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    Machine learning suggests sleep as a core factor in chronic pain. Miettinen Teemu,Mäntyselkä Pekka,Hagelberg Nora,Mustola Seppo,Kalso Eija,Lötsch Jörn Pain Patients with chronic pain have complex pain profiles and associated problems. Subgroup analysis can help identify key problems. We used a data-based approach to define pain phenotypes and their most relevant associated problems in 320 patients undergoing tertiary pain management. Unsupervised machine learning analysis of parameters "pain intensity," "number of pain areas," "pain duration," "activity pain interference," and "affective pain interference," implemented as emergent self-organizing maps, identified 3 patient phenotype clusters. Supervised analyses, implemented as different types of decision rules, identified "affective pain interference" and the "number of pain areas" as most relevant for cluster assignment. These appeared 698 and 637 times, respectively, in 1000 cross-validation runs among the most relevant characteristics in an item categorization approach in a computed ABC analysis. Cluster assignment was achieved with a median balanced accuracy of 79.9%, a sensitivity of 74.1%, and a specificity of 87.7%. In addition, among 59 demographic, pain etiology, comorbidity, lifestyle, psychological, and treatment-related variables, sleep problems appeared 638 and 439 times among the most important characteristics in 1000 cross-validation runs where patients were assigned to the 2 extreme pain phenotype clusters. Also important were the parameters "fear of pain," "self-rated poor health," and "systolic blood pressure." Decision trees trained with this information assigned patients to the extreme pain phenotype with an accuracy of 67%. Machine learning suggested sleep problems as key factors in the most difficult pain presentations, therefore deserving priority in the treatment of chronic pain. 10.1097/j.pain.0000000000002002
    Prognostic Assessment of COVID-19 in ICU by Machine Learning Methods: A Retrospective Study. Pan Pan,Li Yichao,Xiao Yongjiu,Han Bingchao,Su Mingliang,Li Yansheng,Zhang Siqi,Jiang Dapeng,Chen Xia,Zhou Fuquan,Ma Ling,Bao Pengtao,Su Longxiang,Xie Lixin Journal of medical Internet research BACKGROUND:Patients with coronavirus disease (COVID-19) in ICU have a high mortality rate, and how to early assess the prognosis and carry out precise treatment is of great significance. OBJECTIVE:To use machine learning to construct a model for the analysis of risk factors and prediction of death among ICU patients with COVID-19. METHODS:In this retrospective study, 123 COVID-19 patients inthe ICU of Vulcan Hill Hospital were selected from the database, and data were randomly divided into a training data set (n = 98) and test data set (n = 25) with a 4:1 ratio. Significance tests, analysis of correlation and factor analysis were used to screen the 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of COVID-19 patients in ICU. Performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Model interpretation and model evaluation of the risk prediction model, such as calibration curve, SHAP, LIME, etc., were performed to ensure its stability and reliability.The outcome is based on the ICU death recorded from the database. RESULTS:Layer-by-layer screening of 100 potential risk factors finallyrevealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage (LYM%), prothrombin time (PT), lactate dehydrogenase (LDH), total bilirubin (T-Bil), percentage of eosinophils (EOS%), creatinine(Cr), neutrophil percentage (NEUT%), albumin (ALB) level. Finally, eXtreme Gradient Boosting (XGBoost) established by 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model has been translated into an online risk calculator that is freely available for the public usage ( http://114.251.235.51:1226/index). CONCLUSIONS:The 8 factors XGBoost model predicts risk of death in ICU patients with COVID-19 well,which initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients. CLINICALTRIAL: 10.2196/23128