Medicaid

Forecasting trends of rising emergency department chest imaging using machine learning.

INTRODUCTION: Imaging studies in the acute care setting, such as the emergency room, have been increasing. In this report, we use the Centers for Medicare and Medicaid services (CMS) database to assess trends in ED chest CT and chest CTA imaging in ED from 2010 to 2021. In addition, we forecast the utilization of these imaging modalities until 2030 to predict volume of studies using machine learni...

Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts.

BACKGROUND: The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this chal...

Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.

BACKGROUND: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial cos...

Bridging the Digital Divide: A Practical Roadmap for Deploying Medical Artificial Intelligence Technologies in Low-Resource Settings.

In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and deliv...

Low-Rank Representation with Empirical Kernel Space Embedding of Manifolds.

Low-Rank Representation (LRR) methods integrate low-rank constraints and projection operators to model the mapping from the sample space to low-dimens...

When low-light meets flares: Towards Synchronous Flare Removal and Brightness Enhancement.

Low-light image enhancement (LLIE) aims to improve the visibility and illumination of low-light images. However, real-world low-light images are usual...

Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model.

BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosis. Accurate diagnosis can be hampered by tradition...

Artificial intelligence and informatics in neonatal resuscitation.

Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be e...

Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.

Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made imp...

Meta Learning With Graph Attention Networks for Low-Data Drug Discovery.

Finding candidate molecules with favorable pharmacological activity, low toxicity, and proper pharmacokinetic properties is an important task in drug ...

Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area.

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an ...

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalation...

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care phys...

An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial.

BACKGROUND: Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we teste...

Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.

This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing gu...

Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset.

Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental ra...

Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mam...

Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness.

BACKGROUND: The Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries...

[Data-driven intensive care: a lack of comprehensive datasets].

Intensive care units provide a data-rich environment with the potential to generate datasets in the realm of big data, which could be utilized to trai...

Effect of insurance status on perioperative outcomes after robotic pancreaticoduodenectomy: a propensity-score matched analysis.

The influence of Medicaid or being uninsured is prevailingly thought to negatively impact a patient's socioeconomic and postoperative course, yet litt...