Our website uses essential cookies and, with your consent, additional cookies to measure performance and improve our services. Cookie Policy.
You can change your choice at any time.
Tag
This article reviews a validated machine learning decision-tree model using LC-MS/MS steroid profiles to accurately and rapidly diagnose congenital disorders of adrenal steroidogenesis (CDAS), enhancing clinical decision-making and patient
This study demonstrates that combining quantitative CT biomarkers with clinical data significantly improves the detection of previously undiagnosed COPD within lung cancer screening populations, optimizing referrals for confirmatory spirome
A multicenter study developed a robust exosomal microRNA-based model enabling preoperative detection of occult liver micrometastasis in pancreatic ductal adenocarcinoma, improving risk stratification and guiding treatment sequencing.
A novel AI-driven risk prediction model for ovarian cancer demonstrates high accuracy and potential to improve early detection in screening settings.
This article discusses a novel machine learning-based tool, especially XGBoost, to predict hearing loss risk in high-risk neonates using clinical factors for targeted early intervention.
Weekly well-day capillary ketone testing maintains predictive accuracy for 1-month diabetic ketoacidosis risk, offering a practical and less burdensome monitoring strategy for patients with type 1 diabetes.
A novel machine learning-based model demonstrates improved risk prediction in transthyretin amyloid cardiomyopathy, surpassing traditional staging systems and supporting personalized patient management.
This study introduces a validated machine learning tool providing personalized probability scores to improve cochlear implant candidacy screening, surpassing traditional rules by enabling tailored patient counseling and identifying borderli
This study delineates three unique molecular subtypes of cholangiocarcinoma via integrated multiomic and machine learning methodologies, highlighting TNK1 kinase as a promising targeted therapy in a metabolic subtype.
The PREVUE-VALVE Study demonstrates that AI-ECG models developed in hospital settings show reduced diagnostic accuracy when applied to community populations with lower disease prevalence and milder structural heart disease, underscoring the
Machine learning models can effectively predict major 30-day complications following glossectomy for tongue cancer, improving individualized risk assessment over traditional methods.
A machine learning model using simple clinical tests within 72 hours poststroke accurately predicts 6-month upper-limb motor outcomes, aiding rehabilitation planning.
Advanced MRI connectome analysis identifies disruption of key brain hubs as biomarkers predicting long-term seizure recurrence after temporal lobe epilepsy surgery, improving postoperative risk stratification.
Readmissions following chronic/subacute subdural hematoma hospitalizations are common and mostly nonsurgical, with varied clinical trajectories. Machine learning analysis reveals distinct patient clusters with unique readmission risks, unde
Highlight This unprecedented large-scale multinational study developed a semisupervised contrastive machine learning framework that generates a global
A machine learning model using electronic health records effectively identifies patients at high risk of atrial fibrillation, enabling targeted screening and early diagnosis to prevent stroke.
Machine learning is transforming medical devices but faces challenges like shortcut learning and automation bias. Edge AI and evolving regulation aim to balance innovation with safety.
A machine-learning model using perioperative hemodynamic indices accurately predicted stage 2-3 acute kidney injury after orthotopic heart transplantation in 114 patients, with light gradient boosting machine achieving AUC 0.898.
This study introduces and validates a simplified machine learning-based equation for estimating LDL cholesterol, showing comparable accuracy to the original Martin-Hopkins method and improved ease of use.
Recent research demonstrates that machine learning algorithms can accurately predict patient-reported outcomes one year after breast reconstruction, potentially enhancing shared decision-making and individualized care planning.