AIMC Topic: United States

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Identification of prognostic factors for pediatric myocarditis with a random forests algorithm-assisted approach.

Pediatric research
BACKGROUND: Pediatric myocarditis is a rare disease with substantial mortality. Little is known regarding its prognostic factors. We hypothesize that certain comorbidities and procedural needs may increase risks of poor outcomes. This study aims to i...

Machine learning to predict mortality after rehabilitation among patients with severe stroke.

Scientific reports
Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decisi...

Acceptability of Using a Robotic Nursing Assistant in Health Care Environments: Experimental Pilot Study.

Journal of medical Internet research
BACKGROUND: According to the US Bureau of Labor Statistics, nurses will be the largest labor pool in the United States by 2022, and more than 1.1 million nursing positions have to be filled by then in order to avoid a nursing shortage. In addition, t...

Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results across settings and populations. With recent uptake in use of digital health modalities, more precise patient classification may improve outcomes, eff...

Clinician Perceptions of Robotic Exoskeletons for Locomotor Training After Spinal Cord Injury: A Qualitative Approach.

Archives of physical medicine and rehabilitation
OBJECTIVE: To describe the experiences of clinicians who have used robotic exoskeletons in their practice and acquire information that can guide clinical decisions and training strategies related to robotic exoskeletons.

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Journal of the American Heart Association
Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-based interventions have been shown to be both effective and cost-effective in preventing cardiovascul...

Trends in robotic surgery utilization across tertiary children's hospitals in the United States.

Surgical endoscopy
BACKGROUND: A growing number of tertiary children's hospitals are utilizing robotic surgical technology. We sought to characterize national trends in pediatric surgical robotic case utilization and related drivers.

Conversion rate of laparoscopic or robotic to open sacrocolpopexy: are there associated factors and complications?

International urogynecology journal
OBJECTIVES: To evaluate the conversion rate of laparoscopic or robotic to open sacrocolpopexy and to identify associated factors in a large population-based database.

A Novel Use of Artificial Intelligence to Examine Diversity and Hospital Performance.

The Journal of surgical research
BACKGROUND: The US population is becoming more racially and ethnically diverse. Research suggests that cultural diversity within organizations can increase team potency and performance, yet this theory has not been explored in the field of surgery. F...

Comparison of Machine Learning Models for the Androgen Receptor.

Environmental science & technology
The androgen receptor (AR) is a target of interest for endocrine disruption research, as altered signaling can affect normal reproductive and neurological development for generations. In an effort to prioritize compounds with alternative methodologie...