8,473 results for machine learning
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Predicting congenital syphilis cases: A performance evaluation of different machine learning models
…a role in contributing to its fast spread. In light of this situation, machine learning techniques have been explored to assess the incidence of syphilis and contribute to the epidemiological surveillance in this scenario. Objective: The main goal of this work is to evaluate the performance of…
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Estimating person-specific neural correlates of mental rotation: A machine learning approach
…common across individuals. Here, we propose an individually tailored machine learning approach to identify person-specific patterns of neural activity during mental rotation. We trained ridge regressions to predict the reaction time of correct responses in a mental rotation task using task-related…
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Ultrasound tomography enhancement by signal feature extraction with modular machine learning method
Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of…
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Machine learning-based identification of contrast-enhancement phase of computed tomography scans
…phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined…
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Machine learning-based meta-analysis of colorectal cancer and inflammatory bowel disease
…Here, we address this knowledge gap by presenting a novel multivariate machine-learning strategy to pinpoint genes associated with CRC. Additionally, we applied our analysis pipeline to Inflammatory Bowel Disease (IBD), as IBD patients face substantial CRC risk. The importance of the identified…
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Machine learning based prediction of recurrence after curative resection for rectal cancer
…to rectal cancer recurrence after curative resection using different machine learning techniques. Methods: Consecutive patients who underwent curative surgery for rectal cancer between 2004 and 2018 at Gil Medical Center were included. Patients with stage IV disease, colon cancer, anal cancer…
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Football players’ strength training method using image processing based on machine learning
…on the physical capabilities of football players and to develop a machine learning-based approach for posture recognition. A total of 116 adolescents aged 8 to 13 participating in football training are randomly assigned to either an experimental group (n = 60) or a control group (n = 56). Both…
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A machine learning-enabled open biodata resource inventory from the scientific literature
…using open data from the scientific literature. Specifically, we used a machine learning-enabled natural language processing approach to identify biodata resources from titles and abstracts of life sciences publications contained in Europe PMC. Pretrained BERT (Bidirectional Encoder Representations…
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Automated cell counting for Trypan blue-stained cell cultures using machine learning
…available but remain relatively expensive. Here, we present a machine learning (ML) model based on YOLOv4 that is able to perform cell counts with a high accuracy (>95%) for Trypan blue-stained insect cells. Images of two distinctly different cell lines, Trichoplusia ni (High FiveTM; Hi5 cells) and…
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Link prediction and feature relevance in knowledge networks: A machine learning approach
We propose a supervised machine learning approach to predict partnership formation between universities. We focus on successful joint R&D projects funded by the Horizon 2020 programme in three research domains: Social Sciences and Humanities, Physical and Engineering Sciences, and Life Sciences. We…
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Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum
The objectives of this study were to use machine learning algorithms to establish a model for estimating the evapotranspiration fraction (ETf) using two data input scenarios from the spectral information of the Sentinel-2 constellation, and to analyze the temporal and spatial applicability of the…
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Opportunities for machine learning to predict cross-neutralization in FMDV serotype O
…vaccine selection and disease management. In this study, we developed a machine learning approach to estimate r1 values—an established measure of antigenic similarity—using VP1 sequence data and published virus neutralization titer (VNT) results. Our dataset comprised 108 serum-virus pairs…
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Identifying determinants of readmission and death post-stroke using explainable machine learning
…predictors, which we believe can be achieved using Explainable Machine Learning (XML) models as an expansion of conventional methods. Methods: We evaluated 11 established XML models that represent key ML methodologies to predict 90-day outcomes, namely mortality and rehospitalization among stroke…
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Estimating heterogeneous impacts Of subsidised health insurance: A causal machine learning approach
…effect heterogeneity. In this study, we leverage predictive and causal machine learning to assess the impact of the subsidised component of Indonesia’s National Health Insurance Programme (“JKN”) on healthcare utilisation in 2017. We employ causal forests for estimating heterogeneous treatment…
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Machine learning-based equations for improved body composition estimation in Indian adults
…body composition parameters in a cohort of Indian adults using machine learning techniques. The participants were split into training (80%, 1297 males and 1133 females) and testing (20%, 318 males and 289 females) data to develop and validate the performance of equations for total body fat mass…
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Statistical and machine learning models for predicting university dropout and scholarship impact
Although student dropout is an inevitable aspect of university enrollment, when analyzed, universities can gather information which enables them to take preventative actions that mitigate dropout risk. We study a data set consisting of 4,424 records from a Portuguese higher institution. In this…
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Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences
…to enhance early-stage prediction systems in a substantial manner. Using Machine Learning and Data Science, we exploit protein sequential data from commonly altered genes including BCL2, HSP90, PARP, and RB to make predictions for Chronic Myeloid Leukaemia (CML). The methodology we implement is…
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Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo
…due to its high morbidity and mortality rates. This study aims to develop machine learning (ML) models to classify the aetiology of bacterial meningitis using data from the Notifiable Diseases Information System (SINAN) in São Paulo State, Brazil. Methods: Data were collected from the SINAN…
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Machine learning for the prediction of urosepsis using electronic health record data
…care, reducing diagnostic uncertainty, lowering mortality rates. While machine learning (ML) techniques have the potential to aid healthcare professionals in identifying risk factors and recommending treatment options, no study has been developed to predict the development of urosepsis in patients…
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Assessing regional competitiveness in Peru: An approach using nonlinear machine learning models
…complexity and non-linearity of its determinants’. The development of new Machine Learning (ML) models allows the creation of predictive models capable of handling this type of data, providing actionable insights. The objective of the study was to develop and test the use of non-linear Machine…
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