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We developed a machine learning classifier to predict the diagnosis of PTA based on patient symptoms. We retrospectively collected clinical data and symptomatology from 916 patients who underwent attempted needle aspiration for PTA. Machine learning classifiers were trained on a subset of the data to predict the presence or absence of purulence on attempted aspiration. The performance of the model was evaluated on a holdout set. The accuracy of the top\u2010performing algorithm, the artificial neural network, was 72.3%. Artificial neural networks can use patient symptoms to exceed human ability to predict PTA in patients with clinical suspicion for PTA. Similar models can assist medical decision making for clinicians who have suspicion of PTA.<\/jats:p>","DOI":"10.1177\/0194599819868178","type":"journal-article","created":{"date-parts":[[2019,8,20]],"date-time":"2019-08-20T04:48:51Z","timestamp":1566276531000},"page":"796-799","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Machine Learning Diagnosis of Peritonsillar Abscess"],"prefix":"10.1002","volume":"161","author":[{"given":"Michael B.","family":"Wilson","sequence":"first","affiliation":[{"name":"Department of Otolaryngology\u2013Head and Neck Surgery University of Michigan Ann Arbor Michigan USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. 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