Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience
| العنوان: | Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience |
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| المؤلفون: | Julia Sebag, Mikael Sebag, Renaud Duval, Anthony Fanous, Razek Georges Coussa, Ghofril Kahwati, Fares Antaki |
| المصدر: | Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020) Scientific Reports |
| بيانات النشر: | Nature Publishing Group, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Pars plana, Male, Proliferative vitreoretinopathy, lcsh:Medicine, Feature selection, Machine learning, computer.software_genre, Predictive markers, Article, Machine Learning, 03 medical and health sciences, Naive Bayes classifier, 0302 clinical medicine, Postoperative Complications, Vitrectomy, medicine, Humans, Diagnosis, Computer-Assisted, lcsh:Science, Aged, Retrospective Studies, Multidisciplinary, Ophthalmologists, business.industry, Vitreoretinopathy, Proliferative, lcsh:R, Retinal Detachment, Retinal detachment, Retrospective cohort study, Middle Aged, medicine.disease, eye diseases, Support vector machine, medicine.anatomical_structure, Risk factors, Outcomes research, 030221 ophthalmology & optometry, Female, lcsh:Q, Artificial intelligence, sense organs, business, computer, 030217 neurology & neurosurgery, Algorithms, Coding (social sciences) |
| الوصف: | 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 rhegmatogenous retinal detachment (RRD) by a single surgeon at a tertiary-care hospital between 2012 and 2019. Two ophthalmologists without coding experience used an interactive application in MATLAB to build and evaluate ML algorithms for the prediction of postoperative PVR using clinical data from the electronic health records. The clinical features associated with postoperative PVR were determined by univariate feature selection. The area under the curve (AUC) for predicting postoperative PVR was better for models that included pre-existing PVR as an input. The quadratic support vector machine (SVM) model built using all selected clinical features had an AUC of 0.90, a sensitivity of 63.0%, and a specificity of 97.8%. An optimized Naïve Bayes algorithm that did not include pre-existing PVR as an input feature had an AUC of 0.81, a sensitivity of 54.3%, and a specificity of 92.4%. In conclusion, the development of ML models for the prediction of PVR by ophthalmologists without coding experience is feasible. Input from a data scientist might still be needed to tackle class imbalance—a common challenge in ML classification using real-world clinical data. |
| اللغة: | English |
| تدمد: | 2045-2322 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8902a9184c532b0ef2e2546f830700fTest http://link.springer.com/article/10.1038/s41598-020-76665-3Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b8902a9184c532b0ef2e2546f830700f |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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