[HTML][HTML] Using machine learning for healthcare challenges and opportunities

A Alanazi - Informatics in Medicine Unlocked, 2022 - Elsevier
Abstract Machine learning (ML) and its applications in healthcare have gained a lot of
attention. When enhanced computational power is combined with big data, there is an …

[HTML][HTML] Machine learning and data mining methods in diabetes research

I Kavakiotis, O Tsave, A Salifoglou… - Computational and …, 2017 - Elsevier
The remarkable advances in biotechnology and health sciences have led to a significant
production of data, such as high throughput genetic data and clinical information, generated …

Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems

AY Lee, RT Yanagihara, CS Lee, M Blazes… - Diabetes …, 2021 - Am Diabetes Assoc
OBJECTIVE With rising global prevalence of diabetic retinopathy (DR), automated DR
screening is needed for primary care settings. Two automated artificial intelligence (AI) …

Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms

HY Tsao, PY Chan, ECY Su - BMC bioinformatics, 2018 - Springer
Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the
past studies, but it remains unknown which risk factors were more associated with the DR …

Using machine learning techniques to develop risk prediction models for the risk of incident diabetic retinopathy among patients with type 2 diabetes mellitus: a cohort …

Y Zhao, X Li, S Li, M Dong, H Yu, M Zhang… - Frontiers in …, 2022 - frontiersin.org
Objective To construct and validate prediction models for the risk of diabetic retinopathy
(DR) in patients with type 2 diabetes mellitus. Methods Patients with type 2 diabetes mellitus …

Deep neural network for predicting diabetic retinopathy from risk factors

G Alfian, M Syafrudin, NL Fitriyani, M Anshari, P Stasa… - Mathematics, 2020 - mdpi.com
Extracting information from individual risk factors provides an effective way to identify
diabetes risk and associated complications, such as retinopathy, at an early stage. Deep …

Diagnostic performance of artificial intelligence for detection of anterior cruciate ligament and meniscus tears: A systematic review

KN Kunze, DM Rossi, GM White, AV Karhade… - … : The Journal of …, 2021 - Elsevier
Purpose To (1) determine the diagnostic efficacy of artificial intelligence (AI) methods for
detecting anterior cruciate ligament (ACL) and meniscus tears and to (2) compare the …

Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis

SA Saputro, O Pattanaprateep, A Pattanateepapon… - Systematic reviews, 2021 - Springer
Background Many prognostic models of diabetic microvascular complications have been
developed, but their performances still varies. Therefore, we conducted a systematic review …

[HTML][HTML] Multivariate data binning and examples generation to build a Diabetic Retinopathy classifier based on temporal clinical and analytical risk factors

J Pascual-Fontanilles, A Valls… - Knowledge-Based Systems, 2024 - Elsevier
In this paper, we explore the possibility of exploiting retrospective clinical data from
Electronic Health Records (EHR) for classification tasks in chronic patients. The different …

Integrated machine learning approaches for predicting ischemic stroke and thromboembolism in atrial fibrillation

X Li, H Liu, X Du, P Zhang, G Hu, G Xie… - AMIA Annual …, 2017 - pmc.ncbi.nlm.nih.gov
Atrial fibrillation (AF) is a common cardiac rhythm disorder, which increases the risk of
ischemic stroke and other thromboembolism (TE). Accurate prediction of TE is highly …