Machine Learning - AI Medical Compendium

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Critical care medicine Feb 1, 2016

OBJECTIVE: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter ...

Survival Analysis Vital Signs Heart Arrest Diagnostic Techniques and Procedures Support Vector Machine Age Factors Models, Statistical Logistic Models Socioeconomic Factors Machine Learning Intensive Care Units Risk Assessment Cohort Studies Time Factors ROC Curve Humans Neural Networks, Computer

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Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository.

Journal of digital imaging Feb 1, 2016

Radiology report narrative contains a large amount of information about the patient's health and the radiologist's interpretation of medical findings. Most of this critical information is entered in free text format, even when structured radiology re...

Models, Theoretical Cluster Analysis Natural Language Processing Machine Learning Algorithms Radiology Information Systems Humans

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Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy.

Journal of digital imaging Feb 1, 2016

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of ...

Databases, Factual Female Mammography Humans Machine Learning Pattern Recognition, Automated Image Interpretation, Computer-Assisted Information Storage and Retrieval

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Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.

Biomedizinische Technik. Biomedical engineering Feb 1, 2016

There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a numbe...

Discriminant Analysis Nonlinear Dynamics Algorithms Pattern Recognition, Automated Computer Simulation Sensitivity and Specificity Imagination Female Male Humans Reproducibility of Results Evoked Potentials, Somatosensory Machine Learning Brain-Computer Interfaces Young Adult Sensorimotor Cortex Oscillometry Adult Evoked Potentials, Motor

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The importance of imagination (or lack thereof) in artificial, human and quantum decision making.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Jan 13, 2016

Enlarging upon experiments and analysis that I did jointly some years ago, in which artificial (symbolic, neural-net and pattern) learning and generalization were compared with that of humans, I will emphasize the role of imagination (or lack thereof...

Machine Learning Decision Support Techniques Computer Simulation Models, Neurological Decision Making Humans Imagination Models, Statistical Quantum Theory

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