Public Health & Policy

Medicaid

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

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Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach.

BACKGROUND: Twitter is a potentially valuable tool for public health officials and state Medicaid pr...

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units.

Disaster robotics is a growing field that is concerned with the design and development of robots for...

Congenital Hypothyroidism due to a Low Level of Maternal Thyrotropin Receptor-Blocking Antibodies.

INTRODUCTION: Maternal TSH receptor antibodies (TRAbs) can cross the placenta and affect fetal and n...

Robotic Surgical Training at Home: A Low-Fidelity Simulation Method.

OBJECTIVE: Laparoscopic box simulators provide surgical residents a cost-effective and accessible le...

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise.

OBJECTIVE: Iterative reconstruction degrades image quality. Thus, further advances in image reconstr...

ENTRYWISE EIGENVECTOR ANALYSIS OF RANDOM MATRICES WITH LOW EXPECTED RANK.

Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statisti...

Is there any association between low level of serum nesfatin-1 and fibromyalgia syndrome?

OBJECTIVES: This study aims to investigate the relationship between serum level of nesfatin-1 and fi...

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.

OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying em...

Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of p...

Hybrid Low-Order and Higher-Order Graph Convolutional Networks.

With the higher-order neighborhood information of a graph network, the accuracy of graph representat...

Analyzing National Incidences and Predictors of Open Conversion During Minimally Invasive Partial Nephrectomy for cT1 Renal Masses.

To analyze predictors of open conversion during minimally invasive partial nephrectomy (MIPN) for c...

Low-dose CT with deep learning regularization via proximal forward-backward splitting.

Low-dose x-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops ...

Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

PURPOSE: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-cou...

Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the ...

Supervised mixture of experts models for population health.

We propose a machine learning driven approach to derive insights from observational healthcare data ...

Real-Time Human Action Recognition with a Low-Cost RGB Camera and Mobile Robot Platform.

Human action recognition is an important research area in the field of computer vision that can be a...

Multi-legged steering and slipping with low DoF hexapod robots.

Thanks to their sprawled posture and multi-legged support, stability is not as hard to achieve for h...

Development of a real-time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications.

PURPOSE: An indoor, real-time location system (RTLS) can benefit both hospitals and patients by impr...

Robotic low anterior resection: how to maximise success in difficult surgery.

Minimally invasive laparoscopic low or ultra-low anterior resection may present as a complex, techni...

Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.

Accelerated MRI involves undersampling k-space, creating unwanted artifacts when reconstructing the ...

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