AIMC Topic: Machine Learning

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Comparing neural language models for medical concept representation and patient trajectory prediction.

Artificial intelligence in medicine
Effective representation of medical concepts is crucial for secondary analyses of electronic health records. Neural language models have shown promise in automatically deriving medical concept representations from clinical data. However, the comparat...

Integration of novel artificial intelligence tools in pediatric urologic practice.

Current opinion in urology
PURPOSE OF REVIEW: There has been an explosion of creative uses of artificial intelligence (AI) in healthcare, with AI being touted as a solution for many problems facing the healthcare system. This review focuses on tools currently available to pedi...

Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury.

Chemical research in toxicology
Drug-induced liver injury (DILI) is a major cause of drug development failures and postmarket drug withdrawals, posing significant challenges to public health and pharmaceutical research. The biological mechanisms leading to DILI are highly complex a...

Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.

Surgical endoscopy
BACKGROUND: Metabolic-bariatric surgery (MBS) is the last effective way to lose weight whom around half of the patients are women of reproductive age. It is recommended an interval of 12 months between surgery and pregnancy to optimize weight loss an...

Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics.

ACS sensors
Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for real-time health monitoring. This review highlights recent advancements in breath-sensing technologies...

Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care.

Journal of translational medicine
BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient...

Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.

Genome biology
BACKGROUND: Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and ...

Genomic selection in pig breeding: comparative analysis of machine learning algorithms.

Genetics, selection, evolution : GSE
BACKGROUND: The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict...

Machine learning-assisted wearable sensing systems for speech recognition and interaction.

Nature communications
The human voice stands out for its rich information transmission capabilities. However, voice communication is susceptible to interference from noisy environments and obstacles. Here, we propose a wearable wireless flexible skin-attached acoustic sen...

Equitable machine learning counteracts ancestral bias in precision medicine.

Nature communications
Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease. Therapeutics and outcomes remain hidden because we lack insights that could be gained from analyzing...