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Predicting State-Level Firearm Suicide Rates: A Machine Learning Approach Using Public Policy Data.

American journal of preventive medicine
INTRODUCTION: Over 40,000 people die by suicide annually in the U.S., and firearms are the most lethal suicide method. There is limited evidence on the effectiveness of many state-level policies on reducing firearm suicide. The objective of this stud...

A Machine Learning Algorithm Avoids Unnecessary Paracentesis for Exclusion of SBP in Cirrhosis in Resource-limited Settings.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
BACKGROUND & AIMS: Despite the poor prognosis associated with missed or delayed spontaneous bacterial peritonitis (SBP) diagnosis, <15% get timely paracentesis, which persists despite guidelines/education in the United States. Measures to exclude SBP...

Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions.

JDR clinical and translational research
OBJECTIVES: To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions.

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

ESC heart failure
AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes....

Falls Prevention Using AI and Remote Surveillance in Nursing Homes.

Journal of the American Medical Directors Association
The older population of United States is growing, with more adults having complicated medical conditions being admitted into nursing facilities and assisted living facilities. With the COVID-19 pandemic, the biggest challenge has been falls preventio...

Healthcare Violence and the Potential Promises and Harms of Artificial Intelligence.

Journal of patient safety
Currently, the healthcare workplace is one of the most dangerous in the United States. Over a 3-month period in 2022, two nurses were assaulted every hour. Artificial intelligence (AI) has the potential to prevent workplace violence by developing uni...

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis.

Scientific reports
Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and...

Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
INTRODUCTION: People with cystic fibrosis (PwCF) experience frequent symptoms associated with chronic lung disease. A complication of CF is a pulmonary exacerbation (PEx), which is often preceded by an increase in symptoms and a decline in lung funct...

Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Journal of substance use and addiction treatment
BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-leve...

Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.

American journal of obstetrics & gynecology MFM
BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients w...