AIMC Topic: Medically Underserved Area
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Evaluation of factors predicting transition from prediabetes to diabetes among patients residing in underserved communities in the United States - A machine learning approach.
Computers in biology and medicine
Feb 11, 2025
INTRODUCTION: Over one-third of the population in the United States (US) has prediabetes. Unfortunately, underserved population in the United States face a higher burden of prediabetes compared to urban areas, increasing the risk of stroke and heart ...
Perceived Impact of COVID-19 in an Underserved Community: A Natural Language Processing Approach.
Journal of advanced nursing
Oct 7, 2024
AIM: To utilise natural language processing (NLP) to analyse interviews about the impact of COVID-19 in underserved communities and to compare it to traditional thematic analysis in a small subset of interviews.
Underserved populations and health equity in dermatology: Digital medicine and the role of artificial intelligence.
Clinics in dermatology
Jun 27, 2024
We have reviewed the current literature focused on the role of artificial intelligence (AI) for underserved populations and health equity in dermatology. Studies evaluating the utility and safety of AI model builds, and how they meet predefined bench...
Telemedicine-Enhanced Lung Cancer Screening Using Mobile Computed Tomography Unit with Remote Artificial Intelligence Assistance in Underserved Communities: Initial Results of a Population Cohort Study in Western China.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Mar 1, 2024
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Bringing underserved communities life-saving aid through aerial logistics.
Science robotics
Dec 20, 2023
Autonomous drone delivery of medical supplies has improved access to health care for local communities in Africa.
Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.
Journal of telemedicine and telecare
Mar 13, 2023
INTRODUCTION: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of ...
Preventing corneal blindness caused by keratitis using artificial intelligence.
Nature communications
Jun 18, 2021
Keratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of op...
Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.
Journal of diabetes research
Sep 24, 2020
BACKGROUND: An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas...
Aged
Adult
Smoking
Area Under Curve
Male
Odds Ratio
Logistic Models
Humans
Asian People
Middle Aged
Alcohol Drinking
China
Machine Learning
Decision Making
Waist Circumference
Algorithms
Medically Underserved Area
Surveys and Questionnaires
Regression Analysis
Female
Diabetes Mellitus, Type 2
Decision Trees
Risk Factors
Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
INTRODUCTION: Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that ...
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