An intelligent aide for interpreting a patient's dialysis data set

Many machines used in the modern hospital settings offer real time physiological monitoring. Haemodialysis machines combine a therapeutic treatment system integrated with sophisticated monitoring equipment. A large array of parameters can be collected including cardiovascular measures such as heart rate and blood pressure together with treatment related data including relative blood volume, ultrafiltration rate and small molecule clearance. A small subset of this information is used by clinicians to monitor treatment and plan therapeutic strategies but it is not usually analysed in any detail. The focus of this paper is the analysis of data collected over a number of treatment sessions with a view to predicting patient physiological behaviour whilst on dialysis and correlating this with clinical characteristics of individual patients. One of the commonest complications experienced by patients on dialysis is symptomatic hypotension. We have taken real time treatment data and outline a program of work which attempts to predict when hypotension is likely to occur, and which patients might be particularly prone to haemodynamic instability. This initial study has investigated: the rate of change of blood pressure versus rate of change of heart rate, rate of fluid removal, and rate of uraemic toxin clearance. We have used a variety of machine learning techniques (including hierarchical clustering, and Bayesian Network analysis algorithms). We have been able to detect from this dataset, 3 distinct groups which appear to be clinically meaningful. Furthermore we have investigated whether it is possible to predict changes in blood pressure in terms of other parameters with some encouraging results that merit further study