Predicting Refractive Surgery Outcome: Machine Learning Approach With Big Data
- 1 September 2017
- journal article
- Published by SLACK, Inc. in Journal of Refractive Surgery
- Vol. 33 (9), 592-597
- https://doi.org/10.3928/1081597x-20170616-03
Abstract
This model could support clinical decision making and may lead to better individual risk assessment. Expanding the role of machine learning in analyzing big data from refractive surgeries may be of interest. [J Refract Surg. 2017;33(9):592-597.].This publication has 17 references indexed in Scilit:
- Factors Predicting the Need for Retreatment After Laser Refractive SurgeryCornea, 2016
- Decision forest: Twenty years of researchInformation Fusion, 2016
- Past and present of corneal refractive surgeryActa Ophthalmologica, 2014
- Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCTJournal of Ophthalmology, 2013
- The process and utility of classification and regression tree methodology in nursing researchJournal of Advanced Nursing, 2013
- Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?BMJ Open, 2013
- Sources of Medical Error in Refractive SurgeryJournal of Refractive Surgery, 2013
- Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinationsClinics, 2010
- IDOCS: Intelligent Distributed Ontology Consensus System—The Use of Machine Learning in Retinal Drusen PhenotypingInvestigative Ophthalmology & Visual Science, 2007
- Machine learning in bioinformaticsBriefings in Bioinformatics, 2006