AIMC Topic: Machine Learning

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Exploring Deep Physiological Models for Nociceptive Pain Recognition.

Sensors (Basel, Switzerland)
Standard feature engineering involves manually designing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features for the subsequent optimisation of ...

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging.

European radiology experimental
BACKGROUND: To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).

Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk.

Translational psychiatry
Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predicto...

Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania.

PloS one
Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing...

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing.

Advanced materials (Deerfield Beach, Fla.)
Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine lear...

Realistic spiking neural network: Non-synaptic mechanisms improve convergence in cell assembly.

Neural networks : the official journal of the International Neural Network Society
Learning in neural networks inspired by brain tissue has been studied for machine learning applications. However, existing works primarily focused on the concept of synaptic weight modulation, and other aspects of neuronal interactions, such as non-s...

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to i...

Novel Screening Method Identifies PI3Kα, mTOR, and IGF1R as Key Kinases Regulating Cardiomyocyte Survival.

Journal of the American Heart Association
Background Small molecule kinase inhibitors (KIs) are a class of agents currently used for treatment of various cancers. Unfortunately, treatment of cancer patients with some of the KIs is associated with cardiotoxicity, and there is an unmet need fo...

An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition.

Sensors (Basel, Switzerland)
Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher c...