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 review synthesizes evidence on a novel hybrid machine learning model leveraging US lung transplant registry data to predict mortality or retransplant risk at 1, 5, and 10 years, highlighting its clinical utility and balanced interpreta
A multicentre deep‑learning model reliably classified biopsy focus score and ACR‑EULAR–defined Sjögren’s disease (AUROC ≈0.88–0.89) and identified a novel CD8+ T‑cell peri‑acinar pattern associated with disease; prospective validation is ne
Comprehensive proteogenomic profiling in resected extrahepatic cholangiocarcinoma reveals predictive biomarkers for tailoring adjuvant chemotherapy, offering a precision medicine approach to improve patient outcomes.
This study applies machine learning and explainable AI to identify factors associated with chronic pain, psychological distress, and their co-occurrence in Norwegian young adults, highlighting the importance of integrated approaches for ear
This study developed and validated machine learning models with high accuracy to predict early pediatric sepsis and septic shock within 48 hours using multisite electronic health record data in emergency departments.
Integrated radiomic-clinical machine learning models outperform traditional clinical biomarkers in predicting survival and treatment response in unresectable hepatocellular carcinoma patients undergoing atezolizumab plus bevacizumab therapy
A machine learning model accurately stratifies patients with hepatocellular carcinoma to guide individualized selection between liver transplantation and surgical resection, improving survival outcomes while addressing donor scarcity and pa
Machine learning models trained on clinical data improve prediction of coronary artery disease on CT scans compared to traditional risk scores, potentially guiding better resource utilization in cardiovascular care.
A new cell-free DNA fragmentomics model combined with machine learning achieves high accuracy for early pancreatic cancer detection, potentially improving patient outcomes through timely intervention.
A prospective study shows that machine learning models trained on 12-lead ECG features outperform HEART score in predicting mortality among chest pain patients, improving risk-based care precision.
A machine learning model using nationwide registry data accurately predicts long-term remission in refractory ulcerative colitis and identifies key prognostic factors such as pseudopolyps.