Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUC_adversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.

: Coronavirus Disease 2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids and remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable side effects, minimizing costs, and effectively allocating resources. This study presents a machine learning system capable of identifying patients for whom treatment with corticosteroids or remdesivir is associated with improved survival time. : Gradient boosted decision tree models to predict treatment benefit were trained and tested on data from patients hospitalized at 10 hospitals in the United States between December 18, 2019 and October 18, 2020. 893 patients were treated with remdesivir, and 1,471 were treated with corticosteroids. Models were evaluated for their ability to identify patients that exhibited longer survival times when treated with corticosteroids or remdesivir. Fine and Gray models for the proportional hazard were evaluated comparing treated and untreated patients in the full COVID-19 population, in patients receiving supplemental oxygen, and in patients identified by the algorithm. Inverse probability of treatment weights were used to adjust for confounding. Models for each treatment were trained and tested separately. : .Adult patients (age ≥ 18) were included in this study, with men comprising slightly more than 50% of the sample. After adjusting for confounding, neither corticosteroids nor remdesivir were associated with increased survival time in the full hospitalized COVID-19 population or in the population receiving supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 (p = 0.04) and 0.40 (p = 0.04), respectively : Machine learning methods are capable of identifying hospitalized COVID-19 patients for whom treatment with corticosteroids or remdesivir is associated with an increase in survival time. These methods may help improve patient outcomes and allocate resources during the COVID-19 crisis.