AIMC Topic: Machine Learning

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Synthetic Data and Its Utility in Pathology and Laboratory Medicine.

Laboratory investigation; a journal of technical methods and pathology
In our rapidly expanding landscape of artificial intelligence, synthetic data have become a topic of great promise and also some concern. This review aimed to provide pathologists and laboratory professionals with a primer on the role of synthetic da...

Machine learning for antidepressant treatment selection in depression.

Drug discovery today
Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In th...

Surface-Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Surface-enhanced Raman scattering (SERS) imaging technology faces significant technical bottlenecks in ensuring balanced spatial resolution, preventing image bias induced by substrate heterogeneity, accurate quantitative analysis, and substrate prepa...

Potential prediction and coupling relationship revealing for recovery of platinum group metals from spent auto-exhaust catalysts based on machine learning.

Journal of environmental management
As hazardous waste, the massive generation of spent auto-exhaust catalysts (SACs) puts enormous pressure on environmental management, but provides a rare opportunity for platinum group metals (PGMs) recycling. In this study, machine learning (ML) met...

Topological Learning Approach to Characterizing Biological Membranes.

Journal of chemical information and modeling
Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order pha...

Machine learning in biological physics: From biomolecular prediction to design.

Proceedings of the National Academy of Sciences of the United States of America
Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodo...

Optimal use of β-lactams in neonates: machine learning-based clinical decision support system.

EBioMedicine
BACKGROUND: Accurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in ma...

Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation.

South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
HIV/AIDS remains one of the world's most significant public health and economic challenges, with approximately 36 million people currently living with the disease. Considerable progress has been made to reduce the impact of HIV/AIDS in the past years...

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

Radiation oncology (London, England)
PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictiv...