AIMC Topic: Humans

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Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission.

Scientific reports
Scientists aim to create a system that can predict the likelihood of newborns being admitted to the neonatal intensive care unit (NICU) by combining various statistical methods. This prediction could potentially reduce the negative health outcomes, d...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Scientific reports
Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely detection and intervention are crucial in preventing severe health outcomes. In this study, we implemen...

An advanced fire detection system for assisting visually challenged people using recurrent neural network and sea-horse optimizer algorithm.

Scientific reports
The developing elderly population undergoes a high level of eyesight and mental impairment, which frequently results in a defeat of independence. That kind of person should do vital daily tasks like heating and cooking, with methods and devices inten...

CFM-UNet: coupling local and global feature extraction networks for medical image segmentation.

Scientific reports
In medical image segmentation, traditional CNN-based models excel at extracting local features but have limitations in capturing global features. Conversely, Mamba, a novel network framework, effectively captures long-range feature dependencies and e...

Regulatory T cells and matrix-producing cancer associated fibroblasts contribute on the immune resistance and progression of prognosis related tumor subtypes in ccRCC.

Scientific reports
Clear cell renal cell carcinoma (ccRCC) is a prevalent malignant tumor in the field of urology. The effect of cell heterogeneity on the prognosis and reaction to treatment of ccRCC in large populations is still unclear. By analyzing public single cel...

Comparison of physician and large language model chatbot responses to online ear, nose, and throat inquiries.

Scientific reports
Large language models (LLMs) can potentially enhance the accessibility and quality of medical information. This study evaluates the reliability and quality of responses generated by ChatGPT-4, an LLM-driven chatbot, compared to those written by physi...

Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.

Scientific reports
Effective treatment for brain tumors relies on accurate detection because this is a crucial health condition. Medical imaging plays a pivotal role in improving tumor detection and diagnosis in the early stage. This study presents two approaches to th...

AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.

Scientific reports
Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and treatment failures leading to increased morbidity and mortality. This study introduces an explainab...

Deep learning for occupation recognition and knowledge discovery in rheumatology clinical notes.

Scientific reports
Occupational data is a crucial social determinant of health, influencing diagnostic accuracy, treatment strategies, and policy-making in healthcare. However, its inclusion in electronic health records (EHR) is often relegated to unstructured fields. ...

Identification of potential pathogenic genes associated with the comorbidity of rheumatoid arthritis and renal fibrosis using bioinformatics and machine learning.

Scientific reports
This study aimed to identify the potential pathogenic genes associated with the comorbidity of rheumatoid arthritis (RA) and renal fibrosis (RF). Transcriptomic data related to RA and RF were retrieved from the GEO database. Differential expression g...