Hybrid modelling using simulation and machine learning in healthcare.

Prediction of local scour beneath vibrating submarine pipelines subjected to waves and currents based on CFD and machine learning approach

Identification of Strengthening Factors in Hot-Rolled Dual Phase PM Ti-Fe Alloys by a Machine Learning Approach

A dual layer SIW based microwave sensor for microplastic detection with enhanced sensitivity and machine learning approach

Surface roughness prediction of laser-polished 3D-printed polylactic acid parts: A combined experimental and machine learning approach

Predictive models for dynamic properties of soils using machine learning approaches: A comprehensive review

Spatial heterogeneity and multimodal complementarity in travel behavior resilience under extreme rainfall: A machine learning approach

Prediction and optimization of coal ash flow temperature using machine learning approaches

Flow anomaly detection in harsh industrial environments: A data analytics & machine learning approach

As surgical care becomes more complex, artificial intelligence offers promising opportunities to improve patient outcomes. This study investigated the use of machine learning to predict postoperative complications and the length of hospital stay in patients undergoing lower gastrointestinal surgery. We analyzed the operative electronic health records of 771 patients using Support Vector Machine models. The results indicate that patients' preoperative factors, such as age and the number of diagnoses, are significant predictors of postoperative complications. Precisely, an increased number of diagnoses correlates with increased complications and an elongated length of hospital stay. The Support Vector Machine model achieved a cross-validation accuracy of 75.56% in complication prediction. Integrating predictive models into practice can support early risk identification and targeted care. These findings highlight the potential of artificial intelligence in perioperative care and underscore the need for additional research to refine predictive models in nursing practice.

Investigation of dielectric studies of paracetamol-diethylamine solutions: Experimental and machine learning approach.

Integrative bioinformatics and transcriptomic analysis identifies biomarkers in Polycystic Ovary Syndrome through machine learning approach.

The interplay of urban landscape and PM2.5 exposure disparities: A machine learning approach in Shandong, China

Towards screening of children students with autism spectrum disorder based on executive functions with serious game and machine learning approaches

Exploratory analysis and prognostication of multimorbidity trajectories in middle-aged and elderly populations: A machine learning approach

Capacity estimation of Lithium-ion batteries through a Machine Learning approach

Predicting the magnetic behaviour of CoFeNi high entropy alloys: Reducing experimental dependency through machine learning approaches

Urine-based Raman markers for prostate cancer diagnosis: A machine learning approach using fingerprint and lipid spectral region.

Characterization and pyrolysis kinetic modelling of lignocellulosic waste from rambutan seeds: A machine learning approach

Drivers of firm-level tail dependence: A machine learning approach