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

scientific article published on 11 November 2020

Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.1038/S41598-020-76665-3
P698PubMed publication ID33177614

P50authorFares AntakiQ92133994
P2093author name stringRenaud Duval
Mikael Sebag
Razek Georges Coussa
Anthony Fanous
Ghofril Kahwati
Julia Sebag
P2860cites workIntravitreal low molecular weight heparin and 5-Fluorouracil for the prevention of proliferative vitreoretinopathy following retinal reattachment surgeryQ24201611
Risk factors for proliferative vitreoretinopathy after primary vitrectomy: a prospective studyQ24673031
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statementQ28082297
Clinical risk factors for proliferative vitreoretinopathyQ30460309
Proliferative vitreoretinopathy: risk factors and pathobiologyQ34572836
Applying Data Mining Techniques to Improve Breast Cancer DiagnosisQ36096875
Countering imbalanced datasets to improve adverse drug event predictive models in labor and deliveryQ37217833
Machine Learning Techniques in Clinical Vision Sciences.Q38881742
External validation of existing formulas to predict the risk of developing proliferative vitreoretinopathy: the Retina 1 Project; report 5.Q39433770
External validation is necessary in prediction research: a clinical exampleQ40549827
How to predict proliferative vitreoretinopathy: a prospective studyQ43653229
Development of predictive models of proliferative vitreoretinopathy based on genetic variables: the Retina 4 projectQ43828088
Prevalence and risk factors for proliferative vitreoretinopathy in eyes with rhegmatogenous retinal detachment but no previous vitreoretinal surgeryQ44926629
SMOTE: Synthetic Minority Over-sampling TechniqueQ47247524
Predicting proliferative vitreoretinopathy: temporal and external validation of models based on genetic and clinical variablesQ48539185
Deep Learning and its Applications in Biomedicine.Q50420138
A survey of cross-validation procedures for model selectionQ56689497
An updated classification of retinal detachment with proliferative vitreoretinopathyQ67938098
Clinical risk factors for proliferative vitreoretinopathy after retinal detachment surgeryQ72652703
Proliferative vitreoretinopathy: an overviewQ77144020
Prediction of proliferative vitreoretinopathy after retinal detachment surgery: potential of biomarker profilingQ84027262
PREDICTIVE FACTORS FOR PROLIFERATIVE VITREORETINOPATHY FORMATION AFTER UNCOMPLICATED PRIMARY RETINAL DETACHMENT REPAIRQ88771950
Proliferative Vitreoretinopathy: A ReviewQ90754705
Gold Standard Evaluation of an Automatic HAIs Surveillance SystemQ90993292
A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning StudiesQ97681882
Applications of Artificial Intelligence to Electronic Health Record Data in OphthalmologyQ97681889
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility studyQ104478955
The WEKA data mining software: an updateQ105584187
P433issue1
P921main subjectproliferative vitreoretinopathyQ7249608
machine learningQ2539
automated machine learningQ43967068
P304page(s)19528
P577publication date2020-11-11
P1433published inScientific ReportsQ2261792
P1476titlePredictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience
P478volume10

Search more.