Machine learning approaches for electronic health records phenotyping: a methodical review
S Yang, P Varghese, E Stephenson… - Journal of the …, 2023 - academic.oup.com
Objective Accurate and rapid phenotyping is a prerequisite to leveraging electronic health
records for biomedical research. While early phenotyping relied on rule-based algorithms …
records for biomedical research. While early phenotyping relied on rule-based algorithms …
Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies
Z Wang, Z Li, K Li, S Mu, X Zhou, Y Di - Frontiers in endocrinology, 2023 - frontiersin.org
Aims To systematically evaluate the diagnostic value of an artificial intelligence (AI)
algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the …
algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the …
Development and validation of a diabetic retinopathy risk stratification algorithm
D Tarasewicz, AJ Karter, N Pimentel, HH Moffet… - Diabetes …, 2023 - Am Diabetes Assoc
OBJECTIVE Although diabetic retinopathy is a leading cause of blindness worldwide,
diabetes-related blindness can be prevented through effective screening, detection, and …
diabetes-related blindness can be prevented through effective screening, detection, and …
Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes
The adoption of electronic health records in hospitals has ensured the availability of large
datasets that can be used to predict medical complications. The trajectories of patients in …
datasets that can be used to predict medical complications. The trajectories of patients in …
An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease Phenotyping
K Harrigian, T Tang, A Gonzales, CX Cai… - arXiv preprint arXiv …, 2023 - arxiv.org
Diabetic eye disease is a major cause of blindness worldwide. The ability to monitor relevant
clinical trajectories and detect lapses in care is critical to managing the disease and …
clinical trajectories and detect lapses in care is critical to managing the disease and …
Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography
Background To apply machine learning (ML) algorithms to perform multiclass diabetic
retinopathy (DR) classification using both clinical data and optical coherence tomography …
retinopathy (DR) classification using both clinical data and optical coherence tomography …
Drug exposure as a predictor in diabetic retinopathy risk prediction models; a systematic review and meta-analysis
MA Bantounou, TAK Nahar, J Plascevic… - American Journal of …, 2024 - Elsevier
Purpose To conduct a systematic review to assess drug exposure handling in diabetic
retinopathy (DR) risk prediction models, a network-meta-analysis to identify drugs …
retinopathy (DR) risk prediction models, a network-meta-analysis to identify drugs …
[HTML][HTML] Retinopathy prediction in type 2 diabetes: Time-varying Cox proportional hazards and machine learning models
P Looareesuwan, S Boonmanunt, S Siriyotha… - Informatics in Medicine …, 2023 - Elsevier
Background Diabetic retinopathy (DR) is one of the most common complications in type 2
diabetes (T2D) with an estimated prevalence of 22%. Predictive modelling has largely been …
diabetes (T2D) with an estimated prevalence of 22%. Predictive modelling has largely been …
Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy
D Musetti, CA Cutolo, M Bonetto… - European Journal …, 2024 - journals.sagepub.com
Purpose: To assess the role of artificial intelligence (AI) based automated software for
detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography …
detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography …
[HTML][HTML] Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels
Objective The paper aims to address the problem of massive unlabeled patients in
electronic health records (EHR) who potentially have undiagnosed diabetic retinopathy …
electronic health records (EHR) who potentially have undiagnosed diabetic retinopathy …