113.116 hasil untuk machine learning
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Predicting Hypoxia Using Machine Learning: Systematic Review
BACKGROUND: Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration. OBJECTIVE: This systematic…
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BrainAGE: Revisited and reframed machine learning workflow
Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its…
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Detecting unitary synaptic events with machine learning
…Here, we describe an automated approach to detect mEPSCs using a machine learning–based tool. This method, which can be easily generalized to other one-dimensional signals, eliminates inter-observer bias, provides an estimate of its sensitivity and specificity and permits reliable detection of…
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Machine Learning and Health Science Research: Tutorial
Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths…
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Personalized ECG Monitoring and Adaptive Machine Learning
…We review several pECG methods, including anomaly detection and adaptive machine learning (ML), in which learning is performed sequentially as new data are collected. We describe a distributed-network multiscale pECG system to show how the computational load and time associated with adaptive ML…
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Machine learning topological defects in confluent tissues
…defects on experimental data using the trained model. We show that our machine learning model outperforms current defect detection techniques and that this manifests itself in our method as requiring less data to accurately capture defect properties. This could drastically improve the accuracy of…
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Applying Machine Learning Techniques to Implementation Science
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as…
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Fracture Conductivity Prediction Based on Machine Learning
…This research proposes a fracture conductivity prediction model based on machine learning. The main controlling factors of fracture conductivity are determined using the Pearson coefficient method and gray correlation analysis. Example application shows that the R(2) values of the BP neural network…
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Classifying protein kinase conformations with machine learning
…automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing…
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Enhanced Sampling with Machine Learning: A Review
…and requires domain expertise. In recent years, integration of machine learning (ML) techniques in different domains has shown promise, prompting their adoption in enhanced sampling as well. Although ML is often employed in various fields primarily due to its data-driven nature, its integration…
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Complication Prediction after Esophagectomy with Machine Learning
Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who…
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Koina: Democratizing machine learning for proteomics research
Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and…
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Interactive Machine Learning for Laboratory Data Integration
…to a target clinical concept. In this work, we develop an interactive machine learning tool to “extend the reach” of expert laboratory test adjudicators. Our tool provides access to automatic laboratory classification in a user-facing front end that covers all steps in an adjudication workflow, in…
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Detection of honey adulteration using machine learning
…adulterated honey. This research presents a comparative study of machine learning algorithms for detecting adulteration in honey. The study uses hyperspectral imaging, a promising tool for food quality assurance, to classify and predict adulteration in honey. The proposed model relies on…
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Machine learning makes magnificent macromolecules for medicine
At the University of Minnesota, scientists explore the application of machine learning to screen a multiparametric library of polymers to investigate the relationship between polymer attributes, payload type, and biological outcomes to optimize polymeric vector development for delivery of nucleic…
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Classification of Malicious URLs Using Machine Learning
…could be compromised. This study aims to develop models based on machine learning algorithms for the efficient identification and classification of malicious URLs, contributing to enhanced cybersecurity. Within this context, this study leverages support vector machines (SVMs), random forests (RFs)…
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Identifying risk of stillbirth using machine learning
BACKGROUND: Prior predictive models using logistic regression for stillbirth do not leverage the advanced and nuanced techniques involved in sophisticated machine learning methods, such as modeling non-linear relationships between outcomes. OBJECTIVE: To create and refine machine learning models…
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Machine Learning for Human Motion Intention Detection
…sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully…
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