “…classification and rule learning methods (e.g., Ma et al., 1998; Li et al., 2001; Yin and Han, 2003; Chen et al., 2006; Cheng et al., 2007; McCormick et al., 2012; Rudin et al., 2013; Dong et al., 1999; Michalski, 1969; Clark and Niblett, 1989; Frank and Witten, 1998) mine for frequent…”