1
Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample
AbstractBackgroundThe field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However... [Read More]
1
Smooth Support Vector Machine for Suicide-Related Behaviours Prediction
Suicide-related behaviours need to be prevented on psychiatric patients.Prediction of those behaviours based on patient medical records would bevery useful for the prevention by the psychiatric hospit... [Read More]
1
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification
Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms. [Read More]
1
Frontiers | phMRI: methodological considerations for mitigating potential confounding factors | Neuroscience
Pharmacological Magnetic Resonance Imaging (phMRI) is a variant of conventional MRI that adds pharmacological manipulations in order to study the effects of drugs, or uses pharmacological probes to in... [Read More]
1
3.1. Cross-validation: evaluating estimator performance — scikit-learn 1.0.2 documentation
 Cross-validation: evaluating estimator performance — scikit-learn 1.0.2 documentation     [Read More]
1
How to set the global random_state in Scikit Learn | Bartosz Mikulski
What to do if you keep forgetting to set the random_state?Such information should be in the first paragraph of Scikit Learn manual, but it is hidden somewhere in the FAQ, so let’s write about it h... [Read More]
1
Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records
BackgroundDistinguishing cases from non-cases in free-text electronic medical records is an important initial step in observational epidemiological studies, but manual record validation is time-... [Read More]
1
Iterative random forests to discover predictive and stable high-order interactions | PNAS
We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computationa... [Read More]
1
A comparison of machine learning model validation schemes for non-stationary time series data
Machine learning is increasingly applied to time series data, as it constitutes an attractive alterna-tive to forecasts based on traditional time series models. For independent and identically distrib... [Read More]
1
Frontiers | Predicting SSRI-Resistance: Clinical Features and tagSNPs Prediction Models Based on Support Vector Machine | Psychiatry
Background: A large proportion of major depressive patients will experience recurring episodes. Many patients still do not response to available antidepressants. In order to meaningfully predict who w... [Read More]
1
Using machine learning to predict extreme events in complex systems | PNAS
Understanding and predicting extreme events as well as the related anomalous statistics is a grand challenge in complex natural systems. Deep convolutional neural networks provide a useful tool to... [Read More]
1
Hidden Layers
Important Topic To Understand When Working On Machine Learning Models. “What Are Hidden Layers?” is published by Farhad Malik in FinTechExplained. [Read More]
1
Neural modeling and functional brain imaging: an overview
This article gives an overview of the different functional brain imaging methods, the kinds of questions these methods try to address and some of the questions associated with functional neuroim... [Read More]
1
Nilearn: Statistical Analysis for NeuroImaging in Python — Machine learning for NeuroImaging
Tools for computing functional connectivity matrices and also implementation of algorithm for sparse multi subjects learning of Gaussian graphical models. [Read More]
1
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
Decoding, i.e. prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoder... [Read More]