AIMC Topic: Bayes Theorem

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Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.

Scientific reports
Rare diseases, such as Mucopolysaccharidosis (MPS), present significant challenges to the healthcare system. Some of the most critical challenges are the delay and the lack of accurate disease diagnosis. Early diagnosis of MPS is crucial, as it has t...

Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint.

Journal of medical Internet research
Digital twin (DT) technology is revolutionizing clinical practice by integrating diverse epidemiological data sources to create dynamic, patient-specific simulations. By leveraging data from genomics, proteomics, imaging, sociodemographics, and real-...

Uncovering locomotor learning dynamics in people with Parkinson's disease.

PloS one
Locomotor learning is important for improving gait and balance impairments in people with Parkinson's disease (PD). While PD disrupts neural networks involved in motor learning, there is a limited understanding of how PD influences the time course of...

A beautiful loop: An active inference theory of consciousness.

Neuroscience and biobehavioral reviews
Can active inference model consciousness? We offer three conditions implying that it can. The first condition is the simulation of a world model, which determines what can be known or acted upon; namely an epistemic field. The second is inferential c...

A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.

BMC medical imaging
OBJECTIVES: Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume ...

Features extraction based on Naive Bayes algorithm and TF-IDF for news classification.

PloS one
The rapid proliferation of online news demands robust automated classification systems to enhance information organization and personalized recommendation. Although traditional methods like TF-IDF with Naive Bayes provide foundational solutions, thei...

Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.

BMC medical informatics and decision making
INTRODUCTION: The use of machine learning (ML) in intensive care units (ICUs) has led to a large yet fragmented body of literature. It is imperative to conduct a systematic analysis and synthesis of this research to identify methodological trends, cl...

Evidence Based Gait Analysis Interpretation Tools (EB-GAIT) treatment recommendation and outcome prediction models to support decision-making based on clinical gait analysis data.

PloS one
Clinical gait analysis (CGA) has historically relied on clinician experience and judgment, leading to modest, stagnant, and unpredictable outcomes. This paper introduces Evidence-Based Gait Analysis Interpretation Tools (EB-GAIT), a novel framework l...

Machine learning for polycyclic aromatic hydrocarbons analysis in roasted lamb: new insights from spectral and chemical data.

Food chemistry
Polycyclic aromatic hydrocarbons (PAHs) generated during lamb roasting pose health risks but are difficult to predict due to their low concentrations and complex features. Existing models fail to address data scarcity and low-concentration prediction...

Tomato ripeness prediction using low resolution portable spectrometer and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Tomato ripeness assessment is critical to ensure optimal product quality. This study proposes a novel approach to predict total soluble solids (TSS) and firmness, and classify tomato ripeness using a low-resolution AS7265x portable spectrometer combi...