AIMC Topic: Vital Signs

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Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning.

Physiological measurement Jul 30, 2025
The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical e...
Vital Signs Humans Machine Learning Intensive Care Units Female Aged Male Middle Aged Sepsis
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Bias in vital signs? Machine learning models can learn patients' race or ethnicity from the values of vital signs alone.

BMJ health & care informatics Jul 10, 2025
OBJECTIVES: To investigate whether machine learning (ML) algorithms can learn racial or ethnic information from the vital signs alone.
Humans Intensive Care Units Female Aged Adult Male Machine Learning Algorithms Retrospective Studies Critical Illness Heart Rate Vital Signs Racial Groups Respiratory Rate Middle Aged United States Bias Ethnicity
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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.
Middle Aged Brain Injuries, Traumatic Vital Signs Female Aged Adult Male Humans Algorithms Prognosis Intensive Care Units
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A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study.

PLoS medicine Apr 29, 2025
BACKGROUND: Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinical characteristics.
Vital Signs Risk Assessment Postoperative Complications Deep Learning Predictive Value of Tests Humans Algorithms Acute Kidney Injury Aged Male Middle Aged Female Risk Factors
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A comparative study of neural network architectures for vital signs monitoring based on the national early warning systems (NEWS).

Health informatics journal Apr 25, 2025
The study aims to assess the efficacy of various neural network architectures in predicting the National Early Warning Systems (NEWS) score, using vital signs, to enhance early warning and monitoring in clinical settings. A comparative evaluation o...
Monitoring, Physiologic Discriminant Analysis Vital Signs Early Warning Score Humans Neural Networks, Computer Support Vector Machine
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Early prediction of shock in intensive care unit patients by machine learning using discrete electronic health record data.

Journal of critical care Apr 22, 2025
PURPOSE: To use machine learning to predict new-onset shock for at-risk intensive care unit (ICU) patients based on discrete vital sign data from the electronic health record.
Electronic Health Records Female Hospitalization Aged Humans Hospital Mortality Machine Learning Logistic Models Male Intensive Care Units Predictive Value of Tests Middle Aged Shock Risk Assessment Vital Signs Time Factors
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Interpretable machine learning for predicting sepsis risk in emergency triage patients.

Scientific reports Jan 6, 2025
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...
Humans Machine Learning Algorithms Retrospective Studies Electronic Health Records Vital Signs Female Risk Assessment Sepsis Aged Triage Adult Emergency Service, Hospital Male Middle Aged
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A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.

Annals of laboratory medicine Dec 13, 2024
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles...
Vital Signs Heart Arrest Male Middle Aged ROC Curve Humans International Classification of Diseases Machine Learning Hospitalization Retrospective Studies Aged Intensive Care Units Aged, 80 and over Female Adult
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Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

IEEE journal of biomedical and health informatics Sep 5, 2024
The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed...
Adult Prognosis Humans Signal Processing, Computer-Assisted Intensive Care Units Male Machine Learning Middle Aged Critical Care Female Algorithms Deep Learning Aged Vital Signs Stochastic Processes
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Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2024
Health monitoring embedded with intelligence is the demand of the day. In this era of a large population with the emergence of a variety of diseases, the demand for healthcare facilities is high. Yet there is scarcity of medical experts, technicians ...
Middle Aged Female Humans Monitoring, Physiologic Young Adult Vital Signs Machine Learning Internet of Things Adult Algorithms Stress, Physiological Male Forecasting
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