We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology, and End Results Program. We provide...
This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of...
As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noi...
This article describes a methodology to recognize emotional states through an electroencephalography signals analysis, developed with the premise of reducing the computational burden that is associated with it, implementing a strategy that reduces th...
This paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressure through the correlation of variables (age, BMI, exercise level, alcohol consumption level, smokin...
Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentiall...
This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set...
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for pri...
Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group of interdependent metabolic threats that contribute to the emergence of diseases that can lead to death. This study was designed to better predict th...
Explore deep learning applications in predictive analytics for public health data, identify challenges and trends, and then understand the current landscape. A systematic literature review was conducted in June 2023 to search articles on public hea...