Results 241 to 250 of about 5,693,469 (294)

Machine Learning Model for Predicting Hepatocellular Carcinoma Development in Patients With Hepatitis B Virus‐Related Cirrhosis Receiving Antiviral Therapy

The Kaohsiung Journal of Medical Sciences, EarlyView.
ABSTRACT Few prediction models have been specifically designed to evaluate the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B and cirrhosis. This study aimed to develop a machine learning‐based prediction model to assess the risk of developing HCC in patients with hepatitis B virus (HBV)‐related cirrhosis undergoing nucleos(
Pao‐Yuan Huang, Heng‐Syu Lin, Cheng‐Yuan Peng, Ruei‐Hau Hsu, Chien‐Hung Chen   +4 more
wiley   +1 more source

Fewer Injections and Lower Drug Costs With Long-Acting GnRH Depot Formulations: A Nationwide Analysis of 2.3 Million Patient-Years in Japan. [PDF]

Cancer Rep (Hoboken)
Kawahara T, Hashizume A, Yamazaki M, Takamoto D, Takeshima T, Uemura H, Makiyama K, Teranishi JI.   +7 more
europepmc   +1 more source

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani, Rüdiger von Eisenhart‐Rothe, Klaus Woertler, Michael T. Hirschmann, Daniel Rueckert, Florian Hinterwimmer   +5 more
wiley   +1 more source

Relative importance analysis of correlated predictors in aquatic veterinary science: Application to an abalone dataset. [PDF]

Open Vet J
Masaoud E, Sirtiyah A, Abdulqadir M, Mohamed A, Khapoli A, Alshaybani K.   +5 more
europepmc   +1 more source

Machine Learning‐Based Risk Stratification Tool for Hearing Loss in High‐Risk Neonates

The Laryngoscope, EarlyView.
Machine learning models, particularly XGBoost, provide robust risk stratification for neonatal hearing loss by capturing complex interactions among clinical risk factors such as NICU stay duration and family history. To translate these predictive capabilities into routine practice, an open‐access web‐based clinical decision support tool was developed ...
Sevgi Kutlu, Emre Söylemez, Betül Temir, Esranur Ulusoy, Zahide Çiler Büyükatalay   +4 more
wiley   +1 more source

Data-Driven Modeling of Friction in Drawbead Test Through Advanced Machine Learning. [PDF]

Materials (Basel)
Trzepieciński T, Fejkiel R, Kowalik M.
europepmc   +1 more source

AI-ECG Risk Stratification for Atrial Fibrillation: Real-World Performance and Explainability. [PDF]

JACC Adv
Matsuo K, Sobue Y, Miyake T, Takeda K, Watanabe E, Matsuo H, Izawa H.   +6 more
europepmc   +1 more source

XGBoost prediction of adverse neurodevelopmental outcomes in hypoxic-ischemic encephalopathy neonates. [PDF]

Medicine (Baltimore)
Kim TY, Park HM, Youn YA.
europepmc   +1 more source

Forecasting the risk of early death among patients suffering from lung cancer with brain metastasis after radiotherapy using interpretable machine learning: a study based on the SEER database. [PDF]

J Thorac Dis
Song R, Zhang Z, Huo X, Qin C, Cao J, Zhang S.   +5 more
europepmc   +1 more source

Identification of collagen features predictive of recurrence following radiotherapy for localised prostate cancer: a retrospective case control analysis

Jenkins RP, Fu X, Waise S, Dewan M, Griffin C, Stuttle C, Cruickshank C, Dearnaley D, Syndikus I, Hall E, Sahai E, Wilkins A.   +11 more
europepmc   +1 more source