AIMC Topic: Vital Signs

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Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury.

The American surgeon Jul 1, 2019
Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learnin...
Humans Machine Learning Middle Aged Logistic Models Creatinine Analysis of Variance Vital Signs Acute Kidney Injury Female Adult Male
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Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer.

JCO clinical cancer informatics Dec 1, 2018
PURPOSE: With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks a...
Female Prognosis Risk Factors Middle Aged Retrospective Studies Humans Survival Rate Neoplasms Electronic Health Records Male Follow-Up Studies Machine Learning Clinical Decision-Making Informatics Algorithms Vital Signs Databases, Factual
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Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2018
OBJECTIVE: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.
Patient Discharge Reference Values Vital Signs Child, Preschool Child Intensive Care Units, Pediatric Regression Analysis Humans Neural Networks, Computer Machine Learning Infant Adolescent
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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Critical care medicine Apr 1, 2018
OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-ti...
Humans Machine Learning Critical Illness Intensive Care Units Middle Aged Comorbidity Severity of Illness Index Sepsis Time Factors Electronic Health Records Female Aged Male Academic Medical Centers Time-to-Treatment Organ Dysfunction Scores Vital Signs Electrocardiography Hospital Mortality Age Factors ROC Curve Blood Pressure Decision Support Systems, Clinical Heart Rate Socioeconomic Factors Sex Factors
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Adding navigation, artificial audition and vital sign monitoring capabilities to a telepresence mobile robot for remote home care applications.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings] Jul 1, 2017
A telepresence mobile robot is a remote-controlled, wheeled device with wireless internet connectivity for bidirectional audio, video and data transmission. In health care, a telepresence robot could be used to have a clinician or a caregiver assist ...
Self-Help Devices Equipment Design Telemedicine Vital Signs Home Care Services Humans Monitoring, Physiologic Robotics
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Machine learning and new vital signs monitoring in civilian en route care: A systematic review of the literature and future implications for the military.

The journal of trauma and acute care surgery Nov 1, 2016
BACKGROUND: Although air transport medical services are today an integral part of trauma systems in most developed countries, to date, there are no reviews on recent innovations in civilian en route care. The purpose of this systematic review was to ...
Software Monitoring, Physiologic Emergency Medical Services Air Ambulances Vital Signs Transportation of Patients Humans Machine Learning
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Visualizing patient journals by combining vital signs monitoring and natural language processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admi...
Patient Admission Vital Signs Heart Arrest Oxygen Respiratory Insufficiency Critical Care Aged Medical Informatics Middle Aged Hospitals Sepsis Monitoring, Physiologic Models, Statistical Denmark Natural Language Processing Blood Pressure Humans Intensive Care Units Hospitalization
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Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Critical care medicine Jul 1, 2016
OBJECTIVE: The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability.
Humans Cohort Studies Blood Pressure Determination Monitoring, Physiologic Supervised Machine Learning Clinical Alarms Heart Rate Oximetry Artifacts Vital Signs Respiratory Rate
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Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Critical care medicine Feb 1, 2016
OBJECTIVE: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter ...
Survival Analysis Vital Signs Heart Arrest Diagnostic Techniques and Procedures Logistic Models Socioeconomic Factors Support Vector Machine Age Factors Models, Statistical Risk Assessment Cohort Studies Time Factors ROC Curve Humans Neural Networks, Computer Machine Learning Intensive Care Units
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Extraction of Vital Signs from Clinical Notes.

Studies in health technology and informatics Jan 1, 2015
Assessment of vital signs is an essential part of surveillance of critically ill patients to detect condition changes and clinical deterioration. While most modern electronic medical records allow for vitals to be recorded in a structured format, the...
Decision Support Systems, Clinical United States Natural Language Processing Data Mining Semantics Humans Physical Examination Biological Ontologies Machine Learning Vital Signs Software Hospitals, Veterans Electronic Health Records Vocabulary, Controlled
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