Observational studyScientific reports2019
Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning.
Observational study in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05127265 (Pervasive Sensing and Artificial Intelligence in Intelligent ICU Subtitles), which is not on this map. Cited by 65 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pervasive Sensing and Artificial Intelligence in Intelligent ICU Subtitles: -Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making -ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Early heart rate variability evaluation enables to predict ICU patients' outcome.Scientific reports · 2022Trial
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Using Wearable Devices to Monitor Activity and Sleep in Inpatients With Parkinson Disease With and Without Delirium: Feasibility and Acceptability Study.Journal of medical Internet research · 2026Article
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Video-based Detection of Delirium in Hospitalized Adults.medRxiv : the preprint server for health sciences · 2026Article
- Video-based detection of Delirium in hospitalized adults.PLOS digital health · 2026Article
- Automated estimation of drain output in postoperative patients using deep learning on clinical images.Scientific reports · 2026Article
- Decision-making for ICU admission: is there a place for AI? Exploring and understanding meaning, experiences, and perspectives.BMC medical ethics · 2026Article
- Stepwise clinical and diagnostic strategy for coma of unknown origin.Intensive care medicine · 2026Review
- Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data.BMC medical informatics and decision making · 2026Article
- Predicting and Early Detection of Delirium through Motion Patterns: A Narrative Review.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2026Review
- Image recognition-based detection system for preventing accidental dislodgement of head-and-neck medical supplies in ICU patients: A feasibility randomized controlled trial.International journal of nursing sciences · 2026Article
- Research progress in agitation early warning monitoring and new technologies for critically ill patients: a narrative review.Frontiers in medicine · 2026Review
- Progress of nursing management for delirium in critically ill geriatric patients: a narrative review.Frontiers in human neuroscience · 2026Review
- Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.Healthcare (Basel, Switzerland) · 2025Review
- Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.JMIR research protocols · 2025Article
- Enhancing Infection Control in ICUS Through AI: A Literature Review.Health science reports · 2025Article
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review.BMC palliative care · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring in the Intensive Care Unit (ICU). As an exemplary prevalent condition, we characterized delirious patients and their environment. We used wearable sensors, light and sound sensors, and a camera to collect data on patients and their environment. We analyzed collected data to detect and recognize patient's face, their postures, facial action units and expressions, head pose variation, extremity movements, sound pressure levels, light intensity level, and visitation frequency. We found that facial expressions, functional status entailing extremity movement and postures, and environmental factors including the visitation frequency, light and sound pressure levels at night were significantly different between the delirious and non-delirious patients. Our results showed that granular and autonomous monitoring of critically ill patients and their environment is feasible using a noninvasive system, and we demonstrated its potential for characterizing critical care patients and environmental factors.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.