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Review
. 2020 Feb 27;9(2):13.
doi: 10.1167/tvst.9.2.13.

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

Wei-Chun Lin  1 , Jimmy S Chen  2 , Michael F Chiang  1   3 , Michelle R Hribar  1
Affiliations
Review

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

Wei-Chun Lin et al. Transl Vis Sci Technol. .

Abstract

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.

Keywords: artificial intelligence; electronic health record; machine learning; ophthalmology.

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Conflict of interest statement

Disclosure: W.-C. Lin, None; J.S. Chen, None; M.F. Chiang, Novartis (C), and Inteleretina, LLC (E); M.R. Hribar, None

Figures

Figure 1.
Figure 1.
Flow diagram for the literatures selection.
Figure 2.
Figure 2.
Schematic of the steps of machine learning application. NLP, natural language processing; SVM, support vector machine; CART, classification and regression tree; CNN, convolutional neural network; FNN, feed forward neural network.
Figure 3.
Figure 3.
Illustrations of machine learning models. 3A. Linear regression; 3B. Logistic regression; 3C. Support vector machine; 3D. Classification and regression trees (CART); 3E. Ensemble methods; 3F. Artificial neural network (ANN).
See this image and copyright information in PMC

References

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Publication types