[HTML][HTML] Artificial intelligence in healthcare: A bibliometric analysis
BL Jimma - Telematics and Informatics Reports, 2023 - Elsevier
Background The implementation of artificial intelligence technology in health care improves
disease prediction, classification, and diagnosis, benefiting both patients and healthcare …
disease prediction, classification, and diagnosis, benefiting both patients and healthcare …
[HTML][HTML] Artificial intelligence in health care: bibliometric analysis
Background As a critical driving power to promote health care, the health care–related
artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis …
artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis …
[HTML][HTML] Computational methods directed towards drug repurposing for COVID-19: advantages and limitations
Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic
status of countries. Although vaccines are now available against the severe acute …
status of countries. Although vaccines are now available against the severe acute …
DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence
Abstract Background and Objective Artificial intelligence (AI) has several uses in the
healthcare industry, some of which include healthcare management, medical forecasting …
healthcare industry, some of which include healthcare management, medical forecasting …
Lung cancer survival period prediction and understanding: Deep learning approaches
Introduction Survival period prediction through early diagnosis of cancer has many benefits.
It allows both patients and caregivers to plan resources, time and intensity of care to provide …
It allows both patients and caregivers to plan resources, time and intensity of care to provide …
Explainable prediction of chronic renal disease in the colombian population using neural networks and case-based reasoning
GR Vásquez-Morales, SM Martinez-Monterrubio… - Ieee …, 2019 - ieeexplore.ieee.org
This paper presents a neural network-based classifier to predict whether a person is at risk
of developing chronic kidney disease (CKD). The model is trained with the demographic …
of developing chronic kidney disease (CKD). The model is trained with the demographic …
Optimization of prediction method of chronic kidney disease using machine learning algorithm
Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most
important problems of mortality rate in the medical sector nowadays. Based on this critical …
important problems of mortality rate in the medical sector nowadays. Based on this critical …
[HTML][HTML] A comparative analysis of machine learning models: a case study in predicting chronic kidney disease
In the modern world, chronic kidney disease is one of the most severe diseases that
negatively affects human life. It is becoming a growing problem in both developed and …
negatively affects human life. It is becoming a growing problem in both developed and …
[PDF][PDF] Chronic kidney disease prediction using machine learning models
The field of biosciences have advanced to a larger extent and have generated large
amounts of information from Electronic Health Records. This have given rise to the acute …
amounts of information from Electronic Health Records. This have given rise to the acute …
[HTML][HTML] A deep learning approach for kidney disease recognition and prediction through image processing
Chronic kidney disease (CKD) is a gradual decline in renal function that can lead to kidney
damage or failure. As the disease progresses, it becomes harder to diagnose. Using routine …
damage or failure. As the disease progresses, it becomes harder to diagnose. Using routine …