AIMC Topic: Retrospective Studies

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Deep learning for diagnosing osteonecrosis of the femoral head based on magnetic resonance imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Early-stage osteonecrosis of the femoral head (ONFH) can be difficult to detect because of a lack of symptoms. Magnetic resonance imaging (MRI) is sufficiently sensitive to detect ONFH; however, the diagnosis of ONFH require...

Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.

Intensive care medicine
PURPOSE: Due to the increasing demand for intensive care unit (ICU) treatment, and to improve quality and efficiency of care, there is a need for adequate and efficient clinical decision-making. The advancement of artificial intelligence (AI) technol...

Deep Learning-based Recalibration of the CUETO and EORTC Prediction Tools for Recurrence and Progression of Non-muscle-invasive Bladder Cancer.

European urology oncology
Despite being standard tools for decision-making, the European Organisation for Research and Treatment of Cancer (EORTC), European Association of Urology (EAU), and Club Urologico Espanol de Tratamiento Oncologico (CUETO) risk groups provide moderate...

Natural language processing for the surveillance of postoperative venous thromboembolism.

Surgery
BACKGROUND: The objective of this study was to develop a portal natural language processing approach to aid in the identification of postoperative venous thromboembolism events from free-text clinical notes.

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer.

Nature medicine
Machine learning (ML) holds great promise for impacting healthcare delivery; however, to date most methods are tested in 'simulated' environments that cannot recapitulate factors influencing real-world clinical practice. We prospectively deployed and...

Improving Deep Reinforcement Learning With Transitional Variational Autoencoders: A Healthcare Application.

IEEE journal of biomedical and health informatics
Reinforcement learning is a powerful tool for developing personalized treatment regimens from healthcare data. Yet training reinforcement learning agents through direct interactions with patients is often impractical for ethical reasons. One solution...