Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records

Baxter, Sally L.; Marks, Charles; Kuo, Tsung-Ting; Ohno-Machado, Lucila; Weinreb, Robert N.

doi:10.1016/j.ajo.2019.07.005

Cited by 49 publications

(43 citation statements)

References 45 publications

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“…Two studies in this review focused on the field of glaucoma and used supervised machine learning techniques to improve diagnosis and predict progression. 16 , 22 In the study by Chaganti et al., 16 a good performance was obtained (AUC of glaucoma diagnosis 88%), and results showed that the addition of an EMR phenotype could improve the classification accuracy of a random forest classifier with imaging biomarkers. 16 On the other hand, Baxter et al.…”

Section: Resultsmentioning

confidence: 96%

“…As shown in the Table , 8 studies used AUC-ROC to evaluate the performance of classifiers. 15 – 17 , 20 – 22 , 25 , 28 The range of AUC-ROC was from 65% to 98.5%, and the median AUC in all included studies was 90%. In addition, precision and recall were used to evaluate the performance of text-mining algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, Baxter et al. 22 used random forest and logistic regression to identify patients with open-angle glaucoma who had a risk of progression to surgical intervention. Fraccaro et al.…”

Section: Resultsmentioning

confidence: 99%

“… 16 On the other hand, Baxter et al. 22 reported a moderate performance (AUC 67%) in a study that used EHR data alone to predict risk of progression to surgical intervention in patients with open-angle glaucoma. In addition to model performance, it is important to know which factors can be used to improve disease diagnosis.…”

Section: Resultsmentioning

confidence: 99%

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Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

Lin

Chen

Chiang

et al. 2020

Trans. Vis. Sci. Tech.

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Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data. Artificial intelligence (AI) techniques present a promising way to analyze these multimodal data sets. While AI techniques have been extensively applied to imaging data, there are a limited number of studies employing AI techniques with clinical data from the EHR. The objective of this review is to provide an overview of different AI methods applied to EHR data in the field of ophthalmology. This literature review highlights that the secondary use of EHR data has focused on glaucoma, diabetic retinopathy, age-related macular degeneration, and cataracts with the use of AI techniques. These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

Lin

Chen

Chiang

et al. 2020

Trans. Vis. Sci. Tech.

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show abstract

“…They identified high blood pressure as a factor increasing the likelihood of surgical intervention, and several categories of ophthalmic and non-ophthalmic factors decreasing the likelihood of surgery. 11 In contrast to their study, this one predicts implant failure instead of the need for surgical intervention, and includes more classifiers and types of glaucoma.…”

Section: Introductionmentioning

confidence: 97%

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2021

IJDKP

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The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of 165 patients were used. Potential predictors included the patients' race, age, sex, preoperative intraocular pressure (IOP), preoperative visual acuity, number of IOP-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final IOP greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. With a small amount of data, the best classfier was logistic regression, but with more data, the best classifier was the random forest.

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