Electrocardiograph Positioning on Intubation of Peripherally Inserted Central Catheter and Nursing for Patients with Multiple Myeloma under Deep Learning

Abstract

This study was to explore the deep learning-based electrocardiograph (ECG) positioning for peripherally inserted central catheter (PICC) patients with multiple myeloma (MM) and provide theoretical guidance for clinical application. In this study, 70 patients with MM were selected as the research object and randomly divided into two groups, 35 cases in each group. The experimental duration was followed for one year. The efficiency of catheterization under the recurrent neural network (RNN) algorithm and the traditional method was evaluated through the operation time. The effects of catheterization were determined by the infection rates after catheterization. Results. The time required for PICC catheterization under the guidance of RNN algorithm was 25.6 ± 4.8 min. The time required for PICC catheter surgery under the traditional method was 66.2 ± 5.3 min, which was significantly different from the time required for surgery under the guidance of RNN algorithm (P < 0.05). After one year of tracking, under the guidance of RNN algorithm, the cumulative number of infected patients in every two months after PICC catheterization in 35 patients was 0, 0, 0, 0, 1, and 1, respectively. The number of infected patients in the other 35 patients under the traditional method was 0, 0, 0, 1, 2, and 2, respectively. In summary, PICC catheter surgery guided by artificial intelligence algorithm based on RNN neural network required less time and had lower risk of postoperative infection. According to the previous experience, we have summarized the nursing methods after catheterization. In this experiment, compared with the patients who did not follow the doctor's advice, the 70 patients who followed the doctor's advice obtained better therapeutic effects. Postoperative care can ensure the therapeutic effects.