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Prediction of coronal alignment in robotic-assisted total knee arthroplasty with artificial intelligence.
The Knee
Apr 25, 2025
INTRODUCTION: Robotic-assisted technologies provide the ability to avoid soft tissue release by utilizing more accurate bony cuts during total knee arthroplasty (TKA). However, the ideal limb alignment is not yet established. The aim of this study wa...
An explainable artificial intelligence framework for weaning outcomes prediction using features from electrical impedance tomography.
Computer methods and programs in biomedicine
Apr 25, 2025
BACKGROUND: Prolonged mechanical ventilation (PMV) might cause ventilator-associated pneumonia and diaphragmatic injury, and may lead to worsening clinical weaning outcomes. The present study proposes a comprehensive machine learning (ML) framework f...
A Novel Natural Language Processing Model for Triaging Head and Neck Patient Appointments.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Apr 25, 2025
OBJECTIVE: Inaccurate patient triage contributes to suboptimal clinical capacity management and delays in patient care, which in cancer patients may significantly increase morbidity and mortality. We developed a natural language processing (NLP) mode...
Machine learning-based prediction of bleeding risk in extracorporeal membrane oxygenation patients using transfusion as a surrogate marker.
Transfusion
Apr 25, 2025
BACKGROUND: The increasing use of extracorporeal membrane oxygenation (ECMO) has highlighted challenges in managing bleeding complications. Optimal transfusion strategies remain uncertain for this diverse patient group, necessitating accurate predict...
Machine learning for predicting medical outcomes associated with acute lithium poisoning.
Scientific reports
Apr 25, 2025
The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predicting medical outcomes. This study aimed to evaluate the effectiveness of the random forest algorithm ...
A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy.
Scientific reports
Apr 25, 2025
Ovarian cancer is associated with high rates of patient mortality and morbidity. Laparoscopic assessment of tumor localization can be used for treatment planning in newly diagnosed high-grade serous ovarian carcinoma (HGSOC). While spread to multiple...
Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.
JCO clinical cancer informatics
Apr 25, 2025
PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody ...
Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.
Renal failure
Apr 24, 2025
BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning mo...
Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models.
Journal of medical economics
Apr 24, 2025
BACKGROUND: Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers i...
Effect of AI-based pre-hospital health education via QR code on APAIS scores in patients with breast nodules: A retrospective study.
Breast (Edinburgh, Scotland)
Apr 24, 2025
PURPOSE: To explore the effect of AI-based pre-hospital health education via QR code on preoperative anxiety and information needs in patients with breast nodules and provide a decision-making reference for ongoing optimizing clinical workflows.
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