AIMC Topic: Machine Learning

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A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

Acta psychiatrica Scandinavica
OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associated with alterations in grey matter volume. However, it is unclear to what degree these patterns of morphometric change reflect symptom dimensions. H...

Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning.

Journal of neuroimmunology
INTRODUCTION: The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric ...

Super-resolution reconstruction of MR image with a novel residual learning network algorithm.

Physics in medicine and biology
Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural network...

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

PloS one
We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vect...

Spoken language identification based on the enhanced self-adjusting extreme learning machine approach.

PloS one
Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based ...

Sub-millimeter ECoG pitch in human enables higher fidelity cognitive neural state estimation.

NeuroImage
Electrocorticography (ECoG), electrophysiological recording from the pial surface of the brain, is a critical measurement technique for clinical neurophysiology, basic neurophysiology studies, and demonstrates great promise for the development of neu...

Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is th...

Automated EEG-based screening of depression using deep convolutional neural network.

Computer methods and programs in biomedicine
In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of de...

Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.

F1000Research
We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatmen...