AIMC Topic: Machine Learning

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Artificial intelligence prediction model for readmission after DIEP flap breast reconstruction based on NSQIP data.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
BACKGROUND: Readmissions following deep inferior epigastric perforator (DIEP) flap breast reconstruction represent a significant healthcare burden, yet current risk prediction methods lack precision in identifying high-risk patients. We developed a m...

Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin.

Computer methods and programs in biomedicine
BACKGROUND: It is unclear, whether photoplethysmography (PPG) waveforms from wearable devices can differentiate between supraventricular and ventricular arrhythmias. We assessed, whether a neural network-based classifier can distinguish the origin of...

X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head.

Scientific reports
This study aimed to develop an X-ray radiomics model for predicting collapse of early-stage osteonecrosis of the femoral head (ONFH). A total of 87 patients (111 hips; training set: n = 67, test set: n = 44) with non-traumatic ONFH at Association Res...

Machine learning-based return-to-work assessment system for acute myocardial infarction patients within 12 months.

Heart & lung : the journal of critical care
BACKGROUND: Returning to work is a critical indicator of recovery after acute myocardial infarction (AMI), and accurate identification of patients with low return-to-work rates is critical for timely intervention.

SPR-based refractive index sensor design with grated Au-ZnS for dengue detection using machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this paper, we propose a Surface Plasmon Resonance (SPR) fiber optic refractive index (RI) sensor. It consists of a multi-mode fiber (MMF) sensor with a bi-metallic nanostructure with Gold (Au) and Zinc Sulphide (ZnS) as the plasmonic sensing laye...

Interpretable machine learning model for prediction functional cure in chronic hepatitis B patients receiving Peg-IFN therapy: A multi-center study.

International journal of medical informatics
BACKGROUND: Functional cure is the ideal treatment goal for chronic hepatitis B (CHB) treatment. We developed and validated machine learning (ML) models to predict functional cure in CHB patients.

Machine learning-assisted strategies to enhance the mechanical properties of PVA hydrogels.

Journal of the mechanical behavior of biomedical materials
Polyvinyl alcohol (PVA) hydrogels have garnered increasing interest in the field of biomedical materials due to their excellent biocompatibility and controllable mechanical properties. Although various preparation strategies such as freeze-thaw cycle...

QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction.

Molecular diversity
Predicting molecular and quantum material properties, especially the band gap, is crucial for accelerating discoveries in drug design and material science. Although graph neural networks and probabilistic encoders are well established in molecular da...

Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.

Computer methods and programs in biomedicine
Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys ...

Protocol of the pilot study to test and evaluate the iCARE tool: a machine learning-based e-platform tool to make health prognoses and support decision-making for the care of older persons with complex chronic conditions.

BMJ open
INTRODUCTION: The provision of optimal care for older adults with complex chronic conditions (CCCs) poses significant challenges due to the interplay of multiple medical, pharmacological, functional and psychosocial factors. To address these challeng...