AIMC Topic: United States

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A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors.

Journal of biomedical informatics
Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations...

Applications of Artificial Intelligence in Health Care Delivery.

Journal of medical systems
Health care costs now comprise nearly one-fifth of the United States' gross domestic product, with the last 25 years marked by rising administrative costs, a lack of labor productivity growth, and rising patient and physician dissatisfaction. Policy ...

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Drug and chemical toxicology
Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utiliz...

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age.

The lancet. Healthy longevity
BACKGROUND: Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framew...

Deep Learning-Based Survival Analysis for Receiving a Steatotic Donor Liver Versus Waiting for a Standard Liver.

Transplantation proceedings
BACKGROUND: An emerging strategy to expand the donor pool is the use of a steatotic donor liver (SDLs; ≥ 30% macrosteatosis on biopsy). With the obesity epidemic and prevalence of nonalcoholic fatty liver disease, SDLs have been reported in 59% of al...

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis.

Journal of medical Internet research
BACKGROUND: Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals...

Application of Artificial Intelligence to Patient-Targeted Health Information on Kidney Stone Disease.

Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation
OBJECTIVE: The American Medical Association recommends health information to be written at a 6th grade level reading level. Our aim was to determine whether Artificial Intelligence can outperform the existing health information on kidney stone preven...

AI as a New Frontier in Contrast Media Research: Bridging the Gap Between Contrast Media Reduction, the Contrast-Free Question and New Application Discoveries.

Investigative radiology
Artificial intelligence (AI) techniques are currently harnessed to revolutionize the domain of medical imaging. This review investigates 3 major AI-driven approaches for contrast agent management: new frontiers in contrast agent dose reduction, the c...

Sentiment Analysis of Tweets on Menu Labeling Regulations in the US.

Nutrients
Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learnin...

An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.

PloS one
BACKGROUND: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable...