AIMC Topic:
Supervised Machine Learning

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Novel Feature Selection for Artificial Intelligence Using Item Response Theory for Mortality Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Feature selection is a critical component in supervised machine learning classification analyses. Extraneous features introduce noise and inefficiencies into the system leading to a need for feature reduction techniques. Many feature reduction models...
Algorithms Models, Statistical Supervised Machine Learning Artificial Intelligence Machine Learning
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Supervised Machine-Learning Algorithms in Real-time Prediction of Hypotensive Events.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Hypotension is common in critically ill patients. Early prediction of hypotensive events in the Intensive Care Units (ICUs) allows clinicians to pre-emptively treat the patient and avoid possible organ damage. In this study, we investigate the perfor...
Logistic Models Supervised Machine Learning Hypotension Humans Machine Learning Algorithms
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Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals...
Arterial Pressure Algorithms Humans Databases, Factual Machine Learning Supervised Machine Learning
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Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variati...
Tremor Humans Algorithms Logistic Models Supervised Machine Learning Parkinson Disease, Secondary
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Using Supervised Learning Methods to Develop a List of Prescription Medications of Greatest Concern during Pregnancy.

Maternal and child health journal Jul 1, 2020
INTRODUCTION: Women and healthcare providers lack adequate information on medication safety during pregnancy. While resources describing fetal risk are available, information is provided in multiple locations, often with subjective assessments of ava...
Pregnancy Female Adult Humans Databases, Pharmaceutical Drug Labeling Prescription Drugs Supervised Machine Learning Practice Patterns, Physicians' Pregnancy Complications
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Supervised Learning for the ICD-10 Coding of French Clinical Narratives.

Studies in health technology and informatics Jun 16, 2020
Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automa...
Clinical Coding Supervised Machine Learning International Classification of Diseases Neural Networks, Computer Electronic Health Records Narration
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Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2020
OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We dev...
Data Science Observational Studies as Topic Supervised Machine Learning Medical Informatics Classification Humans Electronic Health Records
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Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning.

American journal of human genetics May 7, 2020
Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses. However, gene signals are often insufficiently powered to reach experiment-wide significance, triggering a process of laborious triaging of gen...
Renal Insufficiency, Chronic Disease Models, Animal Area Under Curve Epilepsy Amyotrophic Lateral Sclerosis Mice Exome Genomics Neural Networks, Computer Genetic Association Studies Supervised Machine Learning Humans Reproducibility of Results Deep Learning Stochastic Processes Animals ROC Curve
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ColocML: machine learning quantifies co-localization between mass spectrometry images.

Bioinformatics (Oxford, England) May 1, 2020
MOTIVATION: Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referre...
Software Mass Spectrometry Supervised Machine Learning Neural Networks, Computer Machine Learning
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Modeling engagement in long-term, in-home socially assistive robot interventions for children with autism spectrum disorders.

Science robotics Feb 26, 2020
Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in...
Home Care Services Social Behavior Self-Help Devices Cues Models, Psychological Communication Devices for People with Disabilities Child Behavior Child Autism Spectrum Disorder Robotics Models, Theoretical Precision Medicine Feasibility Studies Humans User-Computer Interface Supervised Machine Learning Algorithms
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