![]() The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545-0.737), and sensitivity and specificity were 34.2% (95% CI 19.6-51.4) and 88.8% (95% CI 85.6-91.6), respectively. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. ![]() This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. Elderly patients are susceptible to postoperative infections with increased mortality.
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