Observational studyScientific reports2021
Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.
Observational study in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Physiological Data Analysis Framework for Pain Prediction in Physical Rehabilitation.Sensors (Basel, Switzerland) · 2026Article
- Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients.Nursing in critical care · 2026Article
- Research status, hotspots and perspectives of artificial intelligence applied to pain management: a bibliometric and visual analysis.Updates in surgery · 2025Article
- Automated Deep Learning Approach for Post-Operative Neonatal Pain Detection and Prediction through Physiological Signals.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Continuous visualization and validation of pain in critically ill patients using artificial intelligence: a retrospective observational study.Scientific reports · 2023Observational
- Pain Assessment Disparities by Race, Ethnicity, and Language in Adult Hospitalized Patients.Pain management nursing : official journal of the American Society of Pain Management Nurses · 2023Article
- Artificial intelligence in critical illness and its impact on patient care: a comprehensive review.Frontiers in medicine · 2023Review
- Investigation of Risk Factors for Postoperative Delirium after Transcatheter Aortic Valve Implantation: A Retrospective Study.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were performed in eligible patients using the Critical-Care Pain Observation Tool (CPOT). Three machine-learning methods-random forest (RF), support vector machine (SVM), and logistic regression (LR)-were employed to predict pain using parameters, such as vital signs, age group, and sedation levels. Prediction accuracy was calculated as the harmonic mean of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Furthermore, 117,190 CPOT assessments were performed in 11,507 eligible patients (median age: 65 years; 58.0% males). We found that pain prediction was possible with all three machine-learning methods. RF demonstrated the highest AUROC for the test data (RF: 0.853, SVM: 0.823, and LR: 0.787). With this method, pain can be objectively, continuously, and semi-automatically evaluated in critically ill patients.
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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.