Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience

We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506 eyes who underwent pars plana vitrectomy for rheg...

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Veröffentlicht in:	Scientific reports 2020-11, Vol.10 (1), p.19528-19528, Article 19528
Hauptverfasser:	Antaki, Fares, Kahwati, Ghofril, Sebag, Julia, Coussa, Razek Georges, Fanous, Anthony, Duval, Renaud, Sebag, Mikael
Format:	Artikel
Sprache:	eng
Schlagworte:	692/308/409 692/499 692/53/2423 Aged Algorithms Automation Bayesian analysis Diagnosis, Computer-Assisted Electronic medical records Female Humanities and Social Sciences Humans Learning algorithms Machine Learning Male Medical personnel Middle Aged multidisciplinary Ophthalmologists Postoperative Complications - etiology Retinal Detachment - surgery Retrospective Studies Risk Factors Science Science (multidisciplinary) Vitrectomy - adverse effects Vitrectomy - methods Vitreoretinopathy, Proliferative - etiology
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