Jing He
ABSTRACT
Elderly patients have low surgical tolerance, weak postoperative basic metabolism and recovery ability, often accompanied by chronic diseases, which not only increases the risk of perioperative complications, but also improves the mortality of patients. Clinically, APACHE II, possum, ASA, SRS, TS, MODS, NNIS and other scales are usually used for preoperative risk assessment. The existing quantitative evaluation methods are easily affected by the patients' own diseases, fail to focus to the elderly patients, and cannot consider all the influencing factors before, during and after operation. Machine learning model has the advantages of strong robustness and excellent generalization ability, which has been widely used in medical aided diagnosis. Therefore, based on the literature analysis and combined with the existing quantitative scoring methods and statistical analysis methods, the study extracts and screens out the elements of the clinical evaluation model, then constructs the perioperative risk evaluation model of elderly patients by the machine learning method, which can refer to not only the operation implementation process and recent post-operative factors, but also the basic state of patients, chronic diseases history, operation difficulty, degree of anesthesia, degree of recovery, etc. In test, the ANN model has the highest accuracy and recall in the prediction of perioperative complications in elderly patients, 96.77 percent and 98.33 percent respectively, which is better than 85.48 percent and 68.33 percent of the traditional possum method. The accuracy and recall rate of SVM model in predicting perioperative death of elderly patients are the highest, which can reach 84.56 percent and 90.48 percent respectively, which are better than APACHE II, ASA and P-POSSUM evaluation models. In study, the machine learning model has a good prediction effect in the perioperative risk assessment for elderly patients, and can also be used as an auxiliary decision-making tool for clinical perioperative risk assessment.
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