AIMC Topic: Machine Learning

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Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture.

World neurosurgery
BACKGROUND: Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associated with rupture.

Using machine learning-based lesion behavior mapping to identify anatomical networks of cognitive dysfunction: Spatial neglect and attention.

NeuroImage
Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map the anatomical basis of spatial neglect after right brain damage. These studies led to inconsistent results and lively controversies. Given these inc...

BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform With a Nine-Core Processor and BLE Connectivity.

IEEE transactions on biomedical circuits and systems
Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. N...

A robust machine learning enabled decomposition of shear ground reaction forces during the double contact phase of walking.

Gait & posture
BACKGROUND: Dynamic analyses of walking rely on the 3D ground reaction forces (GRF) under each foot, while only the resultant force of both limbs may be recorded on a single-belt instrumented treadmill or when both feet touch the same force platform.

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning.

Medical physics
PURPOSE: The dosimetric accuracies of volumetric modulated arc therapy (VMAT) plans were predicted using plan complexity parameters via machine learning.

Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.

BMJ open
INTRODUCTION: Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificit...

Medical Diagnosis of Cerebral Palsy Rehabilitation Using Eye Images in Machine Learning Techniques.

Journal of medical systems
Cerebral Palsy (CP) is a non progressive neurological disorders commonly associated with a spectrum of developmental disabilities such as strabismus (misalignment of eye). The Eye image are captured through camera, this make the quick diagnosis and e...

Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records.

BMC medical informatics and decision making
BACKGROUND: Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. New approaches are therefore necessary ...

Automatically determining cause of death from verbal autopsy narratives.

BMC medical informatics and decision making
BACKGROUND: A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily u...

In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm.

Journal of medicinal chemistry
Hemolytic toxicity of small molecules, as one of the important ADMET end points, can cause the lysis of erythrocytes membrane and leaking of hemoglobin into the blood plasma, which leads to various side effects. Thus, it is very crucial to assess the...