AIMC Topic: Machine Learning

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What does a pain 'biomarker' mean and can a machine be taught to measure pain?

Neuroscience letters
Artificial intelligence allows machines to predict human faculties such as image and voice recognition. Can machines be taught to measure pain? We argue that the two fundamental requirements for a device with 'pain biomarker' capabilities are hardwar...

Toward a motor signature in autism: Studies from human-machine interaction.

L'Encephale
BACKGROUND: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders which core symptoms are impairments in socio-communication and repetitive symptoms and stereotypies. Although not cardinal symptoms per se, motor impa...

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

Journal of chemical information and modeling
The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It wa...

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

Japanese journal of radiology
In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority...

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate AS...

Advances with support vector machines for novel drug discovery.

Expert opinion on drug discovery
Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learni...

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
OBJECTIVES: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospital-level care could triage patients more efficiently to high- or low-resource ED tracks. Existing t...

A Survey of Multi-task Learning Methods in Chemoinformatics.

Molecular informatics
Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting d...

Detection of herpesvirus capsids in transmission electron microscopy images using transfer learning.

Histochemistry and cell biology
The detailed analysis of secondary envelopment of the Human betaherpesvirus 5/human cytomegalovirus (HCMV) from transmission electron microscopy (TEM) images is an important step towards understanding the mechanisms underlying the formation of infect...