Alarms Early Detection in Dialytic Therapies via Machine Learning Models

Hemodialysis (HD) is a clinical treatment for patients affected by Chronic Kidney Disease (CKD). The goal of a treatment is to purify the patient’s blood using dialysis machines, devices that act as artificial kidneys. However, a common problem is the alteration of the patient’s health status due to side effects or to machine malfunctions that may occur during treatment. A dialysis machine is a complex apparatus consisting of a control system of several quantities (e.g., pressure, flow rate, temperature, conductivity, etc.) capable of alerting medical operators when an alarm occurs. In the present work, a Machine Learning (ML) predictive model able to act in advance with respect to the dialysis alarm system was developed. Several machine learning models were tested and a comparison study was carried out. Datasets for training and testing the models came from treatments performed by dialysis machines manufactured by Mozarc Medical. Among the models tested, the Random Forest (RF) classifier was identified as the more promising one and was then used to perform a parametric sensitivity study. By using a time window of 10 seconds, the RF model provided a Recall of 79% and an F1-Score of up to 85% on test data, demonstrating the good generalization ability that is always required by predictive models such as the one analysed in this paper.