AIMC Topic: Machine Learning

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Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective.

Expert review of clinical immunology
INTRODUCTION: Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can i...

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions.

Colloids and surfaces. B, Biointerfaces
Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical stu...

Hybrid multimodal fusion for graph learning in disease prediction.

Methods (San Diego, Calif.)
Graph neural networks (GNNs) have gained significant attention in disease prediction where the latent embeddings of patients are modeled as nodes and the similarities among patients are represented through edges. The graph structure, which determines...

Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability.

CPT: pharmacometrics & systems pharmacology
We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical pur...

Machine learning-based life cycle assessment for environmental sustainability optimization of a food supply chain.

Integrated environmental assessment and management
Effective resource allocation in the agri-food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted ...

Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach.

NeuroImage
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed...

Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning.

Computers in biology and medicine
INTRODUCTION: Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including postpartum hemorrhage. Despite its significance,...

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects.

Computers in biology and medicine
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual d...

Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling.

Analytical chemistry
RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information...

Soil data recency: The foundation for harmonizing soil data across time.

Journal of environmental management
Sustainable soil resource management depends on reliable soil information, often derived from 'legacy soil data' or a combination of old and new soil data. However, the task of harmonizing soil data collected at different times remains a largely unex...