ArticleTranslational vision science & technology2017
Predicting Future Self-Reported Motor Vehicle Collisions in Subjects with Primary Open-Angle Glaucoma Using the Penalized Support Vector Machine Method.
Article in Translational vision science & technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Predicting the Real-World Future of Glaucoma Patients? Cautions Are Required for Machine Learning.Translational vision science & technology · 2017Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeWe predict the likelihood of a future motor vehicle collision (MVC) from visual function data, attitudes to driving, and past MVC history using the penalized support vector machine (pSVM) in subjects with primary open-angle glaucoma (POAG).
methodsPatients with POAG were screened prospectively for eligibility and 185 were analyzed in this study. Self-reported MVCs of all participants were recorded for 3 years from the baseline using a survey questionnaire every 12 months. A binocular integrated visual field (IVF) was calculated for each patient by merging a patient's monocular Humphrey Field Analyzer (HFA) visual fields (VFs). The IVF was divided into six regions, based on eccentricity and the right or left hemifield, and the average of the total deviation (TD) values in each of these six areas was calculated. Then, the future MVCs were predicted using various variables, including age, sex, 63 variables of 52 TD values, mean of the TD values, visual acuities (VAs), six sector average TDs with (pred
resultsThe relationship between pred
conclusionsIt was useful to predict future MVCs in patients with POAG using visual function metrics, patients' attitudes to driving, and past MVC history, using the pSVM. TRANSLATIONAL RELEVANCE: Careful consideration is needed when predicting future MVCs in POAG patients using visual function, and without driving attitude and MVC history.
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