AIMC Topic: Female

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System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method.

Neurocritical care
BACKGROUND: A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests usin...

Machine Learning-Driven Modeling to Predict Postdischarge Venous Thromboembolism After Pancreatectomy for Pancreas Cancer.

Annals of surgical oncology
BACKGROUND: Postdischarge venous thromboembolism (pdVTE) is a life-threatening complication following resection for pancreatic cancer (PC). While national guidelines recommend extended chemoprophylaxis for all, adherence is low and ranges from 1.5 to...

T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

Journal of applied clinical medical physics
PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).

AI in medical diagnosis: A contextualised study of patient motivations and concerns.

Social science & medicine (1982)
Patients' reactions to the implementation of Artificial Intelligence (AI) in healthcare range from adverse to favourable. While AI holds the promise of revolutionising healthcare by enhancing, accelerating, and improving the precision of care service...

Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors.

European journal of radiology
BACKGROUND: Automatic segmentation of thymic lesions in preoperative computed tomography (CT) images is crucial for accurate diagnosis but remains time-consuming. Although UNet is widely used in medical imaging, its performance is limited by the inhe...

A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net.

Computers in biology and medicine
Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to ...

Combining machine learning models and rule engines in clinical decision systems: Exploring optimal aggregation methods for vaccine hesitancy prediction.

Computers in biology and medicine
BACKGROUND: With the increasing application of artificial intelligence (AI) technologies in the healthcare sector and the emergence of new solutions, such as large language models, there is a growing need to combine medical knowledge, often expressed...

Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques.

Computers in biology and medicine
OBJECTIVE: Given the significantly increased number of individuals prescribed stimulants in the past decade, there has been growing concern regarding the risk of cardiovascular events among adults on stimulant therapy. We aimed to quantify the added ...