AIMC Topic: Machine Learning

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Machine learning fusion for glioma tumor detection.

Scientific reports
The early detection of brain tumors is very important for treating them and improving the quality of life for patients. Through advanced imaging techniques, doctors can now make more informed decisions. This paper introduces a framework for a tumor d...

An innovative model based on machine learning and fuzzy logic for tracking lower limb exercises in stroke patients.

Scientific reports
Rehabilitation after a stroke is vital for regaining functional abilities. However, a shortage of rehabilitation professionals leads to many patients with severe disabilities. Traditional rehabilitation methods can be time-consuming and hard to measu...

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Scientific reports
Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest for an accurate and efficient method for early DIC prediction is of paramount importance. Candidate ...

Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning.

Scientific reports
Psoriasis is a common chronic skin disorder with a polygenic background. It is widely acknowledged that Th17/IL-17A axis plays a key role in the pathogenesis of psoriasis. However, numerous regulatory genes upstream of the pathway remain undiscovered...

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical car...

Capitalizing on natural language processing (NLP) to automate the evaluation of coach implementation fidelity in guided digital cognitive-behavioral therapy (GdCBT).

Psychological medicine
BACKGROUND: As the use of guided digitally-delivered cognitive-behavioral therapy (GdCBT) grows, pragmatic analytic tools are needed to evaluate coaches' implementation fidelity.

Data-Driven Prognostication in Distal Medium Vessel Occlusions Using Explainable Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Distal medium vessel occlusions (DMVOs) are estimated to cause acute ischemic stroke in 25%-40% of cases. Prognostic models can inform patient counseling and research by enabling outcome predictions. However, models designed s...

Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.

IEEE transactions on biomedical circuits and systems
This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have ga...

Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma.

Frontiers in immunology
BACKGROUND: Unlike lung adenocarcinoma, patients with advanced squamous carcinoma exhibit a low proportion of driver gene positivity, with fewer effective treatment strategies available. Chemoimmunotherapy has now become the standard first-line treat...

Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective.

Journal of nursing management
Workplace violence, defined as any disruptive behavior or threat to employees, seriously threatens junior nurses. Compared with senior nurses, junior nurses are more vulnerable to workplace violence due to inexperience, low professional recognition,...