AIMC Topic: Female

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Unraveling Uncertainty: The Impact of Biological and Analytical Variation on the Prediction Uncertainty of Categorical Prediction Models.

The journal of applied laboratory medicine
BACKGROUND: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study d...

Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.

European heart journal
BACKGROUND AND AIMS: Robust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool ...

Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores From Colonoscopy Reports.

Inflammatory bowel diseases
BACKGROUND: The Mayo endoscopic subscore (MES) is an important quantitative measure of disease activity in ulcerative colitis. Colonoscopy reports in routine clinical care usually characterize ulcerative colitis disease activity using free text descr...

Exploring Ovarian Cancer Prediction Models and Potential Markers Using Machine Learning.

Annals of clinical and laboratory science
OBJECTIVE: To develop machine learning models, facilitate a more accurate diagnosis of ovarian cancer (OC), and explore potential markers.

Improving Quality of Life of Families Headed by Parents With Intellectual Disabilities and Their Children by Means of Assistive Social Robotics.

Journal of applied research in intellectual disabilities : JARID
BACKGROUND: Families of parents with intellectual disabilities still face discrimination, stigma and inadequate support, placing them in vulnerable positions. Social assistive robotics offers promising support. This study investigates the possible im...

Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation.

Radiology
Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent examinations like chest radiograph interpretation. Purpose To develop and evaluate the diagnostic accur...

A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.

Journal of cellular and molecular medicine
Opportunistic infections (OIs) are the leading cause of hospitalisation and mortality among Human Immunodeficiency Virus-infected (HIV-infected) patients. The diverse pathogen types and intricate clinical manifestations associated present a formidabl...

Machine Learning Models Based on Stretched-Exponential Diffusion Weighted Imaging to Predict TROP2 Expression in Nude Mouse Breast Cancer Models.

Discovery medicine
BACKGROUND: Trophoblast cell surface antigen 2 (TROP2) is a promising target for various cancers, including breast cancer. The development of noninvasive techniques for assessing TROP2 expression in tumors holds considerable importance. This study ai...

Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.

Alzheimer's & dementia : the journal of the Alzheimer's Association
BACKGROUND: Integrating non-invasive measures to estimate abnormal amyloid beta accumulation (Aβ+) is key to developing a screening tool for preclinical Alzheimer's disease (AD). The predictive capability of standard neuropsychological tests in estim...

Leveraging Artificial Intelligence and Radiomics for Improved Nasopharyngeal Carcinoma Prognostication.

Cancer medicine
INTRODUCTION: Nasopharyngeal carcinoma (NPC) typically presents as advanced disease due to the lack of significant symptoms in the early stages. Accurate prognostication is therefore challenging as current methods based on anatomical staging often la...