8.473 hasil untuk machine learning
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A machine learning model for early candidemia prediction in the intensive care unit: Clinical application
…in the intensive care unit (ICU). This study aims to develop a machine learning predictive model for early candidemia diagnosis in ICU patients, leveraging their clinical information and findings. We conducted this study with a cohort of 334 patients admitted to the ICU unit at Ji Ning NO.1…
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Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning
…or extracorporeal membrane oxygenation), and death within 30 days. Machine learning models demonstrated that advanced age, high comorbidity burden, lower body mass index, unvaccinated status, and oral anticoagulant use were the important predictors of hospitalization and escalation of care. Similar…
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Comparing fatal crash risk factors by age and crash type by using machine learning techniques
This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where…
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Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study
Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status…
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An intrusion detection model to detect zero-day attacks in unseen data using machine learning
In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying…
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Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks
…explicit understanding “who do they infect?” Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually…
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Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network
…transport in metabolic networks. To overcome this, a Network-based Machine Learning framework was proposed. It assessed various network properties in Plasmodium falciparum, using a Genome-Scale Metabolic Model (iAM_Pf480) from the BiGG database and essentiality data from the Ogee database. The…
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Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques
…basil. In this study, we employed hyperspectral imaging combined with machine learning techniques to determine the levels of total phenolic contents in Thai holy basil. Spectral data were acquired from 26 holy basil cultivars at different growth stages, and the total phenolic contents of the…
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Post-concussion symptom burden and dynamics: Insights from a digital health intervention and machine learning
…of post-concussion symptomatology by applying an unsupervised machine learning approach to data captured from a digital health intervention (HeadOn). As part of the 35-day program, patients complete a daily symptom diary which rates 8 post-concussion symptoms. Symptom data were analysed using…
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Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning
…biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum from PCOS patients and non-PCOS subjects were collected for untargeted lipidomics analysis. Through analyzing the classification of differential lipid metabolites and the association between differential…
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Identification of O-glycosylation related genes and subtypes in ulcerative colitis based on machine learning
…SEMA5A were found to be significantly related to O-GlcNAcylation using machine learning methods. Based on the four hub genes, two UC subtypes were built. Notably, subtype B might be prone to developing colitis-associated colorectal cancer (CAC). This study delved into the role of intestinal…
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Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults
…of PONV using the gradient tree boosting model, which is a widely used machine learning algorithm in many applications due to its efficiency and accuracy. The model implementation used the LightGBM framework. Data were available for 33,676 patients. Total blood loss was identified as the strongest…
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Tensile strength analysis of additively manufactured CM 247LC alloy specimen by employing machine learning classifiers
…strength. The accuracy of the tensile strength data classification by machine learning algorithms, such as k-nearest neighbours, Naïve Baiyes, Support vector machine, XGBoost, AdaBoost, Decision tree, Random forest, and logistic regression algorithms, was 92.5%, 83.75%, 83%, 85%, 87.5%, 90%…
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An ensemble classification method based on machine learning models for malicious Uniform Resource Locators (URL)
…ensemble classifier aims to take advantage of the performance of each machine learning model and aggregate the output to improve prediction accuracy. The classification accuracies of the machine learning model when applied individually are 93.6%, 95.2%, 95.7% and 94.8% for random forest, XGBoost…
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A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China
Purpose: To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions. Methods: We collected 13,245 written judgments from PKULAW.COM, a public database…
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CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression
…biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving…
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Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review
Aim: In this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults. Methods: We searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date…
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Research on intelligent decision support systems for oil and gas exploration based on machine learning
The process of extracting oil and gas via borehole drilling is largely dependent on subsurface structures, and thus, well log analysis is a major concern for economic feasibility. Well logs are essential for understanding the geology below the earth’s surface, which allows for the estimation of the…
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Machine learning and game theory for cyber governance: Enhancing public opinion and regional sustainable development
…managing public opinion, and influencing people’s behavior, based on machine learning and game theory approaches. Our framework leverages deep learning techniques to analyze public opinion, active learning methods to reduce costs, and game theory to make optimal management decisions. We verify the…
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