AIMC Topic: Machine Learning
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Flow and heat transfer of Poly Dispersed SiO2 Nanoparticles in Aqueous Glycerol in a Horizontal pipe: Application of ensemble and evolutionary machine learning for model-prediction.
PloS one
Jun 3, 2025
Stable nanofluid dispersion with SiO2 particles of 15, 50, and 100 nm is generated in a base liquid composed of water and glycerol in a 7:3 ratio and tested for physical characteristics in the temperature range of 20-100oC. The nanofluid showed excel...
A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.
Renal failure
Jun 2, 2025
Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable outcomes. This research aimed to develop a machine learning-based model for early SAD prediction in ...
Machine Learning-Assisted Prediction of Persistent Incomplete Occlusion in Intracranial Aneurysms From Angiographic Parametric Imaging-Derived Features.
Academic radiology
Jun 2, 2025
RATIONALE AND OBJECTIVES: To develop machine-learning (ML) models incorporating angiographic parametric imaging (API)-derived parameters in predicting persistent incomplete occlusion of intracranial aneurysms (IAs) after flow diverter (FD) treatment.
Decision support using machine learning for predicting adequate bladder filling in prostate radiotherapy: a feasibility study.
Radiological physics and technology
Jun 2, 2025
This study aimed to develop a model for predicting the bladder volume ratio between daily CBCT and CT to determine adequate bladder filling in patients undergoing treatment for prostate cancer with external beam radiation therapy (EBRT). The model wa...
Artificial intelligence in prediction of postpartum hemorrhage: a primer and review.
International journal of obstetric anesthesia
Jun 2, 2025
Postpartum hemorrhage (PPH) is a leading cause of maternal mortality worldwide, and the ability to predict PPH may help address preventable causes of morbidity and mortality such as delays in care. Understanding the importance of standardized approac...
Multi-view based heterogeneous graph contrastive learning for drug-target interaction prediction.
Journal of biomedical informatics
Jun 2, 2025
Drug-Target Interaction (DTI) prediction plays a pivotal role in accelerating drug discovery and development by identifying novel interactions between drugs and targets. Most previous studies on Drug-Protein Pair (DPP) networks have primarily focused...
READRetro web: A user-friendly platform for predicting plant natural product biosynthesis.
Molecules and cells
Jun 2, 2025
Natural products (NPs), a fundamental class of bioactive molecules with broad applicability, are valuable sources in pharmaceutical research and drug discovery. Despite their significance, the large-scale production of NPs is often limited by their a...
Machine learning-enhanced SERS diagnostics: Accelerating the AI-powered transition from laboratory discoveries to clinical practice.
Computers in biology and medicine
Jun 2, 2025
Surface-enhanced Raman spectroscopy (SERS) has emerged as a transformative analytical tool in disease diagnostics, offering unparalleled sensitivity and molecular fingerprinting capabilities. However, its clinical translation is hindered by the inher...
Comparative analysis of SWAT and SWAT coupled with XGBoost model using Optuna hyperparameter optimization for nutrient simulation: A case study in the Upper Nan River basin, Thailand.
Journal of environmental management
Jun 2, 2025
Agricultural runoff leading to nitrate (NO-N) and orthophosphate (PO-P) contamination poses significant environmental and public health risks. This study integrates the Soil and Water Assessment Tool (SWAT) with eXtreme Gradient Boosting (XGBoost), o...
A Machine Learning Trauma Triage Model for Critical Care Transport.
JAMA network open
Jun 2, 2025
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...
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