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Extraction and Imputation of Eastern Cooperative Oncology Group Performance Status From Unstructured Oncology Notes Using Language Models.

JCO clinical cancer informatics
PURPOSE: Eastern Cooperative Oncology Group (ECOG) performance status (PS) is a key clinical variable for cancer treatment and research, but it is usually only recorded in unstructured form in the electronic health record. We investigated whether nat...

Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.

Translational vision science & technology
PURPOSE: To develop convolutional neural network (CNN)-based models for predicting the axial length (AL) using color fundus photography (CFP) and explore associated clinical and structural characteristics.

Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

Translational vision science & technology
PURPOSE: The purpose of this study was to investigate the development of optical biometric components in children with hyperopia, and apply a machine-learning model to predict axial length.

Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.

Cancer medicine
BACKGROUND: Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitizatio...

PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.

Translational vision science & technology
PURPOSE: We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness.

Machine learning reveals ferroptosis features and a novel ferroptosis classifier in patients with sepsis.

Immunity, inflammation and disease
OBJECTIVE: Sepsis is an organ malfunction disease that may become fatal and is commonly accompanied by severe complications such as multiorgan dysfunction. Patients who are already hospitalized have a high risk of death due to sepsis. Even though ear...

Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

Age and ageing
INTRODUCTION: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop...

Prediction of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Artificial Intelligence Techniques.

Translational vision science & technology
PURPOSE: This study aimed to develop artificial intelligence models for predicting postoperative functional outcomes in patients with rhegmatogenous retinal detachment (RRD).