Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient
monitoring (RPM) is one of the common healthcare applications that assist doctors to …
monitoring (RPM) is one of the common healthcare applications that assist doctors to …
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Objectives The objective of this study was to compare performance of logistic regression
(LR) with machine learning (ML) for clinical prediction modeling in the literature. Study …
(LR) with machine learning (ML) for clinical prediction modeling in the literature. Study …
Logistic regression was as good as machine learning for predicting major chronic diseases
Objective To evaluate the performance of machine learning (ML) algorithms and to compare
them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs) …
them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs) …
Role of Big Data Analytics in supply chain management: current trends and future perspectives
It is a widely accepted fact that almost every research or business revolves around Data.
Data from various business sectors has been growing sharply and the management of this …
Data from various business sectors has been growing sharply and the management of this …
Predicting suicide attempts and suicide deaths following outpatient visits using electronic health records
Objective: The authors sought to develop and validate models using electronic health
records to predict suicide attempt and suicide death following an outpatient visit. Method …
records to predict suicide attempt and suicide death following an outpatient visit. Method …
Emergency department triage prediction of clinical outcomes using machine learning models
Background Development of emergency department (ED) triage systems that accurately
differentiate and prioritize critically ill from stable patients remains challenging. We used …
differentiate and prioritize critically ill from stable patients remains challenging. We used …
Predicting hospital admission at emergency department triage using machine learning
Objective To predict hospital admission at the time of ED triage using patient history in
addition to information collected at triage. Methods This retrospective study included all adult …
addition to information collected at triage. Methods This retrospective study included all adult …
Prediction of diabetes using machine learning algorithms in healthcare
There are several machine learning techniques that are used to perform predictive analytics
over big data in various fields. Predictive analytics in healthcare is a challenging task but …
over big data in various fields. Predictive analytics in healthcare is a challenging task but …
Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
Background Early and accurate identification of sepsis patients with high risk of in-hospital
death can help physicians in intensive care units (ICUs) make optimal clinical decisions …
death can help physicians in intensive care units (ICUs) make optimal clinical decisions …
An algorithm based on deep learning for predicting in‐hospital cardiac arrest
Background In‐hospital cardiac arrest is a major burden to public health, which affects
patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac …
patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac …