AIMC Topic: Machine Learning

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Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway.

Journal of neurology
We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute ...

Applying Deep Neural Network Analysis to High-Content Image-Based Assays.

SLAS discovery : advancing life sciences R & D
The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overco...

Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease.

FEBS letters
Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional biochemical information to benefit histological disease diagnosis. Using a dietary model of nonalcoholi...

Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop metastatic tumor growth before it causes excessive patient morbidity. However, nanoparticle delivery t...

Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking.

Journal of medical Internet research
BACKGROUND: Hookah tobacco smoking (HTS) is a particularly important issue for public health professionals to address owing to its prevalence and deleterious health effects. Social media sites can be a valuable tool for public health officials to con...

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

PloS one
PURPOSE: To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-m...

DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy.

Computational biology and chemistry
ncRNA-protein interactions (ncRPIs) play an important role in a number of cellular processes, such as post-transcriptional modification, transcriptional regulation, disease progression and development. Since experimental methods are expensive and tim...

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data.

Acta psychiatrica Scandinavica
OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first 3 days following admission could ...