scholarly journals Author’s Response to Peer Reviews of “Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study” (Preprint)

2021 ◽  
Author(s):  
Riccardo Doyle
Keyword(s):  
Machine Learning ◽  
Observational Study ◽  
Peer Review ◽  
Retrospective Observational Study ◽  
Peer Reviews ◽  
Patient Prognosis

UNSTRUCTURED These are authors' responses to peer review of ms#29392.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naoya Kobayashi ◽  
Takuya Shiga ◽  
Saori Ikumi ◽  
Kazuki Watanabe ◽  
Hitoshi Murakami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Observational Study ◽  
Critically Ill ◽  
Pain Assessment ◽  
Critically Ill Patients ◽  
Vital Signs ◽  
Retrospective Observational Study ◽  
Learning Methods ◽  
Machine Learning Methods

AbstractMonitoring 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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