AIMC Topic: Female

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Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models.

BMC medical informatics and decision making
INTRODUCTION: Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop pro...

A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.

BMC medical informatics and decision making
BACKGROUND: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm fo...

The impact of action descriptions on attribution of moral responsibility towards robots.

Scientific reports
In the era of renewed fascination with AI and robotics, one needs to address questions related to their societal impact, particularly in terms of moral responsibility and intentionality. In seven vignette-based experiments we investigated whether the...

Feasibility of remote robot empowered teleultrasound scanning for radioactive patients.

Scientific reports
To investigate the feasibility of robot-assisted teleultrasound diagnosis for radioactive patients compared with conventional ultrasound diagnosis. In this prospective study (ChineseClinicalTrials.gov identifier, ChiCTR2200057253), 32 radioactive pat...

Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.

Journal of medical systems
Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human...

Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.

Journal of cancer research and clinical oncology
OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to...

AI language model rivals expert ethicist in perceived moral expertise.

Scientific reports
People view AI as possessing expertise across various fields, but the perceived quality of AI-generated moral expertise remains uncertain. Recent work suggests that large language models (LLMs) perform well on tasks designed to assess moral alignment...

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

Scientific reports
Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the...

Applicability of Artificial Intelligence Analysis in Oral Cytopathology: A Pilot Study.

Acta cytologica
INTRODUCTION: Oral cancer, especially oral squamous cell carcinoma (OSCC), is a global health challenge due to factors such as late detection and high mortality rates. Early detection is essential through monitoring by healthcare professionals. Cytop...

Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.

PloS one
BACKGROUND: Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a sign...