AIMC Topic: Humans

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Hierarchical embedding attention for overall survival prediction in lung cancer from unstructured EHRs.

BMC medical informatics and decision making
The automated processing of Electronic Health Records (EHRs) poses a significant challenge due to their unstructured nature, rich in valuable, yet disorganized information. Natural Language Processing (NLP), particularly Named Entity Recognition (NER...

Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.

Radiation oncology (London, England)
BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of ...

MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.

Malaria journal
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...

Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.

BMC oral health
BACKGROUND: Artificial intelligence (AI) holds immense potential in revolutionizing restorative dentistry, offering transformative solutions for diagnostic, prognostic, and treatment planning tasks. Traditional restorative dentistry faces challenges ...

Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.

BMC medical informatics and decision making
BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's health and quality of life. Machine learning, an advanced data analysis technique, has shown great poten...

A machine learning-based severity stratification tool for high altitude pulmonary edema.

BMC medical informatics and decision making
This study aimed to identify key predictors for the severity of High Altitude Pulmonary Edema (HAPE) to assist clinicians in promptly recognizing severely affected patients in the emergency department, thereby reducing associated mortality rates. Mul...

A machine learning approach to investigate the role of fear of pain, personal experience, and vicarious learning in dental anxiety.

BMC oral health
BACKGROUND: Dental anxiety is a pervasive problem worldwide, leading to avoidance of dental care, resulting in oral health problems and impacting daily life through social withdrawal and work absenteeism. Addressing this fear is an important public h...

A systematic literature review: exploring the challenges of ensemble model for medical imaging.

BMC medical imaging
BACKGROUND: Medical imaging has been essential and has provided clinicians with useful information about the human body to diagnose various health issues. Early diagnosis of diseases based on medical imaging can mitigate the risk of severe consequenc...

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.

BMC gastroenterology
BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these pa...

Network meta-analysis of intraocular lens power calculation formulas based on artificial intelligence in short eyes.

BMC ophthalmology
PURPOSE: To systematically assess and compare the accuracy of artificial intelligence (AI) -based intraocular lens (IOL) power calculation formulas with traditional IOL formulas in patients with short eye length.