Public Health & Policy

Medicaid

Latest AI and machine learning research in medicaid for healthcare professionals.

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Accuracy of Low-Dose Chest CT-Based Artificial Intelligence Models in Osteoporosis Detection: A Systematic Review and Meta-analysis.

The purpose of this study is to systematically review and evaluate the accuracy of low-dose chest CT...

Early Detection and Remote Care of Frailty for Older Adults Living in Sparsely Populated Rural Areas in Hokkaido, Japan: A Case Study.

The global increase in the number of older people aged 65 and over is causing concern in healthcare ...

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell p...

Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.

OBJECTIVES: To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) ...

Multisequence 3-T Image Synthesis from 64-mT Low-Field-Strength MRI Using Generative Adversarial Networks in Multiple Sclerosis.

Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to ne...

A Multidimensional Regression Model for Predicting Recurrence in Chronic Low Back Pain.

BACKGROUND: Recurrence is common in chronic low back pain (CLBP). However, predicting the recurrence...

Real-Time sEMG Processing With Spiking Neural Networks on a Low-Power 5K-LUT FPGA.

The accurate modeling of hand movement based on the analysis of surface electromyographic (sEMG) sig...

[Intelligent Monitoring System Based on Computer Vision and Artificial Intelligence].

To ensure the quality of care for inpatients in ophthalmic hospitals, address the complex and variab...

Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.

BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to ...

[Comparison of the Impact of Deep Learning Techniques on Low-noise Head CT Images].

PURPOSE: This study aims to compare the effects of two types of deep learning (DL) techniques on bra...

Using articulatory feature detectors in progressive networks for multilingual low-resource phone recognitiona).

Systems inspired by progressive neural networks, transferring information from end-to-end articulato...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor recept...

Developing an AI-Assisted Platform to Support Tuberculosis Care Delivery.

Artificial Intelligence (AI) has the potential to "bridge the gap" between healthcare provider and p...

Assessment of readability, reliability, and quality of ChatGPT®, BARD®, Gemini®, Copilot®, Perplexity® responses on palliative care.

There is no study that comprehensively evaluates data on the readability and quality of "palliative ...

Machine learning models for differential diagnosing HER2-low breast cancer: A radiomics approach.

To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging...

CT radiomics-based machine learning model for differentiating between enchondroma and low-grade chondrosarcoma.

It may be difficult to distinguish between enchondroma and low-grade malignant cartilage tumors (gra...

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

OBJECTIVE: To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used t...

Prediction of low-density lipoprotein cholesterol levels using machine learning methods.

OBJECTIVE: Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, bu...

Prediction of Low Bone Mass for Japanese Female Athletes Using Machine Learning.

Attainment of optimal bone mineral density (BMD) in female athletes is a very important aspect of en...

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