Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study

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.

Yapay Zeka ve Sağlıkta Yapay Zekanın Kullanımına Yönelik Sağlık Bilimleri Fakültesi Öğrencilerinin Görüşleri

“…Yapay zekanın geliştirilmesinde özellikle tıbbi hataların önüne geçmek için çalışamalar bulunmaktadır [27][28][29]. Klinik karar desteği [30,31] ve hasta takip uygulamaları [32] da yapay zekanın odaklandığı alanlar arasındadır. Geleceğin sağlık profesyonellerinin üstlendikleri yeni rolleri yerine getirebilmeleri için yenilikçi ve girişimci bakış açısına sahip olmaları önemlidir.…”

Investigation of Risk Factors for Postoperative Delirium after Transcatheter Aortic Valve Implantation: A Retrospective Study

“…For patients with AS exhibiting a narrowed AVA, the risk of POD should be considered. Additionally, emphasis should be placed on nutrition, the maintenance of anesthesia depth, the selection of non-narcotic analgesics, early POD monitoring, pain control, and early postoperative non-athletic interventions, such as exercise rehabilitation and cognitive training [ 29 , 30 ].…”

Airway Obstruction Caused by Sputa in Heat and Moisture Exchange Filter During Ventilation Using Supra-Laryngeal Mask Airway: A Case Report

“…When these positions are used with LMA, we recommend being cautious about airway obstructions and considering the possibility of HMEF obstruction. This is one of the basic troubleshooting steps, but like checking vital signs (12], it must be checked because it directly affects the life of the patient.…”