The proposed LSTM-BLS model predicted the risk of death after cardiovascular interventions with an accuracy of 87.46%, precision of 90.74%, and recall of 93.61%.
Does an improved LSTM-BLS machine learning model accurately predict 6-month postoperative mortality risk in patients undergoing cardiovascular interventions compared to traditional models?
A novel LSTM-BLS machine learning model utilizing a structured cardiovascular interventional database can accurately predict 6-month postoperative mortality, potentially aiding in timely clinical decision-making for high-risk patients.
In order to solve the problems of missing, discontinuous, and unstructured data in past cardiovascular interventional studies, this work constructed a database of specific cardiovascular interventional diseases. Within one year of its implementation in a top-three hospital in Zhejiang Province, the database collected a total of 728 cases of cardiovascular interventional patients, realizing the structuring of patient data and 360° whole-cycle management. With the support of a specific disease database, we proposed an improved LSTM-BLS model to predict the risk of death after cardiovascular interventions. Compared with the traditional long-short term memory (LSTM) model, the parallel learning structure-width learning system (BLS) was introduced to calculate the weights directly, which can solve the problems of overfitting and delay caused by the deep structure of LSTM, so as to improve the accuracy of prediction. The experimental results showed that the accuracy rate of the proposed model was 87.46%, the precision was 90.74%, and recall rate was 93.61%, which can objectively reflect the postoperative death risk of patients, and help doctors to make timely medical intervention for patients with high death risk.
Qi et al. (Sat,) conducted a other in Cardiovascular interventional diseases (n=728). LSTM-BLS model vs. Traditional LSTM, DNN, and RNN models was evaluated on Prediction accuracy of postoperative mortality risk. The proposed LSTM-BLS model predicted the risk of death after cardiovascular interventions with an accuracy of 87.46%, precision of 90.74%, and recall of 93.61%.