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Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

Journal of clinical monitoring and computing
PURPOSE: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive...

Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.

Cardiology
INTRODUCTION: Heart failure (HF) may induce bowel hypoperfusion, leading to hypoxia of the villa of the bowel wall and the occurrence of Clostridioides difficile infection (CDI). However, the risk factors for the development of CDI in HF patients hav...

Construction and Validation of a General Medical Image Dataset for Pretraining.

Journal of imaging informatics in medicine
In the field of deep learning for medical image analysis, training models from scratch are often used and sometimes, transfer learning from pretrained parameters on ImageNet models is also adopted. However, there is no universally accepted medical im...

Exploring mechanobiology network of bone and dental tissue based on Natural Language Processing.

Journal of biomechanics
Bone and cartilage tissues are physiologically dynamic organs that are systematically regulated by mechanical inputs. At cellular level, mechanical stimulation engages an intricate network where mechano-sensors and transmitters cooperate to manipulat...

A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the ...

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them le...

CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images.

IEEE/ACM transactions on computational biology and bioinformatics
Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been pr...

CASL: Capturing Activity Semantics Through Location Information for Enhanced Activity Recognition.

IEEE/ACM transactions on computational biology and bioinformatics
Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for correspondin...

DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity...

Graph Embedded Ensemble Deep Randomized Network for Diagnosis of Alzheimer's Disease.

IEEE/ACM transactions on computational biology and bioinformatics
Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist in the back propagation (BP) based trained neural networks. Ensemble deep random vector functional link (edRVFL) network utilize the strength of two g...