113.116 hasil untuk machine learning
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Prediction of Intracranial Aneurysm Risk using Machine Learning
…Health Screening Program in Korea were utilized as input for several machine learning algorithms: logistic regression (LR), random forest (RF), scalable tree boosting system (XGB), and deep neural networks (DNN). Algorithm performance was evaluated through the area under the receiver operating…
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Machine Learning Uncovers Food- and Excipient-Drug Interactions
…outcomes. To speed up such discoveries, we apply state-of-the-art machine learning to delineate currently unknown biological effects of inactive ingredients—focusing on P-glycoprotein (P-gp) and uridine diphosphate-glucuronosyltransferase-2B7 (UGT2B7), two proteins that impact the pharmacokinetics…
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Clinical Text Data in Machine Learning: Systematic Review
…its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. OBJECTIVE: The main aim of this study was to provide systematic evidence on the properties of text data used to train machine…
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Context encoding enables machine learning-based quantitative photoacoustics
…of the measurements remains a major challenge. We introduce the first machine learning-based approach to quantitative PA imaging (qPAI), which relies on learning the fluence in a voxel to deduce the corresponding optical absorption. The method encodes relevant information of the measured signal and…
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Applied machine learning and artificial intelligence in rheumatology
Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning…
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Machine Learning for LTE Energy Detection Performance Improvement
…opportunities are based on spectrum sensing. In this paper, two Machine Learning (ML) algorithms, namely k-Nearest Neighbours and Random Forest, have been proposed to increase spectrum sensing performance. These algorithms have been applied to Energy Detection (ED) and Energy Vector-based data (EV)…
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PigLeg: prediction of swine phenotype using machine learning
…using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive…
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GenomeForest: An Ensemble Machine Learning Classifier for Endometriosis
…invasive diagnostic method. In this study, we developed a new ensemble machine learning classifier based on chromosomal partitioning, named GenomeForest and applied it in classifying the endometriosis vs. the control patients using 38 RNA-seq and 80 enrichment-based DNA-methylation (MBD-seq)…
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Machine Learning–Based Deep Phenotyping of Atopic Dermatitis
…The associations of 130 factors with AD severity were analyzed applying a machine learning–gradient boosting approach with cross-validation–based tuning as well as multinomial logistic regression. RESULTS: A total of 367 patients (157 male [42.8%]; mean [SD] age, 39 [17] years; 94% adults) were…
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Collective variable-based enhanced sampling and machine learning
…sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling…
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NeuroCrypt: Machine Learning over Encrypted Distributed Neuroimaging Data
The field of neuroimaging can greatly benefit from building machine learning models to detect and predict diseases, and discover novel biomarkers, but much of the data collected at various organizations and research centers is unable to be shared due to privacy or regulatory concerns (especially…
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adabmDCA: adaptive Boltzmann machine learning for biological sequences
…functional sequences. RESULTS: Our adaptive implementation of Boltzmann machine learning, adabmDCA, can be generally applied to both protein and RNA families and accomplishes several learning set-ups, depending on the complexity of the input data and on the user requirements. The code is fully…
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A survey on missing data in machine learning
Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from…
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Machine Learning for Health: Algorithm Auditing & Quality Control
Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19…
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Diabetic retinopathy classification for supervised machine learning algorithms
BACKGROUND: Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness…
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Expert-enhanced machine learning for cardiac arrhythmia classification
…of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are…
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A Machine Learning Approach to Predicting Diabetes Complications
Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for…
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Machine Learning and Its Application in Skin Cancer
Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of new samples and perform a desired task. Although it…
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Machine Learning: An Overview and Applications in Pharmacogenetics
This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give…
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