HemoSys: An Integrated Framework for Hemodialysis Machine Operation, Real-Time Monitoring, Data Management and Predictive Modeling for Patient Safety

Abstract

Hemodialysis is of paramount importance to patients who undergo renal failure and kidney dysfunction. It requires precise control and monitoring to ensure the safety of the patient and the efficacy of the process. HemoSys is a high-end framework for the revolutionized operation of hemodialysis machines by real-time monitoring, data management, and predictive modeling. The system will answer all diverse needs related to hemodialysis treatment. It has highly developed peristaltic pumps and syringe pump controllers to manage blood and dialysate flows, assuring cautious fluid management. Advanced sensors are integrated for real-time monitoring of pressure, temperature, and bubble formation that support the continuous tracking of critical parameters of treatment. The Graphical User Interface enables users to easily interact with real-time visualization of sensor data and offers intuitive controls for the hemodialysis operation. Other than the operational capacities, several strong data management features are hosted by HemoSys. All patients, operators, physicians, and sessions data get stored in a MySQL database, thereby providing an efficient retrieval and update of data. The system also offers export options to PDF and excel, thereby enhancing data accessibility and usability. One of the unique features of HemoSys is predictive modeling incorporated in the software framework. In this research, Random Forest, XG Boost, Cat Boost and Light GBM were employed, trained on the hemodialysis patient data of 10352 dialysis sessions, to predict the adequacy of hemodialysis session based on all the relative variables instead of only relying on few variables like Kt/V, Ultrafiltration etc. These models were trained using RFE and PCA methods to find the best feature classification/reduction technique for adequacy prediction.xvii The paired t-test indicated that there was a significant difference between outcome parameters for both in feature reduction techniques and RFE was proved better. And under RFE, the paired t-test was employed on performance matrix (accuracy, precision, F1 score, recall and AUROC) of all the models and there was significant difference between two combinations (RF, XG Boost) and (RF, LGBM). XG Boost and LGBM performed best based on outcome parameters (0.9932, 0.9949, 0.0.9949, 0.9949, 0.9924) and (0.9931, 0.9949, 0.9952, 0.9951, 0.9916) respectively. HemoSys is the step forward to introduce predictive modelling in dialysis technology to provide operators with significant insight to change session parameters for better patient outcomes. This approach increases safety, efficiency, and effectiveness regarding hemodialysis treatment and is therefore a very promising solution for healthcare providers, patients, and all stakeholders.