214.423 hasil untuk machine learning
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Using machine learning to identify pediatric ophthalmologists
This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning algorithms to identify pediatric ophthalmologists based on physician coding patterns. A random forest model achieved an area under the receiver…
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Prediction of biological age using machine learning
…of study. This research utilizes health examination data to propose a machine learning-based biological age prediction model that quantifies physiological age through residual life estimation. The model leverages LightGBM, which shows an 11.40% improvement in predictive performance (R-squared)…
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Interpretable machine learning for precision cognitive aging
INTRODUCTION: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to address this by enhancing transparency and trust in AI decision-making. However, a…
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Machine Learning to Diagnose Complications of Diabetes
Machine learning (ML) uses computer systems to develop statistical algorithms and statistical models that can draw inferences from demographic data, structured behavioral data, continuous glucose monitor (CGM) tracings, laboratory data, cardiovascular and neurological physiology measurements, and…
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Automated spectrometer alignment via machine learning. Corrigendum
Corrigendum to the article by Feuer-Forson et al. [(2024). J. Synchrotron Rad.31, 698–705].
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Machine learning for personalized antimicrobial susceptibility breakpoints
…predict diagnosis when specimens are received. Here, we assess whether machine learning could facilitate personalized antimicrobial susceptibility breakpoint reporting by predicting urinary tract infection (UTI) diagnoses. METHODS: XGBoost models were trained using open-source electronic healthcare…
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Machine Learning in Transforming the Food Industry
…setting the futuristic trends in the food sector. The use of different Machine Learning (ML)-based models has demonstrated promising applications in the food processing industry. Processing operations such as drying, frying, cooking, heating, and baking are complex and challenged by multifaceted…
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Machine learning-based prediction of glioma grading
…by subjectivity and poor reproducibility. This study aimed to develop a machine learning–based prediction model that integrates clinical and molecular characteristics to improve early glioma grading, thereby enhancing diagnostic accuracy and supporting individualized treatment strategies. METHODS…
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Koina: Democratizing machine learning for proteomics research
Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which…
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Machine Learning for Protein Science and Engineering
Recent years have seen significant breakthroughs at the intersection of machine learning and protein science. Tools such as AlphaFold have revolutionized protein structure prediction. They are also enabling variant effect prediction and functional annotation of proteins, as well as opening up new…
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Machine learning-based penetrance of genetic variants
Accurate variant penetrance estimation is crucial for precision medicine. We constructed machine learning (ML) models for 10 diseases using 1,347,298 participants with electronic health records (EHRs), then applied them to an independent cohort with linked exome data. Resulting probabilities were…
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Machine Learning Phase Classification of Thermoelectric Materials
In this study, we employ a Support Vector Machine (SVM) model to efficiently classify the phases of thermoelectric (TE) alloys. While ab initio calculations and experiments have explored the phases of functional TE materials, the large variety of alloys makes these explorations time-consuming and…
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Machine learning predicts meter-scale laboratory earthquakes
In recent years, there has been a growing interest in utilizing machine learning (ML) to investigate the predictability of shear-slip failures, known as laboratory quakes, in centimeter-scale rock-friction experiments. However, the applicability of ML to larger-scale laboratory quakes and natural…
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Machine Learning-Based Transcriptomic Diagnosis of Periodontitis
…by subjectivity and low sensitivity. This study aimed to develop a machine learning (ML)-based diagnostic framework using transcriptomic data to enhance diagnostic accuracy and efficiency. METHODS: Transcriptomic datasets from 616 samples (452 periodontitis, 164 healthy controls) were retrieved…
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Prediction of stillbirth using machine learning methods
This study developed a machine learning model to predict stillbirth using retrospective data from 32,953 singleton pregnancies at multi-centers in South Korea. Variables were collected at baseline, E1 (before 13 weeks of pregnancy), and T0 (before 28 weeks of pregnancy). Each separate cohort (all…
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Incisional hernia prediction using machine learning models
…low or high Risk. There is no currently full peer-reviewed study in which Machine learning (ML) is applied for the prediction of IH, so the objective of this study was to develop a predictive model of IH based on different ML techniques. METHODS: Retrospective cohort study type of diagnostic tests…
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Unraveling sperm kinematic heterogeneity with machine learning
…for analysis using conventional statistical methods. Advances in machine learning and deep learning have addressed these limitations by leveraging motility parameters and trajectory representations for automated classification and clustering of motility patterns. These methods, including supervised…
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Machine Learning-Assisted Quantification of Organelle Abundance
…potential bias and variability. To address this issue, we present a machine learning–assisted algorithm that allows for the quantification of organelle density using the open-source Fiji platform and WEKA segmentation. Our method enables the automated quantification of organelle number, area, and…
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Advances in Machine Learning for Wearable Sensors
Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and…
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