We present sparse identification of nonlinear dynamics with shallow recurrent decoders (SINDy-SHRED), which jointly solves the sensing, model reduction and model identification problem with simple ...

EHR validation in 406,230 “relaxed” and 127,151 “strict” patients showed stable PREVENT discrimination across sexes, indicating resilience to real-world data incompleteness. Median race- and ...

The patients with intrahepatic cholangiocarcinoma (iCCA) are highly susceptible to recurrence after radical resection, while predicting recurrence remains challenging. Adjacent-to-tumor tissues (ATTs) ...

Patients with HER2-positive breast cancer in the FLAMINGO-01 phase 3 clinical trial are seeing a continued reduction in recurrence rates, Greenwich LifeSciences, Inc. has reported. The preliminary ...

An artificial intelligence model that incorporates a range of tumor and patient data proved significantly more accurate in retrospectively predicting breast cancer recurrence than a commonly used ...

At the San Antonio Breast Cancer Symposium, researchers presented findings on Clarity BCR, a multimodal multitask deep-learning algorithm that aims to estimate late distant recurrence risk in hormone ...

Please provide your email address to receive an email when new articles are posted on . A novel AI multimodal model outperformed Oncotype DX in overall distant recurrence rate at 15 years. Additional ...

This study aimed to develop a machine learning–based model to predict recurrence risk after perianal abscess surgery, thereby supporting personalized follow-up and intervention strategies. Clinical ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Artificial intelligence (AI) systems, particularly artificial neural networks, have proved to be highly promising tools for uncovering patterns in large amounts of data that would otherwise be ...