359.866 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Fast, Continuous Audiogram Estimation using Machine Learning
…estimating a continuous threshold audiogram using Bayesian estimation and machine learning classification. The objective of this study is to assess the accuracy and reliability of this new method relative to a commonly used threshold measurement technique. DESIGN: The authors performed air…
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Introduction to machine learning: k-nearest neighbors
Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The article introduces some basic ideas underlying the kNN algorithm…
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IRB Process Improvements: A Machine Learning Analysis
…analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. RESULTS…
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Epileptic Seizures Prediction Using Machine Learning Methods
…of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two…
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Machine Learning Interface for Medical Image Analysis
TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats…
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Unintended consequences of machine learning in medicine?
Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not…
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Decontaminating eukaryotic genome assemblies with machine learning
…organism. RESULTS: We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of…
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Machine learning reveals orbital interaction in materials
We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies…
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How machine learning is shaping cognitive neuroimaging
…uncover new aspects of cognitive organization. We also give a statistical learning perspective on these progresses and on the remaining gaping holes.
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Skull-Stripping with Machine Learning Deformable Organisms
…these may be easily incorporated into the plan. In addition, we use a machine learning method to make our segmentation more accurate. RESULTS: Our method had the least Hausdorff distance error, but included slightly less brain voxels (false negatives). It also had the lowest false positive error…
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Machine Learning Algorithms Implemented in Image Analysis
…photographs, histological stains, DAB immunohistochemistry, etc. We used machine learning algorithms as implemented in FIJI to isolate specific features in longitudinal retinal photographs of non-human primates. Images acquired over several years with different technologies, cameras and skills were…
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Revisiting Warfarin Dosing Using Machine Learning Techniques
Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which…
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Artikel Jurnal Teks lengkap
Distinguishing Asthma Phenotypes Using Machine Learning Approaches
…phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique—latent class analysis—and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It…
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Machine learning in nephrology: scratching the surface
Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology…
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Machine learning techniques in cardiac risk assessment
BACKGROUND: The objective of this study was to predict the mortality risk of patients during or shortly after cardiac surgery by using machine learning techniques and their learning abilities from collected data. METHODS: The dataset was obtained from Acıbadem Maslak Hospital. Risk factors of the…
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Artikel Jurnal Teks lengkap
Artificial Intelligence/Machine Learning in Diabetes Care
Artificial intelligence/Machine learning (AI/ML) is transforming all spheres of our life, including the healthcare system. Application of AI/ML has a potential to vastly enhance the reach of diabetes care thereby making it more efficient. The huge burden of diabetes cases in India represents a…
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