#4345 NOVEL REAL-TIME PREDICTION SYSTEM FOR HYPOTENSION IN DIALYSIS COMBINING RISK INCIDENCE AND NUMERICAL PREDICTION MODEL
Authors: Chu Jennie Ting Hsuan , Hu Hsiang Wei , Lin Hsuanming , Chen Kuanyu , Un Chi Hin , Wang Jian-An , Liu Chih-Hao 
Published: June 2023

Abstract

Background and Aims
Although there have been many studies on the machine learning prediction model of intradialytic hypotension, the high prediction accuracy in the academic field needs to be transferred to the clinical environment to aid in medical professionals' decision-making process. Before this clinical trial, our team built a data storage, edge computing, and decision support system. However, to achieve real-time decision support, real-time data transmission is also needed along with the system to achieve clinical decision needs. Therefore, this study aims to integrate the AI predictive model with edge computing to build an overall decision support system for intradialytic hypotension in the real-world clinical environment; we have developed a system architecture to deploy the system.

Method
Development of the IDH decision support system: We developed a complete IDH prediction system to be implemented in dialysis centers and hospitals, which incorporates patient data retrieval to AI prediction models to the web application and monitoring dashboard for medical professionals. The system architecture includes a bridge service used to retrieve the HIS database (patient history), DIS database (dialysis information), and NIS database (nursing care). The data is stored in a data warehouse and then converted into a structured format for AI model training and prediction. Furthermore, an API Server, a communication service for the database and front-end web, is used to present the warning signals in real-time. Lastly, there is a multi-bed monitoring dashboard presenting personalized patient information, the incidence rate of hypotension and numerical prediction of the actual blood pressure value.

Design and validation of the online AI prediction model
The real-time AI prediction model is built through automatic machine-learning training, which retrains the model every week, utilizing the data of the past 180 days. The features of the input data include biochemistry lab data, nursing records, demographical characteristics, hemodialysis machine parameters and vital signs; it then uses the SMOTE method to fill in missing values and via RFECV to do feature extraction and selection. Lastly, the model is trained through improved RNN models of LSTM (Long short-term memory) and GRU (Gated Recurrent Unit) integrated with machine learning models of XGboost and LightGBM algorithms to generate a combination of binary classification and numerical prediction. Hypotension's output label is defined when systolic blood pressure drops below 90 mmHg. Finally, the validation of the AI machine learning models for binary classification used accuracy score, specificity and sensitivity, whereas the numerical prediction model used RMSE and MAE.

Results
With the implementation of the edge computing and decision support system, clinical and dialysis data of the patients upload directly to the data warehouse in real-time, and it presents the hypotension prediction information to the medical professional's computer. As a result, the online real-time AI prediction system for the binary classification model yielded an accuracy score, specificity and sensitivity of 0.95, 0.99 and 0.86, respectively, and the RMSE and MAE yielded 17.5 and 11.6, respectively. Furthermore, the model predicted within 15 seconds to ensure a real-time and automatic parameters update.

Conclusion
Introducing and implementing this integrated intelligence AI prediction system reduces the occurrence of hypotension in dialysis, improves patient safety and satisfaction of patients, and enhances the work environment of medical professionals. Furthermore, this framework and validation can also be provided to home dialysis patients to improve their quality of care. In addition, this system architecture can also be extended to other medical conditions in developing intelligent decision support systems for practical clinical applications.