Intelligent Death Risk Analysis For Patients With Chronic Kidney Disease

Chronic kidney disease(CKD)is a severe chronic disease that threatens the health of people around the world.The research on the death risk of CKD patients with data can assert doctors understand the disease progression and formulate treatment plans.It also improves patient survival rates.Deep learning methods are adept at handling large and diverse datasets and have a wide range of applications in the medical data processing.In this paper,we use this technology to implement intelligent analysis of the mortality risk of CKD patients.This study is grounded on data collected from CKD patients undergoing treatment at multiple Chinese domestic peritoneal dialysis centers between 2016 and 2020.This article aims to utilize deep learning methods to accomplish two tasks.The first task is to establish a risk analysis method for mortality in CKD patients.The second task is to implement a personalized and accurate filling strategy for missing follow-up data of patients,thereby improving the accuracy of mortality prediction analysis.The main research results are as follows:1.A time-series COX model to analyze the risk of death in patients with chronic kidney disease.In this paper,we design a time-series COX model,which combines the proportional hazards model(COX model)with deep learning methods to estimate the endpoint event risk of CKD patients.It also effectively processes initial diagnosis and follow-up patient data.The model is applied to two risk prediction tasks for patients with different loss functions.The analysis of the model-based parameters uncovers the factors associated with the risk of patient death.In the task of assessing patient-death relative risk,C-index scores of the time-series COX model achieve 0.746 and 0.763 for all-cause mortality and cardiovascular disease(CVD)mortality.In the task of the endpoint probability prediction within three months,the time-series COX model has a C-index score of 0.896 and an accuracy of 0.820 for predicting all-cause mortality,while these two indicators reach 0.879 and 0.798 for CVD mortality.Finally,the experiments demonstrate that the time-series COX model can provide personalized and accurate risk predictions.2.A diffusion model for personalized follow-up data filling to improve the accuracy of survival analysis.This study aims to improve the accuracy of survival analysis by efficiently filling in missing follow-up data with a diffusion patient data imputation model.The method maps random distributions to patient feature distributions and fills in missing data by merging generated features with existing patient data.The performance of the filling method is measured using patient follow-up feature distances.In the experiment,the mean squared error of patient follow-up data supplemented by the diffusion patient data imputation model reaches 0.70,and the follow-up feature distance reaches 88.01,outperforming other methods in the comparative experiment.The validity and accuracy of the model are verified through this experiment.After filling in patient data with the diffusion model,the C-index score for all-cause mortality analysis increases to 0.853,improving the accuracy of the patient death-risk analysis model.The two methodologies present for the intelligent processing data of patients suffering from chronic kidney disease.They provide a promising avenue for disease prediction,personalized treatment designation,and disease management.These advancements in healthcare present the potential to offer enhanced medical care for patients,augment the effectiveness of chronic kidney disease treatment,and extend patients’ life expectancy.