AIMC Topic: Machine Learning

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Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.

BMC medical informatics and decision making
BACKGROUND: Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-prov...

HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints.

Sensors (Basel, Switzerland)
With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images ...

HSM6AP: a high-precision predictor for the Homo N6-methyladenosine (m^6 A) based on multiple weights and feature stitching.

RNA biology
Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information ...

Artificial intelligence, machine learning, and drug repurposing in cancer.

Expert opinion on drug discovery
: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposin...

Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification.

Neural networks : the official journal of the International Neural Network Society
Recent years have witnessed numerous successful applications of incorporating attention module into feed-forward convolutional neural networks. Along this line of research, we design a novel lightweight general-purpose attention module by simultaneou...

Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder.

Neural networks : the official journal of the International Neural Network Society
Neurodevelopmental disorders are characterized by heterogeneous and non-specific nature of their clinical symptoms. In particular, hyper- and hypo-reactivity to sensory stimuli are diagnostic features of autism spectrum disorder and are reported acro...

Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.

Acta orthopaedica
Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to differe...

Leveraging innovative technology to generate drug response phenotypes for the advancement of biomarker-driven precision dosing.

Clinical and translational science
Although traditional approaches to biomarker discovery have elucidated key molecular markers that have improved drug selection (precision medicine), the discovery of biomarkers that inform optimal dose selection (precision dosing) continues to be a c...

Structural protein fold recognition based on secondary structure and evolutionary information using machine learning algorithms.

Computational biology and chemistry
Understanding the function of protein is conducive to research in advanced fields such as gene therapy of diseases, the development and design of new drugs, etc. The prerequisite for understanding the function of a protein is to determine its tertiar...