8.473 hasil untuk machine learning
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Prediction of Preterm Deliveries from EHG Signals Using Machine Learning
…lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95…
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Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods
The goal of this work is to introduce new metrics to assess risk of Alzheimer's disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and…
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AHaH Computing–From Metastable Switches to Attractors to Machine Learning
…and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of…
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Meaningless comparisons lead to false optimism in medical machine learning
A new trend in medicine is the use of algorithms to analyze big datasets, e.g. using everything your phone measures about you for diagnostics or monitoring. However, these algorithms are commonly compared against weak baselines, which may contribute to excessive optimism. To assess how well an…
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Regularized Machine Learning in the Genetic Prediction of Complex Traits
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Machine Learning Helps Identify CHRONO as a Circadian Clock Component
…candidate clock components. We used a simple form of probabilistic machine learning to integrate biologically relevant, genome-scale data and ranked genes on their similarity to known clock components. We then used a secondary experimental screen to characterize the top candidates. We found that…
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Health system measurement: Harnessing machine learning to advance global health
…the efficiency of health system measurement, we applied unsupervised machine learning methods to assess the performance of the service readiness index (SRI) defined by the World Health Organization and compared it to empirically derived indices. Methods: We drew data from nationally representative…
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Statistical and Machine Learning forecasting methods: Concerns and ways forward
Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to…
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Predicting hospital admission at emergency department triage using machine learning
…usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91). Conclusion: Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information…
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Segmenting accelerometer data from daily life with unsupervised machine learning
…for segmenting and clustering the accelerometer data using unsupervised machine learning. Methods: The data used came from five hundred fourteen-year-old participants from the Millennium cohort study who wore an accelerometer (GENEActiv) on their wrist on one weekday and one weekend day. A Hidden…
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Better medicine through machine learning: What’s real, and what’s artificial?
Machine Learning Special Issue Guest Editors Suchi Saria, Atul Butte, and Aziz Sheikh cut through the hyperbole with an accessible and accurate portrayal of the forefront of machine learning in clinical translation.
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Optimal intensive care outcome prediction over time using machine learning
…models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it…
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Predicting hypertension using machine learning: Findings from Qatar Biobank Study
…of hypertension were constructed and compared using three supervised machine learning algorithms: decision tree, random forest, and logistics regression using 5-fold cross-validation. The performance of algorithms was assessed using accuracy, positive predictive value (PPV), sensitivity, F-measure…
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Machine learning based CRISPR gRNA design for therapeutic exon skipping
…that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice…
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Weighted-persistent-homology-based machine learning for RNA flexibility analysis
…the biomolecular inner topological structures. Recently, a topology-based machine learning model has been proposed. By using the features from persistent homology, this model achieves a remarkable high Pearson correlation coefficient (PCC) in protein B-factor prediction. Motivated by its success…
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A systematic review of fuzzing based on machine learning techniques
…and how to bypass the format verification effectively. Therefore machine learning techniques have been introduced as a new method into fuzz testing to alleviate these challenges. This paper reviews the research progress of using machine learning techniques for fuzz testing in recent years, analyzes…
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Machine learning-based e-commerce platform repurchase customer prediction model
…the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust.
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Predicting mental health problems in adolescence using machine learning techniques
…risk of developing mental health problems. Additionally, the advent of machine learning techniques represents an exciting way to potentially improve upon the standard prediction modelling technique, logistic regression. Therefore, we aimed to I.) develop a model that can predict mental health…
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A machine learning approach for the prediction of pulmonary hypertension
Background: Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating…
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Can machine learning improve patient selection for cardiac resynchronization therapy?
…patients who meet the clinical practice guidelines. Objective: To apply machine learning to create an algorithm that predicts CRT outcome using electronic health record (EHR) data avaible before the procedure. Methods and results: We applied machine learning and natural language processing to the…
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