214.423 hasil untuk machine learning
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Machine Learning for Benchmarking Critical Care Outcomes
…The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative…
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Predicting ward transfer mortality with machine learning
…of admission, the second set is more clinically relevant. We trained 16 machine learning models using both datasets. The best-performing models were fine-tuned and evaluated. The LightGBM model achieved the best results for both datasets. The model trained with 22 variables achieved a Receiver…
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Machine Learning assisted systematic reviewing in orthopaedics
BACKGROUND: Machine learning assisted systematic reviewing may help to reduce the work burden in systematic reviews. The aim of this study is therefore to determine by a non-developer the performance of machine learning assisted systematic reviewing on previously published orthopaedic reviews in…
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Machine learning methods for tracer kinetic modelling
…routine has been constrained by its complexity and computational costs. Machine learning poses an opportunity to improve modelling processes in terms of arterial input function prediction, the prediction of kinetic modelling parameters and model selection in both clinical and preclinical studies…
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Simplifying complex antibody engineering using machine learning
Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with…
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Machine learning models’ assessment: trust and performance
ABSTRACT: The common black box nature of machine learning models is an obstacle to their application in health care context. Their widespread application is limited by a significant “lack of trust.” So, the main goal of this work is the development of an evaluation approach that can assess…
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Machine-learning diagnostic models for ovarian tumors
PURPOSE: To create a diagnostic framework for clinical behavior and pathological tissue prognosis in ovarian cancer by using machine-learning (ML) methods based on multiple biomarkers. EXPERIMENTAL DESIGN: Overall, 713 patients with ovarian tumors at Sun Yat Sen Memorial Hospital were randomized…
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Identification of eupneic breathing using machine learning
…eupneic breathing challenging. The present article uses an unsupervised machine learning model to identify eupneic breathing amidst a deluge of different DIAm electromyography (EMG) burst patterns in awake rats. This technique offers a scalable and reliable tool that improves efficiency of DIAm EMG…
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Integrating machine learning to advance epitope mapping
…but vary in accuracy, throughput, cost, and feasibility. By comparing machine learning epitope mapping tools, we discuss the importance of data selection, feature design, and algorithm choice in determining the specificity and prediction accuracy of an algorithm. This review discusses limitations…
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Familial Hypercholesterolemia Detection Through Machine Learning Algorithms
[Figure: see text]
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Spatially Resolved Uncertainties for Machine Learning Potentials
[Image: see text] Machine learning potentials have become an essential tool for atomistic simulations, yielding results close to ab initio simulations at a fraction of computational cost. With recent improvements on the achievable accuracies, the focus has now shifted on the data set composition…
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Enhancing Spectrometer Performance with Unsupervised Machine Learning
…to fluctuations. Here, we demonstrate a method rooted in the unsupervised machine learning algorithm principal component analysis (PCA) to evaluate the impact of environmental parameters that affect sensitivity, resolution and peak positions (chemical shifts) in multidimensional SSNMR protein…
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Machine learning for precision diagnostics of autoimmunity
…industry. We have developed an integration pipeline to enable the use of machine learning for patient classification based on multi-omics data in combination with clinical values and laboratory results. The application of our framework resulted in up to 96% prediction accuracy of autoimmune…
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Predicting Phylogenetic Bootstrap Values via Machine Learning
…range (SH-aLRT). Here, we present the educated bootstrap guesser (EBG), a machine learning-based tool that predicts SBS branch support values for a given input phylogeny. EBG is on average [Formula: see text] ([Formula: see text]) times faster than UFBoot2. EBG-based SBS estimates exhibit a median…
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Machine learning in obsessive-compulsive disorder medications
…The aim of this study is to classify the medications of OCD with machine learning (ML) methods and to compare the classification performances of the decision tree (DT), chi-square automatic interaction detection (CHAID) algorithm, and linear model in ML methods. This research is a descriptive…
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Machine learning in causal inference for epidemiology
…is challenging, especially in high-dimensional settings. Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form of the relationship between variables. However, when ML…
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Machine Learning Approaches for Skin Neoplasm Diagnosis
…error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of…
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