New advances in telemedicine, ubiquitous computing, and artificial intelligence have supported the emergence of more advanced applications and support systems for chronic patients. This trend addresses the important problem of chronic illnesses, high...
OBJECTIVES: We validate a machine learning-based sepsis-prediction algorithm () for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specifi...
STUDY OBJECTIVE: Standards for emergency department (ED) triage in the United States rely heavily on subjective assessment and are limited in their ability to risk-stratify patients. This study seeks to evaluate an electronic triage system (e-triage)...
OBJECTIVE: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural langua...
IEEE journal of biomedical and health informatics
Dec 7, 2016
Physiological variables, such as heart rate (HR), blood pressure (BP) and respiration (RESP), are tightly regulated and coupled under healthy conditions, and a break-down in the coupling has been associated with aging and disease. We present an appro...
INTRODUCTION: Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used r...
Journal of clinical monitoring and computing
Oct 5, 2015
Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on ...
As the core of health information technology (HIT), electronic medical record (EMR) systems have been changing to meet health care demands. To construct a new-generation EMR system framework with the capability of self-learning and real-time feedback...
Permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (...
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