Machine Learning Diagnosis of Peritonsillar Abscess.
Otolaryngol Head Neck Surg
; 161(5): 796-799, 2019 11.
Article
en En
| MEDLINE
| ID: mdl-31426695
ABSTRACT
Peritonsillar abscess (PTA) is a difficult diagnosis to make clinically, with clinical examination of even otolaryngologists showing poor sensitivity and specificity. Machine learning is a form of artificial intelligence that "learns" from data to make predictions. 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-performing 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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Absceso Peritonsilar
/
Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adolescent
/
Adult
/
Aged
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Aged80
/
Child
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Child, preschool
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Female
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Humans
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Infant
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Male
Idioma:
En
Revista:
Otolaryngol Head Neck Surg
Asunto de la revista:
OTORRINOLARINGOLOGIA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos