8.473 hasil untuk machine learning
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Applications of machine learning in decision analysis for dose management for dofetilide
Background: Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of toxicity within this time period. Although a recommended dose management algorithm for dofetilide exists, there is a range of real-world approaches to…
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Predicting breast cancer risk using personal health data and machine learning models
…from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and…
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Machine learning models for identifying preterm infants at risk of cerebral hemorrhage
…impaired cerebral autoregulation, infections, and coagulation disorders. Machine learning methods allow the identification of combinations of clinical factors to best differentiate preterm infants with intra-cerebral bleeding and the development of models for patients at risk of cerebral…
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A validation of machine learning-based risk scores in the prehospital setting
…differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data. Methods: Dispatch, ambulance, and hospital data were collected in one Swedish region from…
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Correction: Designing machine learning workflows with an application to topological data analysis
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Prediction model development of late-onset preeclampsia using machine learning-based methods
…to its prompt management. This study aimed to develop models using machine learning to predict late-onset preeclampsia using hospital electronic medical record data. The performance of the machine learning based models and models using conventional statistical methods were also compared. A total of…
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A machine learning model of microscopic agglutination test for diagnosis of leptospirosis
…this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected…
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Using machine learning for the personalised prediction of revision endoscopic sinus surgery
…of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots. Results: The logistic regression, gradient boosting and random forest classifiers performed similarly in…
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Evaluation of surgical skill using machine learning with optimal wearable sensor locations
…interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n =…
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Machine learning to predict the source of campylobacteriosis using whole genome data
…MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS…
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Determining jumping performance from a single body-worn accelerometer using machine learning
…sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national…
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Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation
…of the relevant mechanisms is a difficult task. Here, we introduce a new machine learning approach to this problem. It is based on the analysis of trajectories of individual loci in the course of chromosomal segregation obtained by fluorescence microscopy. While machine learning approaches have…
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Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards
…traits from a given morphology is consequently poor. We developed a machine learning model to accurately estimate multivariate performance data from morphology alone by training it on a dataset containing performance and morphology data from 68 lizard species. Our final, stacked model predicts…
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Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach
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Signal-piloted processing and machine learning based efficient power quality disturbances recognition
…the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and…
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Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine
Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end…
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MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis
…MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep learning model to early predict the occurrence of sepsis in an interpretable manner. We show that our model outperforms the current state-of-the-art and present evidence that different labelling heuristics lead to discrepancies…
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Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing
…values from MOD16 product for all Brazil were also downloaded. By using machine learning algorithms and information from WorldClim as covariates, ETo was estimated through images from the MOD16 product. To perform the modeling of ETo, eight regression algorithms were tested: multiple linear…
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Facial geometric feature extraction based emotional expression classification using machine learning algorithms
…used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17% using a Random Forest (RF) classifier compared to other features and classifiers in…
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Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations
Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model…
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