AIMC Topic: Vital Signs

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A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series.

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...
Male Aging Middle Aged Sepsis Baroreflex Aged Aged, 80 and over Young Adult Adult Critical Care Vital Signs Monitoring, Physiologic Models, Statistical Area Under Curve Signal Processing, Computer-Assisted Humans Machine Learning Algorithms Databases, Factual Female
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Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.

PloS one Aug 17, 2016
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...
Critical Care Cohort Studies Early Diagnosis Algorithms Models, Theoretical Vital Signs Hematologic Neoplasms Prognosis Treatment Outcome Heart Arrest Male Monitoring, Physiologic Retrospective Studies Middle Aged Electronic Health Records Adolescent Female Humans Aged Neural Networks, Computer Aged, 80 and over Machine Learning Young Adult Adult
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Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.

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 ...
Vital Signs Hospital Information Systems Aged Oscillometry Decision Support Systems, Clinical Humans Clinical Alarms Adult Data Interpretation, Statistical Monitoring, Physiologic Machine Learning Male Blood Pressure Data Mining Algorithms Middle Aged Area Under Curve Heart Rate Female Artifacts Risk
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Clinical evaluation of the newly developed infectious disease/fever screening radar system using the neural network and fuzzy grouping method for travellers with suspected infectious diseases at Narita International Airport Clinic.

The Journal of infection Sep 28, 2015
Travel Fever Airports Vital Signs Communicable Disease Control Radar Aged Young Adult Adult Male Middle Aged Adolescent Mass Screening Fuzzy Logic Cluster Analysis Humans Neural Networks, Computer Algorithms Female
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An electronic medical record system with treatment recommendations based on patient similarity.

Journal of medical systems Mar 12, 2015
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...
China Vital Signs Health Information Exchange Socioeconomic Factors Humans Decision Making, Computer-Assisted Age Factors Machine Learning Diagnostic Techniques and Procedures Sex Factors Medical Records Systems, Computerized
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Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury.

Computers in biology and medicine Nov 15, 2014
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 (...
Humans Artificial Intelligence Databases, Factual Brain Injuries Vital Signs Predictive Value of Tests Trauma Severity Indices
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GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV.

Studies in health technology and informatics May 15, 2025
Temporal missingness, defined as unobserved patterns in time series, and its predictive potentials represent an emerging area in clinical machine learning. We trained a gated recurrent unit with decay mechanisms, called GRU-D, for a binary classifica...
Machine Learning Female Aged Male Vital Signs Humans
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Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.

BMC emergency medicine May 13, 2025
BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEM...
Adult Male Middle Aged Emergency Medical Services Registries Retrospective Studies Female Aged Aged, 80 and over Air Ambulances Japan Vital Signs Humans Machine Learning
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A Comparison of Five Algorithmic Methods and Machine Learning Pattern Recognition for Artifact Detection in Electronic Records of Five Different Vital Signs: A Retrospective Analysis.

Anesthesiology Jul 1, 2024
BACKGROUND: Research on electronic health record physiologic data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized that different artifact detection a...
Female Aged Male Middle Aged Artifacts Pattern Recognition, Automated Vital Signs Machine Learning Algorithms Retrospective Studies Humans Electronic Health Records
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A novel method for conformity assessment testing of patient monitors for post-market surveillance purposes.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2023
BACKGROUND: Patient monitors are medical devices used to monitor vital parameters such as heart rate, respiratory rate, blood pressure, blood oxygen saturation, and body temperature during inpatient treatment. As such, patient monitors provide physic...
Humans Artificial Intelligence Reproducibility of Results Blood Pressure Heart Rate Vital Signs
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