214,423 results for machine learning
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Neonatal Disease Prediction Using Machine Learning Techniques
…This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia…
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Machine Learning Magnetic Parameters from Spin Configurations
…a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single…
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Prediction of Promiscuity Cliffs Using Machine Learning
…promiscuity differences). To address this prediction task, different machine learning models were generated and the results were compared with single compound predictions. PCs encoding promiscuity differences were found to contain more structure‐promiscuity relationship information than sets of…
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Machine learning identifies stroke features between species
…are often based on single parameters, or in the case of complex machine learning methods, require vast training datasets and are prone to a lack of model interpretation. Methods: We induced IS using the middle cerebral artery occlusion model on two rat cohorts. We acquired apparent diffusion…
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Mitigating bias in machine learning for medicine
Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications.
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Cyberbullying severity detection: A machine learning approach
…information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation…
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Machine learning in neurosurgery: a global survey
BACKGROUND: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical…
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Software Requirements Classification Using Machine Learning Algorithms
…shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions “Which works best (Bag of Words (BoW) vs. Term Frequency–Inverse Document Frequency (TF-IDF) vs. Chi Squared…
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Machine Learning for Discovery of GSK3β Inhibitors
…amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate…
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Machine learning glass transition temperature of polymers
As an important thermophysical property, polymers' glass transition temperature, Tg, could sometimes be difficult to determine experimentally. Modeling methods, particularly data-driven approaches, are promising alternatives to predictions of Tg in a fast and robust way. The molecular traceless…
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Forecasting Corn Yield With Machine Learning Ensembles
…to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predictions faster and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a “committee” of models (machine…
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Practical Vitamin D Supplementation Using Machine Learning
BACKGROUND: Patients with breast cancer are at increased risk of developing osteoporosis. Maintaining normal levels of vitamin D may decrease the risk of osteoporosis, and vitamin D levels must be corrected in patients who develop osteoporosis before beginning bone modifying agents. Therefore, it…
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Data science and machine learning in anesthesiology
Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with developing algorithms and models to perform prediction…
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Applications of Machine Learning in Cardiac Electrophysiology
Artificial intelligence through machine learning (ML) methods is becoming prevalent throughout the world, with increasing adoption in healthcare. Improvements in technology have allowed early applications of machine learning to assist physician efficiency and diagnostic accuracy. In…
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Artificial Intelligence, Machine Learning, and Cardiovascular Disease
…recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new…
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Breast Cancer Type Classification Using Machine Learning
…cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple…
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Tackling Photonic Inverse Design with Machine Learning
Machine learning, as a study of algorithms that automate prediction and decision‐making based on complex data, has become one of the most effective tools in the study of artificial intelligence. In recent years, scientific communities have been gradually merging data‐driven approaches with…
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Has machine learning rendered simple rules obsolete?
…obsolete with the arrival of artificial intelligence, and in particular machine learning (ML)? Can ML deliver better algorithmic rules than traditional simple legal rules? This paper argues that the answer to these question is “no” by building an argument based on three increasingly more serious…
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Machine learning empowers phosphoproteome prediction in cancers
MOTIVATION: Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass…
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