AIMC Topic: Decision Trees

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Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models.

International journal of medical informatics Aug 24, 2019
INTRODUCTION: Machine learning has been increasingly used to develop predictive models to diagnose different disease conditions. The heterogeneity of the kidney transplant population makes predicting graft outcomes extremely challenging. Several kidn...
Humans Bayes Theorem Neural Networks, Computer Machine Learning Decision Trees Predictive Value of Tests Graft Rejection Kidney Transplantation Databases, Factual
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Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry.

Artificial intelligence in medicine Aug 9, 2019
INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learnin...
Humans Artificial Intelligence Machine Learning Alzheimer Disease Decision Trees Mental Disorders Models, Biological Psychiatry Schizophrenia Precision Medicine Data Mining Depression
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Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions.

Research synthesis methods Aug 5, 2019
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free s...
Research Design Reproducibility of Results Animals Ecosystem Artificial Intelligence Machine Learning Algorithms Biology Ecology Models, Statistical Asia Decision Trees Geography Data Interpretation, Statistical Introduced Species Review Literature as Topic
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Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

Journal of biomedical informatics Jul 30, 2019
AIM: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical softwa...
Humans Risk Factors Coronary Disease Neural Networks, Computer Computational Biology Models, Statistical Logistic Models Algorithms Support Vector Machine Decision Trees Databases, Factual Longitudinal Studies Area Under Curve Prospective Studies Supervised Machine Learning
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Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.

Molecules (Basel, Switzerland) Jul 26, 2019
Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are...
Reproducibility of Results Drug Discovery Computational Biology Models, Theoretical Decision Trees Ligands Chemical Phenomena Databases, Chemical Quantitative Structure-Activity Relationship Machine Learning Algorithms Decision Support Techniques
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Machine learning and statistical models for predicting indoor air quality.

Indoor air Jul 25, 2019
Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mo...
Humans Neural Networks, Computer Machine Learning Air Pollutants Air Pollution, Indoor Environmental Monitoring Models, Statistical Regression Analysis Decision Trees
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Comparisons among Machine Learning Models for the Prediction of Hypercholestrolemia Associated with Exposure to Lead, Mercury, and Cadmium.

International journal of environmental research and public health Jul 25, 2019
Lead, mercury, and cadmium are common environmental pollutants in industrialized countries, but their combined impact on hypercholesterolemia (HC) is poorly understood. The aim of this study was to compare the performance of various machine learning ...
Cadmium Middle Aged Hypercholesterolemia ROC Curve Logistic Models Republic of Korea Environmental Pollutants Humans Lead Support Vector Machine Female Environmental Exposure Decision Trees Adult Machine Learning Mercury Male Forecasting
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Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Artificial intelligence in medicine Jul 25, 2019
OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health Organization, India has the highest number of pre-term births [1], with over 3.5 million babies born...
Intensive Care Units, Neonatal Clinical Decision Rules Algorithms Apnea Infant, Newborn Humans India Heart Rate Infant, Extremely Premature Neural Networks, Computer Datasets as Topic Support Vector Machine Deep Learning Infant, Premature Decision Trees Gestational Age Birth Weight
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Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases.

Artificial intelligence in medicine Jul 23, 2019
In order to gain insight into oligogenic disorders, understanding those involving bi-locus variant combinations appears to be key. In prior work, we showed that features at multiple biological scales can already be used to discriminate among two type...
Game Theory Humans Machine Learning Multifactorial Inheritance Genetic Predisposition to Disease Decision Trees
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.

Scientific reports Jul 17, 2019
Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machin...
INDEL Mutation Crohn Disease Alleles Nonlinear Dynamics Genotyping Techniques ROC Curve Genetic Predisposition to Disease Polymorphism, Single Nucleotide Genotype Logistic Models Models, Genetic Decision Trees Area Under Curve Female Humans Male Neural Networks, Computer Deep Learning Genome-Wide Association Study
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