Usefulness of an Artificial Neural Network in the Prediction of β-Lactam Allergy

An accurate diagnosis of β-lactam (BL) allergy improves the use of antibiotics, increases patients’ safety, and reduces costs to health systems. Nevertheless, it requires skin and drug provocation tests, which are time-consuming and put the patient at risk. Furthermore, allergy testing is not availa...

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Veröffentlicht in:	The journal of allergy and clinical immunology in practice (Cambridge, MA) MA), 2020-10, Vol.8 (9), p.2974-2982.e1
Hauptverfasser:	Moreno, Esther M., Moreno, Vidal, Laffond, Elena, Gracia-Bara, M. Teresa, Muñoz-Bellido, Francisco J., Macías, Eva M., Curto, Belen, Campanon, M. Valle, de Arriba, Sonia, Martin, Cristina, Davila, Ignacio
Format:	Artikel
Sprache:	eng
Schlagworte:	Allergies Allergy workup Anti-Bacterial Agents - therapeutic use Antibiotics Artificial intelligence Artificial neural networks Back propagation beta-Lactams Decision making Decision trees Diagnosis Drug dosages Drug hypersensitivity Drug Hypersensitivity - diagnosis Drug Hypersensitivity - drug therapy Drug Hypersensitivity - epidemiology Drug provocation tests Erythema Humans Machine learning Neural networks Neural Networks, Computer Neurons Patients Penicillin Penicillin allergy Predictive models Prospective Studies Provocation tests Retrospective Studies Risk groups Skin Tests Software Staphylococcus infections Statistical analysis Variables β-Lactam antibiotics β-Lactams
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