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Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.
Applied immunohistochemistry & molecular morphology : AIMM
Mar 27, 2025
Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly...
Machine Learning Models predicting Decompensation in Cirrhosis.
Journal of gastrointestinal and liver diseases : JGLD
Mar 27, 2025
BACKGROUND AND AIMS: Decompensation of cirrhosis significantly decreases survival, thus, prevention of complications is paramount. We used machine learning techniques to identify parameters predicting decompensation.
Exploring the factors influencing the adoption of artificial intelligence technology by university teachers: the mediating role of confidence and AI readiness.
BMC psychology
Mar 27, 2025
OBJECTIVES: This study aims to explore the mediating role of confidence and artificial intelligence (AI) readiness in university teachers' behavioral intention to adopt AI technology, providing empirical support for enhancing teachers' willingness to...
A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.
BMC health services research
Mar 27, 2025
BACKGROUND: The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefo...
Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.
BMC cancer
Mar 27, 2025
BACKGROUND: Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoing chemotherapy, yet existing risk prediction models demonstrate limited accuracy. This study aimed to evaluate the clinical utility of machine learnin...
Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.
BMC pregnancy and childbirth
Mar 27, 2025
Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical processes can provide critical insights into the mechanisms of labor and potentially inform better ...
Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.
BMC medical imaging
Mar 27, 2025
BACKGROUND: To evaluate the performance of a deep learning reconstruction (DLR) based on Adaptive-Compressed sensing (CS)-Network for brain MRI and validate it in a clinical setting.
Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study.
BMC cardiovascular disorders
Mar 27, 2025
BACKGROUND: Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the catastrophic complications of dilated cardiomyopathy (DCM). This study intended to develop a prediction model to predict the risk of ICT in patients ...
Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.
BMC infectious diseases
Mar 27, 2025
BACKGROUND: Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain regions due to immune escape and waning vaccine efficacy. Identifying high-risk patients to mitigate...
Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation.
Scientific reports
Mar 27, 2025
The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Additionally, the growing incidence of falls among the elderly, coupled with the urgent need to prevent ...
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