AIMC Topic: Decision Trees

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A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis.

Computational and mathematical methods in medicine Mar 30, 2021
Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. ...
Diagnostic and Statistical Manual of Mental Disorders Child, Preschool Humans Machine Learning Decision Support Techniques Computational Biology Male Algorithms Decision Trees Autism Spectrum Disorder Brazil Expert Systems Female Infant Infant, Newborn Diagnosis, Computer-Assisted
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Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome.

Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society Mar 30, 2021
INTRODUCTION: Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome.
Models, Statistical Decision Trees Feasibility Studies Autopsy Humans Machine Learning Infant, Newborn Infant Sudden Infant Death Clinical Decision Rules
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Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.

Journal of the American College of Surgeons Mar 8, 2021
BACKGROUND: The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the ...
Emergency Treatment Aged Adult Emergency Service, Hospital Male Artificial Intelligence Middle Aged Benchmarking Risk Assessment Databases, Factual Postoperative Complications Feasibility Studies Humans Female Hospital Mortality Laparotomy Risk Factors Decision Trees
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Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.

BMC anesthesiology Mar 2, 2021
BACKGROUND: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under ce...
Support Vector Machine Decision Trees Heart Rate Humans Anesthesia Reproducibility of Results Neural Networks, Computer Male Female Middle Aged Electrocardiography
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Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples.

Computational and mathematical methods in medicine Feb 23, 2021
Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of PE screening has attracted much attention. The purpose of this study is to screen placental mRNA to o...
Machine Learning Algorithms Biomarkers RNA, Messenger Pregnancy Genetic Markers Decision Trees Humans Female Pre-Eclampsia Placenta Neural Networks, Computer Transcriptome Genetic Testing Databases, Nucleic Acid Diagnosis, Computer-Assisted Computational Biology
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DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images.

Interdisciplinary sciences, computational life sciences Feb 15, 2021
In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading amon...
Radiography, Thoracic COVID-19 Reproducibility of Results Lung Diagnosis, Computer-Assisted Deep Learning Humans Decision Trees Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted
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Intelligent Neutrosophic Diagnostic System for Cardiotocography Data.

Computational intelligence and neuroscience Feb 10, 2021
Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The ...
COVID-19 Algorithms Reproducibility of Results Support Vector Machine Cardiotocography Decision Trees Predictive Value of Tests Humans Neural Networks, Computer Artificial Intelligence Machine Learning
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Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Feb 3, 2021
BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and envir...
Stroke Motor Activity Environment Stroke Rehabilitation Disability Evaluation Functional Status Patient Discharge Aged, 80 and over Male Middle Aged Time Factors Treatment Outcome Decision Trees Recovery of Function Predictive Value of Tests Decision Support Techniques Databases, Factual Humans Female Aged Machine Learning
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A training algorithm with selectable search direction for complex-valued feedforward neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 28, 2021
This paper focuses on presenting an efficient training algorithm for complex-valued feedforward neural networks by utilizing a tree structure. The basic idea of the proposed algorithm is that, by introducing a set of direction factors, distinctive se...
Machine Learning Decision Trees
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Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia.

Environmental science and pollution research international Jan 23, 2021
Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equa...
Malaysia Neural Networks, Computer Decision Trees Artificial Intelligence Solar Energy Algorithms
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