AIMC Topic: Machine Learning

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Machine learning-based reproducible prediction of type 2 diabetes subtypes.

Diabetologia
AIMS/HYPOTHESIS: Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is c...

Developing a machine learning-based instrument for subjective well-being assessment on Weibo and its psychological significance: An evaluative and interpretive research.

Applied psychology. Health and well-being
Demystifying machine learning (ML) approaches through the synergy of psychology and artificial intelligence can achieve a balance between predictive and explanatory power in model development while enhancing rigor in validation and reporting standard...

On the Application of Artificial Intelligence/Machine Learning (AI/ML) in Late-Stage Clinical Development.

Therapeutic innovation & regulatory science
Whereas AI/ML methods were considered experimental tools in clinical development for some time, nowadays they are widely available. However, stakeholders in the health care industry still need to answer the question which role these methods can reali...

Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach.

Internal and emergency medicine
The diagnosis of abdominal pain in emergency departments is challenging, and appendicitis is a common concern. Atypical symptoms often delay diagnosis. Although the Alvarado score aids in decision-making, its low specificity can lead to unnecessary s...

Explainable machine learning for assessing upper respiratory tract of racehorses from endoscopy videos.

Computers in biology and medicine
Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endoscopy imaging of horse throat is a gold standard for URT assessment. However, current manual assessment faces several challenges, stemming from the poo...

Continual learning for seizure prediction via memory projection strategy.

Computers in biology and medicine
Despite extensive algorithms for epilepsy prediction via machine learning, most models are tailored for offline scenarios and cannot handle actual scenarios where data changes over time. Catastrophic forgetting(CF) for learned electroencephalogram(EE...

Establishment and validation of a risk stratification model for stroke risk within three years in patients with cerebral small vessel disease using a combined MRI and machine learning algorithm.

SLAS technology
BACKGROUND: Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a...

Machine Learning-Assisted Eu(III)-Functionalized HOF-on-HOF Composite-Based Sensor Platform for Precise and Visual Identification of Multiple Pesticides.

Analytical chemistry
Precise and rapid identification of pesticides is crucial to ensure a green environment, food safety, and human health. However, complex sample environments often hinder precise identification, especially for simultaneous differentiation of multiple ...

Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.

Journal of water and health
Household water treatment (HWT) is recommended when safe drinking water is limited. To understand determinants of HWT adoption, we conducted a cross-sectional survey with 650 households across different regions in Haiti. Data were collected on 71 dem...