Severity: Warning
Message: fopen(/var/lib/php/sessions/ci_sessionbko9o77lkobn6fu5oanf7onvjvr2ij18): Failed to open stream: No space left on device
Filename: drivers/Session_files_driver.php
Line Number: 177
Backtrace:
File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)
Filename: Session/Session.php
Line Number: 137
Backtrace:
File: /var/www/html/index.php
Line: 316
Function: require_once
Objective: To improve the estimation of healthcare expenditures by introducing a novel method that is well-suited to situations where data exhibit strong skewness and zero-inflation.
Data Sources: Simulations, and two real-world datasets: the 2016-2017 Medical Expenditure Panel Survey (MEPS); the Back Pain Outcomes using Longitudinal Data (BOLD).
Study Design: Super learner is an ensemble machine learning approach that can combine several algorithms to improve estimation. We propose a two-stage super learner that is well suited for healthcare expenditure data by separately estimating the probability of any healthcare expenditure and the mean amount of healthcare expenditure conditional on having healthcare expenditures. These estimates can then be combined to yield a single estimate of expenditures for each observation. The analytical strategy can flexibly incorporate a range of individual estimation approaches for each stage of estimation, including both regression-based approaches and machine learning algorithms such as random forests. We compare the performance of the two-stage super learner with a one-stage super learner, and with multiple individual algorithms for estimation of healthcare cost under a broad range of data settings in simulated and real data. The predictive performance was compared using Mean Squared Error and R.
Conclusions: Our results indicate that the two-stage super learner has better performance compared with a one-stage super learner and individual algorithms, for healthcare cost estimation under a wide variety of settings in simulations and in empirical analyses. The improvement of the two-stage super learner over the one-stage super learner was particularly evident in settings when zero-inflation is high.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683480 | PMC |
http://dx.doi.org/10.1007/s10742-022-00275-x | DOI Listing |
Osteoporos Int
March 2025
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, USA.
Unlabelled: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AUC = 0.76, accuracy = 95.
View Article and Find Full Text PDFFront Med (Lausanne)
February 2025
Division of Molecular Nutrition, Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Introduction: Achieving remission is a critical therapeutic goal in the management of rheumatoid arthritis (RA). Despite methotrexate being the cornerstone of early RA treatment, a significant proportion of patients fail to achieve remission. This study aims to predict 6-month non-remission in 222 disease-modifying anti-rheumatic drug (DMARD)-naïve RA patients initiating methotrexate monotherapy, using baseline patient characteristics from the ARCTIC trial.
View Article and Find Full Text PDFJ Chem Inf Model
March 2025
Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife 50670-465, Brazil.
Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to biotechnology, such as the development of therapeutic monoclonal antibodies, the engineering of enzymes for industrial biocatalysis, the development of biosensors for disease detection, and the assembly of artificial protein complexes for drug screening. Therefore, predicting the strength of their association allows for understanding the molecular mechanisms and ultimately controlling them.
View Article and Find Full Text PDFIntensive Care Med
February 2025
Program for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, USA.
Rationale: Most cases of acute kidney injury (AKI) resolve within 72 h. However, a small number of patients with persistent severe AKI have significantly worse outcomes. We sought to describe the occurrence, impact on outcome and risk factors associated with persistent severe AKI in critically ill patients using a standardized definition.
View Article and Find Full Text PDFPLoS Negl Trop Dis
February 2025
Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, California, United States of America.
Background: Water, sanitation, hygiene (WSH), nutrition (N), and combined (N+WSH) interventions are often implemented by global health organizations, but WSH interventions may insufficiently reduce pathogen exposure, and nutrition interventions may be modified by environmental enteric dysfunction (EED), a condition of increased intestinal permeability and inflammation. This study investigated the heterogeneity of these treatments' effects based on individual pathogen and EED biomarker status with respect to child linear growth.
Methods: We applied cross-validated targeted maximum likelihood estimation and super learner ensemble machine learning to assess the conditional treatment effects in subgroups defined by biomarker and pathogen status.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!