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Detecting low birth weight is crucial for early identification of at-risk pregnancies which are associated with significant neonatal and maternal morbidity and mortality risks. This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios.
View Article and Find Full Text PDFSpatial Variation in the Association between Extreme Heat Events and Warm Season Pediatric Acute Care Utilization: A Small-Area Assessment of Multiple Health Conditions and Environmental Justice Implications in California (2005-2019).
Environ Health Perspect
January 2025
Scripps Institution of Oceanography, San Diego, California, USA.
Background: The increasing frequency and severity of extreme heat events due to climate change present unique risks to children and adolescents. There is a lack of evidence regarding how heat's impacts on pediatric patients vary spatially and how structural and sociodemographic factors drive this heterogeneity.
Objectives: We examined the association between extreme heat events and pediatric acute care utilization in California for 19 distinct health conditions.
Introduction: Metabolic and bariatric surgery (MBS) is increasingly used for obesity and metabolic disease, with safety profiles showing it is among the safest major operations. The last 20 + years have noted significantly improved safety that has been accompanied by decreasing length of stay and select populations electing for outpatient surgery, leading to continued decreases in cost. Regardless, readmissions and complications still occur, requiring inpatient postoperative care (IP-POC).
View Article and Find Full Text PDFDevelopment and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke.
Front Neurol
January 2025
Department of Neurology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Objective: To develop and validate an explainable machine learning (ML) model predicting the risk of hemorrhagic transformation (HT) after intravenous thrombolysis.
Methods: We retrospectively enrolled patients who received intravenous tissue plasminogen activator (IV-tPA) thrombolysis within 4.5 h after symptom onset to form the original modeling cohort.
Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.
Front Artif Intell
January 2025
Department of Rehabilitation Medicine, The First Affiliated Hospital of Shenzhen University, The Second People's Hospital of Shenzhen, Shenzhen, Guangdong, China.
Background: The Department of Rehabilitation Medicine is key to improving patients' quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models.
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