AIMC Topic: Humans

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A machine-learning method for predicting the 1-year risk of death in maintenance hemodialysis patients based on continuous compliance with dialysis quality indicators.

BMC nephrology
OBJECTIVE: To establish a 1-year mortality risk prediction model for maintenance hemodialysis (HD) patients using machine learning method based on the continuous assessment methods of dialysis quality indicators.

Strengthening ethics review of the development of artificial intelligence (AI) systems in health research: a guide for research ethics committees in Uganda.

BMC medical ethics
INTRODUCTION: The ability of artificial intelligence (AI) to analyze data in real-time and improve patients' diagnosis has led to a rapid growth of AI- health research in Uganda. Yet, there are no national guidelines on how to conduct AI-research in ...

Development and external validation of an artificial intelligence model for predicting mortality and prolonged ICU stay in postoperative critically ill patients: a retrospective study.

World journal of emergency surgery : WJES
BACKGROUND: Existing predictive models in critical care, specifically for postoperative critically ill patients, often struggle to accurately predict prolonged intensive care unit (ICU) stays, a key aspect of patient care. The integration of artifici...

Identification and validation of aryl hydrocarbon receptor-associated hub genes in ulcerative colitis via integrated bioinformatics analysis.

Human genomics
OBJECTIVE: Ulcerative colitis (UC), a chronic inflammatory bowel disease, continues to pose substantial challenges in both diagnosis and treatment. The aryl hydrocarbon receptor (AhR) plays a pivotal role in intestinal immune regulation; however, its...

The ethics of simplification: balancing patient autonomy, comprehension, and accuracy in AI-generated radiology reports.

BMC medical ethics
BACKGROUND: Large language models (LLMs) such as GPT-4 are increasingly used to simplify radiology reports and improve patient comprehension. However, excessive simplification may undermine informed consent and autonomy by compromising clinical accur...

Machine learning-based stratification of mild cognitive impairment in Parkinson's disease: a multicenter cross-sectional analysis.

BMC medical informatics and decision making
BACKGROUND: Cognitive impairment is a prominent non-motor manifestation of Parkinson's disease (PD) and is associated with reduced quality of life, increased mortality, and higher healthcare utilization. We aimed to develop and externally validate a ...

Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.

BMC oral health
BACKGROUND: The aim of this systematic review is to compare the efficacy of convolutional neural networks (CNN) and Vision Transformers (ViT) in the field of dental imaging, in order to examine in depth the potential, advantages, and limitations of b...

Looking back to move forward: can historical clinical trial data and machine learning drive change in participant recruitment in anticipation of future value assessments?

Trials
Drug development is an expensive endeavor, with costs averaging $879.3 million and only 14.3% of them ultimately securing regulatory approval. One fundamental challenge is ensuring that the enrolled patient population in a clinical trial accurately r...

Identifying subjective life expectancy risk factors in physically active and inactive middle-aged and older adults using machine learning models.

BMC public health
BACKGROUND: Physical activity is a key focus in the field of public health, and subjective life expectancy is closely associated with individuals' physical and psychological well-being. This study aimed to identify the risk factors for subjective lif...

A neuro-fuzzy model for evaluating and predicting computational thinking skills of students.

Scientific reports
Computational thinking skill is an important skill individuals should acquire to meet the requirements of the digital age. The aim of the study is to predict the computational thinking skills of middle school students through ANFIS approach, which is...