AIMC Topic: Machine Learning

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Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.

Computational and mathematical methods in medicine
In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect...

Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Journal of healthcare engineering
OBJECTIVE: Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis ons...

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.

Journal of healthcare engineering
There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming...

Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution.

Journal of healthcare engineering
This study aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality and machine learning. Hemorrhagic stroke, air quality, and meteorological data for 2016-2017 were obtained from the Longquanyi Dist...

Intravascular ultrasound-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions.

Atherosclerosis
BACKGROUND AND AIMS: Intravascular ultrasound (IVUS)-derived morphological criteria are poor predictors of the functional significance of intermediate coronary stenosis. IVUS-based supervised machine learning (ML) algorithms were developed to identif...

Machine-learned analysis of side-differences in odor identification performance.

Neuroscience
A right-left dichotomy of olfactory processes has been recognized on several levels of the perception or processing of olfactory input. On a clinical level, the lateralization of components of human olfaction is contrasted by the predominantly birhin...

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

NeuroImage
Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algo...

Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy.

Journal of neurology
BACKGROUND: Chemosensory loss is a symptom of Parkinson's disease starting already at preclinical stages. Their appearance without an identifiable etiology therefore indicates a possible early symptom of Parkinson's disease. Supervised machine-learni...

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

Journal of general internal medicine
BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for cre...