摘要：Background: Some patients discharged automatically are classified as terminal discharge, while their clinical outcome is survival, disrupting the results of clinical research. Methods: The data of this study were taken from inpatients admitted to the ICU of the First Medical Center of the People's Liberation Army General Hospital, Beijing, China from 2008-2017. We collected the data regarding medications used over the three days before discharge from the group of patients who survived and the group of patients who died, and the outcomes of all patients were recalculated by three classification algorithms (AdaBoosting, Pearson correlation coefficient, observed to expected ratio-weighted cosine similarity). Our basic assumption is that if the classification result is death but the actual in-hospital outcome is survival, the associated patient was likely terminally discharged. Results: The coincidence rate of the outcomes calculated by the AdaBoosting algorithm was 98.1%, the coincidence rate calculated by the Pearson correlation coefficient was 61.1%, and the coincidence rate calculated by the observed to expected ratio-weighted cosine similarity was 93.4%. When the three classification methods were combined, the accuracy reached 98.56%. Conclusion: The combination of clinical rules and classification methods has a synergistic effect on judgments of patients’ discharge outcomes, greatly saving time on manual retrieval and reducing the negative influence of statistics or rules.
关键词：Intensive care units;Terminal discharge;Machine learning
