Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably
نویسندگان
چکیده
Objective The objective of the study was to compare performance logistic regression and boosted trees for predicting patient mortality from large sets diagnosis codes in electronic healthcare records. Study Design Setting We analyzed national hospital records official death patients with myocardial infarction (n = 200,119), hip fracture 169,646), or colorectal cancer surgery 56,515) England 2015–2017. One-year predicted age, sex, socioeconomic status, 202 257 International Classification Diseases 10th Revision recorded preceding year not (binary predictors). Performance measures included c-statistic, scaled Brier score, several calibration. Results 17.2% (34,520) after infarction, 27.2% (46,115) fracture, 9.3% (5,273) surgery. Optimism-adjusted c-statistics models were 0.884 (95% confidence interval [CI]: 0.882, 0.886), 0.798 (0.796, 0.800), 0.811 (0.805, 0.817). equivalent tree 0.891 CI: 0.889, 0.892), 0.804 (0.802, 0.806), 0.803 (0.797, 0.809). Model also similar when measured using scores. All well calibrated overall. Conclusion In datasets records, numerous comparably.
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ژورنال
عنوان ژورنال: Journal of Clinical Epidemiology
سال: 2021
ISSN: ['1878-5921', '0895-4356']
DOI: https://doi.org/10.1016/j.jclinepi.2020.12.018