214.423 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
An application of machine learning to haematological diagnosis
Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to predict a haematologic disease. One predictive model used all the available blood test parameters and…
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Machine-Learning Techniques Applied to Antibacterial Drug Discovery
…to pre-clinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.
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Machine Learning Techniques for Arterial Pressure Waveform Analysis
…overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the…
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PCP-ML: Protein characterization package for machine learning
BACKGROUND: Machine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction. To speed further development of machine learning based tools and their release to the community, we have developed a package which characterizes several…
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Machine learning applications in cancer prognosis and prediction
…the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex…
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Machine learning approach for pooled DNA sample calibration
…and 1.0, and address a limited set of error types. We propose a novel machine learning method to address the limitations identified. RESULTS: The approach is tested on SNPs genotyped with the Sequenom iPLEX platform and compared to existing state of the art calibration methods. The new method is…
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Investigating Predictors of Cognitive Decline Using Machine Learning
OBJECTIVES: Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer’s disease (AD), to predict cognitive decline. METHODS: Health and…
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Machine learning methods in psychiatry: a brief introduction
Machine learning (ML) techniques have been widely used to address mental health questions. We discuss two main aspects of ML in psychiatry in this paper, that is, supervised learning and unsupervised learning. Examples are used to illustrate how ML has been implemented in recent mental health…
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Machine learning–based identification of hip arthroplasty designs
BACKGROUND: The purposes of this study were to develop a machine learning–based implant recognition program and to verify its accuracy. METHODS: Postoperative anteroposterior (AP) X-rays (≥300 dpi) were collected of patients who underwent total hip arthroplasty. X-rays with a wire or plate added…
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Analyzing collective motion with machine learning and topology
We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D’Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective…
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A machine-learning approach to predict postprandial hypoglycemia
…is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. METHODS: We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day…
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ContraDRG: Automatic Partial Charge Prediction by Machine Learning
In recent years, machine learning techniques have been widely used in biomedical research to predict unseen data based on models trained on experimentally derived data. In the current study, we used machine learning algorithms to emulate computationally complex predictions in a reverse…
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Harnessing networks and machine learning in neuropsychiatric care
…has been supported by these study designs, along with methods bridging machine learning and network science. Such methods combine analytic power, interpretability, and direct connection to underlying theory in cognitive neuroscience. Ultimately, we propose a general framework for the treatment of…
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MACHINE LEARNING APPROACHES TO ENHANCE CLAIMS DATA ANALYSES
This presentation will cover recent advances in machine learning applied to large claims databases involving medical disparities. First, we will describe methods involving the enrichment of existing claims data with social determinants of health from census data, where variables are imputed from…
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Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning
…surfaces, management records, and remotely sensed data. Using three machine learning methods, we compared models of forest productivity and evaluate the gain and Shapley values for interpreting the influence of categorical variables on the power of these methods to predict forest productivity at a…
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Advanced machine learning methods in psychiatry: an introduction
Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods…
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