AIMC Topic: Humans

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[Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography].

Radiologie (Heidelberg, Germany)
CLINICAL-METHODOLOGICAL PROBLEM: Imaging procedures employing ionizing radiation require compliance with European directives and national regulations in order to protect patients. Each exposure must be indicated, individually adapted, and documented....

Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.

Internal and emergency medicine
This study aimed to develop several new machine learning models based on hibernating myocardium to predict the major adverse cardiac events(MACE) of ischemic left ventricular systolic dysfunction(LVSD) patients receiving either percutaneous coronary ...

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

ESC heart failure
AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes....

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...

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical b...

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...