AIMC Topic: Vital Signs
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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...
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 (...
Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.
Journal of medical Internet research
Jul 3, 2025
BACKGROUND: Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously optimizing treatment strategies to improve survival rates.
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...
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...
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...
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...
[The future patient monitoring in the bed ward].
Ugeskrift for laeger
Aug 29, 2022
Current monitoring of vital signs in hospital wards rely on infrequent manual measurements. This narrative review describes how new wearable devices with artificial intelligence interpretation may overcome this challenge by providing nurses with cont...
Validation of a Machine Learning Model for Early Shock Detection.
Military medicine
Jan 4, 2022
OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity...
A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.
Shock (Augusta, Ga.)
Jan 1, 2022
Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Eme...
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