Personalized peritoneal dialysis treatment using design of experiment techniques

An apparatus, comprising:

• at least one processor;
• and
• a memory coupled to the at least one processor, the memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
• receive prescription dosage information for a peritoneal dialysis (PD) process for a patient;
• determine a predicted ultrafiltration volume (UFV) for the patient by processing the prescription dosage information via a trained computational model, wherein the trained computational model is trained based on at least one prior PD process cycle of the patient and a corresponding ultrafiltration volume measurement;
• receive a measured UFV for the PD process of the patient;
• determine an error value based on a comparison of the predicted UFV and the measured UFV;
• determine a treatment recommendation for the patient based on the error value;
• determine a hydration status of the patient;
• and
• send a control signal to a controller of a PD machine to control a dwell time of the PD process based on the hydration status of the patient, wherein the PD process includes using the PD machine to treat the patient according to the treatment recommendation during the dwell time, wherein the trained computational model includes fuzzy logic configured to vary training data used to train the trained computational model to predict an IPV to minimize fluctuations of the hydration status of the patient around a target range.

A method, comprising:

• receiving prescription dosage information for a peritoneal dialysis (PD) process for a patient;
• and
• determining a predicted ultrafiltration volume (UFV) for the patient by processing the prescription dosage information via a trained computational model, wherein the trained computational model is trained based on at least one prior PD process cycle of the patient and a corresponding ultrafiltration volume measurement;
• receiving a measured UFV for the PD process of the patient;
• determining an error value based on a comparison of the predicted UFV and the measured UFV;
• determining a treatment recommendation for the patient based on the error value;
• determining a hydration status of the patient;
• and
• sending a control signal to a controller of a PD machine to control a dwell time of the PD process based on the hydration status of the patient, wherein the PD process includes using the PD machine to treat the patient according to the treatment recommendation during the dwell time, wherein the trained computational model includes fuzzy logic configured to vary training data used to train the trained computational model to predict an IPV to minimize fluctuations of the hydration status of the patient around a target range.