scholarly article | Q13442814 |
P356 | DOI | 10.1038/S41598-020-76665-3 |
P698 | PubMed publication ID | 33177614 |
P50 | author | Fares Antaki | Q92133994 |
P2093 | author name string | Renaud Duval | |
Mikael Sebag | |||
Razek Georges Coussa | |||
Anthony Fanous | |||
Ghofril Kahwati | |||
Julia Sebag | |||
P2860 | cites work | Intravitreal low molecular weight heparin and 5-Fluorouracil for the prevention of proliferative vitreoretinopathy following retinal reattachment surgery | Q24201611 |
Risk factors for proliferative vitreoretinopathy after primary vitrectomy: a prospective study | Q24673031 | ||
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement | Q28082297 | ||
Clinical risk factors for proliferative vitreoretinopathy | Q30460309 | ||
Proliferative vitreoretinopathy: risk factors and pathobiology | Q34572836 | ||
Applying Data Mining Techniques to Improve Breast Cancer Diagnosis | Q36096875 | ||
Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery | Q37217833 | ||
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 example | Q40549827 | ||
How to predict proliferative vitreoretinopathy: a prospective study | Q43653229 | ||
Development of predictive models of proliferative vitreoretinopathy based on genetic variables: the Retina 4 project | Q43828088 | ||
Prevalence and risk factors for proliferative vitreoretinopathy in eyes with rhegmatogenous retinal detachment but no previous vitreoretinal surgery | Q44926629 | ||
SMOTE: Synthetic Minority Over-sampling Technique | Q47247524 | ||
Predicting proliferative vitreoretinopathy: temporal and external validation of models based on genetic and clinical variables | Q48539185 | ||
Deep Learning and its Applications in Biomedicine. | Q50420138 | ||
A survey of cross-validation procedures for model selection | Q56689497 | ||
An updated classification of retinal detachment with proliferative vitreoretinopathy | Q67938098 | ||
Clinical risk factors for proliferative vitreoretinopathy after retinal detachment surgery | Q72652703 | ||
Proliferative vitreoretinopathy: an overview | Q77144020 | ||
Prediction of proliferative vitreoretinopathy after retinal detachment surgery: potential of biomarker profiling | Q84027262 | ||
PREDICTIVE FACTORS FOR PROLIFERATIVE VITREORETINOPATHY FORMATION AFTER UNCOMPLICATED PRIMARY RETINAL DETACHMENT REPAIR | Q88771950 | ||
Proliferative Vitreoretinopathy: A Review | Q90754705 | ||
Gold Standard Evaluation of an Automatic HAIs Surveillance System | Q90993292 | ||
A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies | Q97681882 | ||
Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology | Q97681889 | ||
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study | Q104478955 | ||
The WEKA data mining software: an update | Q105584187 | ||
P433 | issue | 1 | |
P921 | main subject | proliferative vitreoretinopathy | Q7249608 |
machine learning | Q2539 | ||
automated machine learning | Q43967068 | ||
P304 | page(s) | 19528 | |
P577 | publication date | 2020-11-11 | |
P1433 | published in | Scientific Reports | Q2261792 |
P1476 | title | Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience | |
P478 | volume | 10 |
Search more.