A Comparison of Classification Algorithms to Automatically Identify Chest X-Ray Reports That Support Pneumonia

By Wendy W. Chapman, Marcelo Fiszman, B. Chapman, P. Haug. 2001

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Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[98] applied a Bayesian network inference model to predict pancreatic cancer using”
Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy (2013, background): “Over the last 20 years, various combinations of approaches, including natural language processing [28–31], expert rules [32,33], Bayesian [32,34] or neural networks [35] and machine-learning [33], have held their own compared to physicians for their ability to find pneumonia-related concepts in repo”
The use of receiver operating characteristic curves in biomedical informatics (2005, background): “Recent examples of continuous classifiers evaluated using ROC curves include statistical models for retrieving highly useful clinical articles [8], radiology reports that suggest pneumonia [9] and radiology reports suggesting anthrax [10].”
Assessing Pneumonia Identification from Time-Ordered Narrative Reports (2012, background): “(5) The patient was admitted to the Cardiac Medicine Service and treated for presumed diastolic and”
The max-min hill-climbing Bayesian network structure learning algorithm (2006, methodology): “Keywords Bayesian networks · Graphical models · Structure learning”