8.473 hasil untuk machine learning
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Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits
…homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population…
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Protein asparagine deamidation prediction based on structures with machine learning methods
…dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated…
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Can machine-learning improve cardiovascular risk prediction using routine clinical data?
…from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods: Prospective cohort…
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Exploration of machine learning techniques in predicting multiple sclerosis disease course
Objective: To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods: 1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used…
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Machine Learning: How Much Does It Tell about Protein Folding Rates?
…from computational fields, which led to the publication of several machine learning-based models to predict the rate of protein folding. Some of them claim to predict the logarithm of protein folding rate with an accuracy greater than 90%. However, there are reasons to believe that such claims are…
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Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
…within or even across different cognitive tasks. In the present study, a machine learning approach was employed to investigate the outlined issue. Ne as well as Pe were extracted from the single-trial EEG signals of participants conducting a flanker and a mental rotation task and subjected to a…
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Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery
…To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the…
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A bioavailable strontium isoscape for Western Europe: A machine learning approach
Strontium isotope ratios (87Sr/86Sr) are gaining considerable interest as a geolocation tool and are now widely applied in archaeology, ecology, and forensic research. However, their application for provenance requires the development of baseline models predicting surficial 87Sr/86Sr variations…
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Predicting urinary tract infections in the emergency department with machine learning
…prediction tools. Objective: Our aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients. Methods: Single-center, multi-site, retrospective cohort analysis of 80,387 adult ED visits with urine culture results and UTI symptoms…
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Machine learning for patient risk stratification for acute respiratory distress syndrome
…suitability for driving clinical interventions. We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant time points throughout a patient’s stay. Methods…
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Regional level influenza study based on Twitter and machine learning method
The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease…
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Predicting one-year outcome in first episode psychosis using machine learning
…in psychosis. Conclusions and relevance: Using advanced statistical machine learning techniques, we provide the first externally validated evidence, in a temporally and geographically independent cohort, for the ability to predict one-year EET status and symptom remission in individual FEP patients.
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Improving preterm newborn identification in low-resource settings with machine learning
…those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. Methods and findings: This study uses data from an ongoing obstetrical cohort…
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Machine learning to predict mesenchymal stem cell efficacy for cartilage repair
…we performed a meta-analysis on MSC therapies for cartilage repair using machine learning. A small database was generated from published in vivo and clinical studies. The unique features of our neural network model in handling missing data and calculating prediction uncertainty enabled precise…
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A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity
Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic…
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New machine learning method for image-based diagnosis of COVID-19
COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest…
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Benchmarking machine learning models on multi-centre eICU critical care dataset
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large…
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Risk mitigation in algorithmic accountability: The role of machine learning copies
Machine learning plays an increasingly important role in our society and economy and is already having an impact on our daily life in many different ways. From several perspectives, machine learning is seen as the new engine of productivity and economic growth. It can increase the business…
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OrganoidTracker: Efficient cell tracking using machine learning and manual error correction
…cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely…
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Identifying intentional injuries among children and adolescents based on Machine Learning
…the feasibility of discriminating the intention of injury based on Machine Learning (ML) modelling and provided ideas for understanding whether there was a misclassification of intentional injuries. Methods: Information entropy was used to determine the correlation between variables and the…
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