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Showing 20 of 243 academic articles

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records

Cristian Soto Jacome, MD, Danny Segura Torres, MD, Jungwei W. Fan, PhD, Ricardo Loor-Torres, MD et al. • Mayo Clinic Proceedings: Digital Health (2024)

Objective: To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS). Patients and Methods: Between 2017 and 2021, we identified 18...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mcpdig.2024.01.001

Deep Learning for Diabetes: A Systematic Review

Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou • IEEE Journal of Biomedical and Health Informatics (2020)

Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of ...

OPENALEX other-oa DOI: https://doi.org/10.1109/jbhi.2020.3040225

An Automated Image-Based Dietary Assessment System for Mediterranean Foods

Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis • IEEE Open Journal of Engineering in Medicine and Biology (2023)

Goal: The modern way of living has significantly influenced the daily diet. The ever-increasing number of people with obesity, diabetes and cardiovascular diseases stresses the need to find tools that could help in the daily intake of the n...

DOAJ Open Access (DOAJ) DOI: 10.1109/OJEMB.2023.3266135

Machine Learning and Data Mining Methods in Diabetes Research

Ioannis Kavakiotis, O. Tsave, Athanasios Salifoglou, Nicos Maglaveras et al. • Computational and Structural Biotechnology Journal (2017)

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 from large Electronic Health Records (EHRs). To this end, ap...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.csbj.2016.12.005 PMC: 5257026

Development and validation of a predictive model for diabetic peripheral neuropathy with type 2 diabetes mellitus in Xinjiang, China

Asiguli Adili, Maihesumu Aikemu, Karanvir Singh Rana, Yihan Wei et al. • Scientific Reports (2025)

Abstract This study aims to identify risk factors associated with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetesmellitus (T2DM) and to develop a predictive model to support clinical decision-making. A total of 1,001 p...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-19922-7

Machine learning in pain research

Jörn Lötsch, Alfred Ultsch • Pain (2017)

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1097/j.pain.0000000000001118 PMC: 5895117

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis

Lin Wang, Jiaming Su, Zhongjie Liu, Shaowei Ding et al. • BioData Mining (2024)

Abstract Background Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-st...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13040-024-00369-x

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Kai Zhao, Yujia Shi, Hon-Cheong So • Pharmaceutics (2022)

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles fro...

DOAJ Open Access (DOAJ) DOI: 10.3390/pharmaceutics14020234

Comparing Clinical Metadata Fusion Strategies for Diabetic and Hypertensive Retinopathy Classification

Vinicius de Carvalho Rispoli, Alan Müller, Weverson Garcia Medeiros, Marília Miranda Forte Gomes et al. • Machine Learning and Knowledge Extraction (2026)

Structured clinical variables may complement fundus photographs, but the most appropriate point at which to integrate them into a retinal foundation model remains uncertain. This study compares an image-only baseline with three strategies f...

DOAJ Open Access (DOAJ) DOI: 10.3390/make8100299

Investigating the Role of Artificial Intelligence in Management of Diabetes in Iran: A Systematic Review

Fatemeh Bahador, Azam Sabahi, Samaneh Jalali, Fatemeh Ameri • پیاورد سلامت (2023)

Background and Aim: Diabetes is one of the most common metabolic diseases in Iran and the fifth leading cause of death all over the world. Its spread around the world has created new methods in biomedical research, including artificial inte...

DOAJ Open Access (DOAJ)

Economic Evaluations of Artificial Intelligence Implementation in Diabetic Retinopathy Screening

Ahmad Ahmad, Neily Zakiyah, Auliya A. Suwantika • Pharmacology and Clinical Pharmacy Research (2025)

Diabetic retinopathy (DR) is a common complication of diabetes that damages retinal blood vessels and can lead to vision impairment. The application of Artificial Intelligence (AI) in DR screening offers a promising alternative to conventio...

DOAJ Open Access (DOAJ) DOI: 10.15416/pcpr.v9i3.60068

Risk of Ischemic Heart Disease in Patients With Postpancreatectomy Diabetes and Pancreatic Cancer: A Population‐Based Study

Daegwang Yoo, Minsun Kang, Jaehun Jung • Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease (2023)

Background Postpancreatectomy diabetes can be caused by resection of functioning pancreatic tissue and is associated with postoperative pancreatic islet cell mass loss and subsequent endocrine dysfunction. Diabetes is a well‐known risk fact...

DOAJ Open Access (DOAJ) DOI: 10.1161/JAHA.123.031321

Sex-based differences in characteristics, management, and outcomes in heart failure with reduced ejection fraction

Christian Basile, Stefan D. Anker, Gianluigi Savarese • Global Cardiology (2025)

Heart failure with reduced ejection fraction (HFrEF) exhibits significant sex-based differences in clinical presentation, management, and outcomes. This study aimed to evaluate these differences using data from the Swedish Heart Failure Reg...

DOAJ Open Access (DOAJ) DOI: 10.4081/cardio.2025.73