AI Medical Compendium Journal:
IEEE journal of translational engineering in health and medicine

Showing 31 to 40 of 40 articles

Predicting the Travel Distance of Patients to Access Healthcare Using Deep Neural Networks.

IEEE journal of translational engineering in health and medicine
OBJECTIVE: Improving geographical access remains a key issue in determining the sufficiency of regional medical resources during health policy design. However, patient choices can be the result of the complex interactivity of various factors. The aim...

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography.

IEEE journal of translational engineering in health and medicine
Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients ...

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure.

IEEE journal of translational engineering in health and medicine
Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between ...

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization.

IEEE journal of translational engineering in health and medicine
Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by...

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

IEEE journal of translational engineering in health and medicine
OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less deve...

Autonomous Robot for Removing Superficial Traumatic Blood.

IEEE journal of translational engineering in health and medicine
: To remove blood from an incision and find the incision spot is a key task during surgery, or else over discharge of blood will endanger a patient's life. However, the repetitive manual blood removal involves plenty of workload contributing fatigue ...

Predictive Monitoring of Critical Cardiorespiratory Alarms in Neonates Under Intensive Care.

IEEE journal of translational engineering in health and medicine
We aimed at reducing alarm fatigue in neonatal intensive care units by developing a model using machine learning for the early prediction of critical cardiorespiratory alarms. During this study in over 34,000 patient monitoring hours in 55 infants 27...

High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.

IEEE journal of translational engineering in health and medicine
INTRODUCTION: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of...

Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network.

IEEE journal of translational engineering in health and medicine
OBJECTIVE: Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported that most of PD patients have voice impairments. But these voice impairments are not perceptible to common listeners. Therefore, different machine learning me...

Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients.

IEEE journal of translational engineering in health and medicine
This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. A spectroscopic setup was prepared for acquisition of blood absorbance spectrum and tested in an op...