A Quality Control Model for Lung Cancer Based on A Standardized Treatment Mode

In the medical field, quality of care is one of the most important goals, and only by achieving good quality of care can we provide better health services to the society. Therefore, it is particularly important to implement quality control of the diagnosis and treatment process. In this paper, we propose a lung cancer quality control model based on the standardized judgment of the treatment model to address the requirements of lung cancer treatment quality control, the complex content of medical text data, and the irregular recording time. First, the word vector is trained by BERT pre-training model; then, the K-medoids algorithm is used to mine the standardized treatment patterns, and the time-aware LSTM unit is used to deal with the uneven input interval on this basis; finally, the attention mechanism is introduced to highlight the different contributions of the visit days to complete the prediction of the next cycle of lung cancer treatment items. The experimental results showed that the recall and average accuracy of the BERT-TLSTM-ATT fusion model reached 0.895 and 0.782, respectively, which performed well compared with other models and effectively controlled the quality of next-cycle treatment, achieving the goal of quality control of all aspects of lung cancer treatment.