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.
We test theoretically informed hypotheses using survey reports of adolescents attending three middle schools in the outskirts of Fuzhou, Fujian, China. Results yielded by regression analyses are quite consistent with the hypothesized relationships, that is, Chinese singleton adolescents are more likely to anticipate going to college than non‐singleton adolescents. Further, singletons are more associated with conventional peers and they report better adjustments both psychologically and behaviorally than non‐singleton adolescents. Singletons and non‐singletons, however, are not different in their self‐reported performance in four school subjects, namely, Chinese, Math, English, and Political Studies. These results are discussed in light of the theoretical literature, especially related to attachment theory, resource dilution theory as well as confluence model.
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