CovRNN - A recurrent neural network model for predicting outcomes of COVID-19 patients: model development and validation using EHR data

BackgroundPredicting outcomes of COVID-19 patients at an early stage is critical for optimized clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, based on the need for extensive data pre-processing and feature engineering, these models have not been validated or implemented outside of the original study site. MethodsIn this study, we propose CovRNN, recurrent neural network (RNN)-based models to predict COVID-19 patients outcomes, using their available electronic health record (EHR) data on admission, without the need for specific feature selection or missing data imputation. CovRNN is designed to predict three outcomes: in-hospital mortality, need for mechanical ventilation, and long length of stay (LOS >7 days). Predictions are made for time-to-event risk scores (survival prediction) and all-time risk scores (binary prediction). Our models were trained and validated using heterogeneous and de-identified data of 247,960 COVID-19 patients from 87 healthcare systems, derived from the Cerner(R) Real-World Dataset (CRWD). External validation was performed using three test sets (approximately 53,000 patients). Further, the transferability of CovRNN was validated using 36,140 de-identified patients data derived from the Optum(R) de-identified COVID-19 Electronic Health Record v. 1015 dataset (2007-2020). FindingsCovRNN shows higher performance than do traditional models. It achieved an area under the receiving operating characteristic (AUROC) of 93% for mortality and mechanical ventilation predictions on the CRWD test set (vs. 91{middle dot}5% and 90% for light gradient boost machine (LGBM) and logistic regression (LR), respectively) and 86.5% for prediction of LOS > 7 days (vs. 81{middle dot}7% and 80% for LGBM and LR, respectively). For survival prediction, CovRNN achieved a C-index of 86% for mortality and 92{middle dot}6% for mechanical ventilation. External validation confirmed AUROCs in similar ranges. InterpretationTrained on a large heterogeneous real-world dataset, our CovRNN model showed high prediction accuracy, good calibration, and transferability through consistently good performance on multiple external datasets. Our results demonstrate the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering.

Psychological Training of Self Consistency and Congruence in Army Men / 中国心理卫生杂志

Objective:To explore the effect of psychological training on self consistency and congruence of army men. Methods:532 army men were tested by Self Consistency and Congruence Scale(SCCS)、EPQ、simplified coping style questionnaire(SCSQ)and SCL-90. Results:There were significant difference in score of SCCS between army men group and norm.There were significant correlation between factor scores of SCCS and scores of EPQ,SCSQ and SCL-90. After psychological training,the study group had higher score in flexibility than that of norm. The study group had higher score in flexibility and positive coping style than control group,and lower scores in P and N of EPQ,total score,obsessive-compulsive,interpersonal sensitivity,anxiety,hostility,paranoid ideation and psychoticism of SCL-90 than control. Conclusion:Self consistency and congruence level of army men were associated with personality, coping style and mental health. The psychological training has effect on balancing personality,facilitating self consistency,improving coping style and the psychological status of army men.