AIMC Topic: Machine Learning

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Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.

The Journal of thoracic and cardiovascular surgery Aug 10, 2020
BACKGROUND: Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We...
Cardiac Surgical Procedures Prognosis Humans Machine Learning Logistic Models Bayes Theorem
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Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

Laboratory investigation; a journal of technical methods and pathology Aug 10, 2020
As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)popula...
Protein Binding Mice Computational Biology Algorithms Animals Deep Learning Receptors, Estrogen Models, Statistical Databases, Chemical Endocrine Disruptors Humans Machine Learning
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Effect of vessel wall segmentation on volumetric and radiomic parameters of coronary plaques with adverse characteristics.

Journal of cardiovascular computed tomography Aug 10, 2020
BACKGROUND: Quantitative coronary plaque parameters are increasingly being utilized as surrogate endpoints of pharmaceutical trials. However, little is known whether differences in segmentation significantly alter parameter values.
Vascular Calcification Severity of Illness Index Computed Tomography Angiography Humans Clinical Competence Retrospective Studies Machine Learning Coronary Angiography Observer Variation Predictive Value of Tests Aged Female Radiographic Image Interpretation, Computer-Assisted Male Coronary Vessels Reproducibility of Results Coronary Artery Disease Middle Aged Plaque, Atherosclerotic
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Manganese (Mn) removal prediction using extreme gradient model.

Ecotoxicology and environmental safety Aug 10, 2020
Exploring the Manganese (Mn) removal prediction with several independent variables is tremendously critical and indispensable to understand the pattern of removal process. Mn is one of the key heavy metals (HMs) stipulated by the WHO for the developm...
Models, Theoretical Support Vector Machine Water Pollutants, Chemical Fresh Water Algorithms Manganese Machine Learning Forecasting Ecosystem
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Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test.

Sensors (Basel, Switzerland) Aug 10, 2020
Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson's disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging weara...
Parkinson Disease Humans Gait Analysis Machine Learning Gait Disorders, Neurologic Aged Wearable Electronic Devices
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Golf Swing Segmentation from a Single IMU Using Machine Learning.

Sensors (Basel, Switzerland) Aug 10, 2020
Golf swing segmentation with inertial measurement units (IMUs) is an essential process for swing analysis using wearables. However, no attempt has been made to apply machine learning models to estimate and divide golf swing phases. In this study, we ...
Movement Golf Humans Biomechanical Phenomena Male Neural Networks, Computer Range of Motion, Articular Machine Learning
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Challenge-Enabled Machine Learning to Drug-Response Prediction.

The AAPS journal Aug 10, 2020
In recent decades, the advancement of computational algorithms and the availability of big data have enabled artificial intelligence (AI) to dramatically improve predictive performance in nearly all research areas. Specifically, machine learning (ML)...
Machine Learning Drug Discovery
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Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms.

Environmental monitoring and assessment Aug 10, 2020
Drought, which has become one of the most severe environmental problems worldwide, has serious impacts on ecological, economic, and socially sustainable development. The drought monitoring process is essential in the management of drought risks, and ...
Algorithms Environmental Monitoring Support Vector Machine Droughts Machine Learning
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Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study.

Journal of medical Internet research Aug 10, 2020
BACKGROUND: As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised.
Male Middle Aged Neoplasms Aged, 80 and over Young Adult Adult Humans Health Records, Personal Neural Networks, Computer RNA Machine Learning High-Throughput Nucleotide Sequencing Adolescent Child Support Vector Machine Algorithms Female Aged
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ASO Author Reflections: Development and Validation of a Novel Risk Score Using Machine-Learning Methodology to Predict Recurrence After Hepatectomy for Colorectal Liver Metastases.

Annals of surgical oncology Aug 9, 2020
Humans Machine Learning Liver Neoplasms Reproducibility of Results Neoplasm Recurrence, Local Risk Factors Hepatectomy Colorectal Neoplasms
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