AIMC Topic: Vital Signs
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Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.
Computers in biology and medicine
Apr 24, 2019
OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for...
A novel machine-learning-based infection screening system via 2013-2017 seasonal influenza patients' vital signs as training datasets.
The Journal of infection
Feb 22, 2019
Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks.
IEEE transactions on bio-medical engineering
Oct 8, 2018
Missing data is a ubiquitous problem. It is especially challenging in medical settings because many streams of measurements are collected at different-and often irregular-times. Accurate estimation of the missing measurements is critical for many rea...
Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.
BMC emergency medicine
Jul 3, 2018
BACKGROUND: The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algor...
An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.
Journal of the American Heart Association
Jun 26, 2018
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 arrest early, they have limitations, with low sensitivity and high fal...
Early Diagnosis
Diagnosis, Computer-Assisted
Vital Signs
Deep Learning
Heart Arrest
Prognosis
Time Factors
Resuscitation
Retrospective Studies
Inpatients
Seoul
Female
Humans
Predictive Value of Tests
Aged
Reproducibility of Results
Decision Support Techniques
Adult
Risk Factors
Male
Middle Aged
Risk Assessment
Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards.
Artificial intelligence in medicine
Jun 2, 2018
BACKGROUND: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records...
ProFUSO: Business process and ontology-based framework to develop ubiquitous computing support systems for chronic patients' management.
Journal of biomedical informatics
Apr 5, 2018
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...
Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.
BMJ open
Jan 26, 2018
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...
Emergency Service, Hospital
Vital Signs
San Francisco
Boston
Patients' Rooms
Adolescent
Hospital Mortality
ROC Curve
Shock, Septic
Humans
Area Under Curve
Male
Middle Aged
Intensive Care Units
Severity of Illness Index
Female
Length of Stay
Aged
Sepsis
Young Adult
Machine Learning
Adult
Algorithms
Databases, Factual
Prognosis
Retrospective Studies
Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.
Annals of emergency medicine
Sep 6, 2017
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)...
Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.
Methods of information in medicine
Aug 16, 2017
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...
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