AIMC Topic: Machine Learning

Clear Filters Showing 6591 to 6600 of 34417 articles

Fine-Tuning on AI-Driven Video Analysis through Machine Learning: Development of an Automated Evaluation Tool of Facial Palsy.

Plastic and reconstructive surgery
BACKGROUND: Establishment of a quantitative, objective evaluation tool for facial palsy has been a challenging issue for clinicians and researchers, and artificial intelligence-driven video analysis can be considered a reasonable solution. The author...

Accurate prediction of pollution and health risks of iodinated X-ray contrast media in Taihu Lake with machine learning and revealing key environmental factors.

Water research
Iodinated X-ray contrast media (ICM) are commonly detected at considerable concentrations in aquatic environments. The long-term pollution trends in ICM at the whole lake/river scale have not yet been investigated; therefore, the risks associated wit...

AI-Driven Drug Discovery for Rare Diseases.

Journal of chemical information and modeling
Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, m...

Unmasking the chameleons: A benchmark for out-of-distribution detection in medical tabular data.

International journal of medical informatics
BACKGROUND: Machine Learning (ML) models often struggle to generalize effectively to data that deviates from the training distribution. This raises significant concerns about the reliability of real-world healthcare systems encountering such inputs k...

Immune-based Machine learning Prediction of Diagnosis and Illness State in schizophrenia and bipolar Disorder: How data bias and overfitting were avoided.

Brain, behavior, and immunity
In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comments provide generic advice rather than a specific critique of our methods or findings. Despite raising...

Development and Validation of an Explainable Prediction Model for Postoperative Recurrence in Pediatric Chronic Rhinosinusitis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS).

Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model.

Environmental research
Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) ...

Analysis of high-molecular-weight proteins using MALDI-TOF MS and machine learning for the differentiation of clinically relevant Clostridioides difficile ribotypes.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology
PURPOSE: Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a corre...

Balanced hydropower and ecological benefits in reservoir-river-lake system: An integrated framework with machine learning and game theory.

Journal of environmental management
The negative impacts of large hydroelectric reservoirs on downstream ecosystems have attracted worldwide attention. Few attempts have been made to dynamically predict ecological benefits and rationally negotiation in the reservoir-river-lake (RRL) sy...