AIMC Topic: Machine Learning

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An adverse drug effect mentions extraction method based on weighted online recurrent extreme learning machine.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic extraction of adverse drug effect (ADE) mentions from biomedical texts is a challenging research problem that has attracted significant attention from the pharmacovigilance and biomedical text mining communities. I...

Efficient learning from big data for cancer risk modeling: A case study with melanoma.

Computers in biology and medicine
BACKGROUND: Building cancer risk models from real-world data requires overcoming challenges in data preprocessing, efficient representation, and computational performance. We present a case study of a cloud-based approach to learning from de-identifi...

An algorithm for learning shape and appearance models without annotations.

Medical image analysis
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data...

Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

Pediatric radiology
BACKGROUND: An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e.g., automated hanging protocols) and help facilitate the automated curation of large medical imagin...

Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling.

Molecules (Basel, Switzerland)
The performance of quantitative structure-activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main catego...

A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis.

Sensors (Basel, Switzerland)
An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (...

Implications of asymmetric neural activity patterns in the basal ganglia outflow in the integrative neural network model for cervical dystonia.

Progress in brain research
Cervical dystonia (CD) is characterized by abnormal twisting and turning of the head with associated head oscillations. It is the most common form of dystonia, which is a third most common movement disorder. Despite frequent occurrence there is pauci...

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models.

NeuroImage. Clinical
Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in mild cognitive impairment (MCI). Focused on providing an earlier and more accurate diagnosis, sophist...

Methodology and validation for identifying gait type using machine learning on IMU data.

Journal of medical engineering & technology
With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this dif...

Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua.

Medical care research and review : MCRR
Free-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but they have been used mostly for English-language data sets. Considering such a framework, the perform...