AIMC Topic: Vital Signs

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Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records.

JCO clinical cancer informatics Feb 1, 2020
PURPOSE: Acute graft-versus-host disease (aGVHD) remains a significant complication of allogeneic hematopoietic cell transplantation (HCT) and limits its broader application. The ability to predict grade II to IV aGVHD could potentially mitigate morb...
Survival Rate Hematologic Neoplasms Male Middle Aged Infant Female Aged Young Adult Adult Humans Follow-Up Studies Machine Learning Graft vs Host Disease Biomarkers Hematopoietic Stem Cell Transplantation Adolescent Prognosis Transplantation, Homologous Child, Preschool Retrospective Studies Vital Signs Child Electronic Health Records ROC Curve Longitudinal Studies
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A Machine Learning-Based Triage Tool for Children With Acute Infection in a Low Resource Setting.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Dec 1, 2019
OBJECTIVES: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hosp...
Prospective Studies Prognosis Female Male Infant Humans Machine Learning Child Mortality Child Severity of Illness Index Sex Factors Hospital Mortality Adolescent Infections Age Factors Rwanda Vital Signs Child, Preschool Triage Developing Countries
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Identifying Patients with Significant Problems Related to Social Determinants of Health with Natural Language Processing.

Studies in health technology and informatics Aug 21, 2019
Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data usi...
Humans Electronic Health Records Natural Language Processing Social Determinants of Health Vital Signs Records
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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...
Analysis of Variance Vital Signs Male Middle Aged Logistic Models Creatinine Humans Machine Learning Acute Kidney Injury Female Adult
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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...
Risk Factors Prognosis Humans Survival Rate Retrospective Studies Follow-Up Studies Machine Learning Male Electronic Health Records Algorithms Informatics Middle Aged Clinical Decision-Making Databases, Factual Vital Signs Neoplasms Female
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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.
Intensive Care Units, Pediatric Regression Analysis Patient Discharge Reference Values Vital Signs Machine Learning Infant Adolescent Child, Preschool Child Humans Neural Networks, Computer
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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 Organ Dysfunction Scores Machine Learning Vital Signs Critical Illness ROC Curve Comorbidity Intensive Care Units Decision Support Systems, Clinical Blood Pressure Severity of Illness Index Electronic Health Records Socioeconomic Factors Heart Rate Sepsis Electrocardiography Female Sex Factors Time Factors Age Factors Aged Academic Medical Centers Hospital Mortality Male Middle Aged Time-to-Treatment
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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 ...
Equipment Design Telemedicine Vital Signs Home Care Services Self-Help Devices 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 ...
Vital Signs Transportation of Patients Software Monitoring, Physiologic Emergency Medical Services Humans Air Ambulances 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...
Sepsis Monitoring, Physiologic Models, Statistical Denmark Humans Patient Admission Natural Language Processing Intensive Care Units Vital Signs Hospitalization Heart Arrest Aged Blood Pressure Middle Aged Oxygen Hospitals Respiratory Insufficiency Critical Care Medical Informatics
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