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Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department.
BMC emergency medicine
Jul 15, 2025
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing ...
Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort.
BMC gastroenterology
Jul 15, 2025
OBJECTIVES: This study aimed to develop an accurate prediction model for the risk of Non-alcoholic fatty liver disease (NAFLD) using the random survival forests (RSF), and to investigate the distribution of NAFLD risk with time.
Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients.
Scientific reports
Jul 15, 2025
Following complete mesocolic excision (CME), heart failure (HF) emerges as a significant complication, exerting substantial impacts on both short-term and long-term patient prognoses. The primary objective of our investigation was to develop a machin...
Applying binary mixed model to predict knee osteoarthritis pain.
PloS one
Jul 15, 2025
Data used to understand knee osteoarthritis (KOA) often involves knee-level, rather than person-level information. Failure to account for the correlation between joints within a person may lead to inaccurate inferences. The aim of this study was to d...
Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy.
Renal failure
Jul 14, 2025
OBJECTIVES: Renal biopsy is the gold standard for the diagnosis of glomerular diseases including membranous nephropathy (MN), however, it faces challenges in accuracy, objectivity, and reproducibility of tissue evaluation. This study aims to develop ...
Mortality and antibiotic timing in deep learning-derived surviving sepsis campaign risk groups: a multicenter study.
Critical care (London, England)
Jul 14, 2025
BACKGROUND: The current Surviving Sepsis Campaign (SSC) guidelines provide recommendations on timing of administering antibiotics in sepsis patients based on probability of sepsis and presence of shock. However, there have been minimal efforts to str...
A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study.
World journal of emergency surgery : WJES
Jul 14, 2025
BACKGROUND: Accurately identifying difficult laparoscopic cholecystectomy (DLC) preoperatively remains a clinical challenge. Previous studies utilizing clinical variables or morphological imaging markers have demonstrated suboptimal predictive perfor...
Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability.
BMC medical informatics and decision making
Jul 14, 2025
OBJECTIVES: Timely intervention in shock is vital, as delays over one hour greatly increase mortality. This study aims to develop an enhanced machine learning model that improves predictive performance by utilizing self-controlled data and applying f...
Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study.
BMC medical research methodology
Jul 14, 2025
BACKGROUND: Missing survey data can threaten the validity and generalizability of findings from longitudinal cohort studies. Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data ...
Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.
BMC cancer
Jul 14, 2025
BACKGROUND: Accurately distinguishing the different molecular subtypes of 2021 World Health Organization (WHO) grade 4 Central Nervous System (CNS) gliomas is highly relevant for prognostic stratification and personalized treatment.
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