AIMC Topic: Machine Learning

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Inexpensive method for the quantitative estimation of hepatitis C virus RNA in blood plasma for low-resource settings using ML-based image intensity analysis of RT-LAMP products.

Lab on a chip
Hepatitis C virus (HCV) infection is a severe public health problem affecting nearly 3% of the world population. Of those affected, approximately 80% develop chronic infections. Initiating treatment through HCV RNA testing remains challenging, especi...

Incorporating respiratory signals for machine learning-based multimodal sleep stage classification: a large-scale benchmark study with actigraphy and heart rate variability.

Sleep
Insufficient sleep quality is directly linked to various diseases, making reliable sleep monitoring crucial for prevention, diagnosis, and treatment. As sleep laboratories are cost- and resource-prohibitive, wearable sensors offer a promising alterna...

Diffusive gradient in thin films combined with machine learning to discern the accumulation characteristics and driving factors of Cd and Cu in soil-rice systems.

Journal of hazardous materials
The dietary exposure risk of cadmium (Cd) in rice is significantly higher than that of copper (Cu), while the co-migration of Cd and Cu in the soil-crop system may enhance the bioavailability of pollution, thus making rapid and accurate prediction of...

Development and external validation of a prediction model for prolonged intensive care unit stay in heart failure patients.

European journal of cardiovascular nursing
AIMS: Prolonged intensive care unit (ICU) stays in heart failure patients are associated with poor prognosis and result in high medical expenses. To develop and validate a predictive model for prolonged ICU stays in heart failure patients.

Compositional analysis of alternative protein blends using near and mid-infrared spectroscopy coupled with conventional and machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The non-invasive real-time analysis of the composition of alternative, plant-based protein sources is important to control high moisture extrusion processes and ensure the quality and texture of the final extrudates used in the elaboration of meat an...

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic methods. Near-infrared spectroscopy (NIRS) offers unique advantages in probing molecular vibrational i...

A machine learning-based nuclear magnetic resonance profiling model to authenticate 'Jerez-Xérès-Sherry' wines.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Traditionally, wine quality and certification have been assessed through sensory analysis by trained tasters. However, this method has the limitation of relying on highly specialized individuals who are typically trained to evaluate only specific typ...

[Generative artificial intelligence ChatGPT in clinical nutrition - Advances and challenges].

Nutricion hospitalaria
ChatGPT and other artificial intelligence (AI) tools can modify nutritional management in clinical settings. These technologies, based on machine learning and deep learning, enable the identification of risks, the proposal of personalized interventio...

Use of Client-Side Machine Learning Models for Privacy-Preserving Healthcare Predictions - A Deployment Case Study.

Studies in health technology and informatics
INTRODUCTION: Machine learning (ML) and deep learning (DL) models in healthcare traditionally rely on server-centric architectures, where sensitive patient data is transmitted to external servers for processing via frameworks like Flask, raising sign...

Medical Entity Linking in Low-Resource Settings with Fine-Tuning-Free LLMs.

Studies in health technology and informatics
INTRODUCTION: Medical entity linking is an important task in biomedical natural language processing, aiming to align textual mentions of medical concepts with standardized concepts in ontologies. Most existing approaches rely on supervised models or ...