AIMC Topic: Retrospective Studies
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Using deep learning for estimation of time-since-injury in pediatric accidental fractures.
Pediatric radiology
Apr 22, 2025
BACKGROUND: Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evaluated at non-children's hospitals, yet pediatric radiologists can disagree with up to one in six skele...
Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator.
World journal of surgical oncology
Apr 22, 2025
BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk predi...
Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a prediction model: a retrospective study.
BMC musculoskeletal disorders
Apr 22, 2025
PURPOSE: The wedge effect (V-effect) is a common complication in intramedullary nailing surgery for intertrochanteric fractures and can significantly affect postoperative outcomes. The purpose of this study was to screen risk factors for the intraope...
Artificial intelligence based assessment of clinical reasoning documentation: an observational study of the impact of the clinical learning environment on resident documentation quality.
BMC medical education
Apr 22, 2025
BACKGROUND: Objective measures and large datasets are needed to determine aspects of the Clinical Learning Environment (CLE) impacting the essential skill of clinical reasoning documentation. Artificial Intelligence (AI) offers a solution. Here, the ...
Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.
BMC nephrology
Apr 22, 2025
BACKGROUND: Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learning approaches offer promising solutions for risk prediction, their clinical application often lacks ...
Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning.
BMC infectious diseases
Apr 22, 2025
INTRODUCTION: Sepsis is a life-threatening condition characterized by widespread inflammatory response syndrome in the body resulting from infection. Previous studies have demonstrated that some inflammatory factors or nutritional elements contribute...
The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.
Scientific reports
Apr 22, 2025
Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer's disease. We previously develop...
Evaluating the impact of explainable AI on clinicians' decision-making: A study on ICU length of stay prediction.
International journal of medical informatics
Apr 21, 2025
BACKGROUND: Explainable Artificial Intelligence (XAI) is increasingly vital in healthcare, where clinicians need to understand and trust AI-generated recommendations. However, the impact of AI model explanations on clinical decision-making remains in...
SMOTE-Enhanced Explainable Artificial Intelligence Model for Predicting Visual Field Progression in Myopic Normal Tension Glaucoma.
Journal of glaucoma
Apr 21, 2025
PRCIS: The AI model, enhanced by SMOTE to balance data classes, accurately predicted visual field deterioration in patients with myopic normal tension glaucoma. Using SHAP analysis, the key variables driving disease progression were identified.
Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Apr 21, 2025
BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.
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