Medical Decision Support System for Smart Infusion Therapy
In critically ill patients, volume and/or fluid replacement therapy, if not adequately monitored, may lead to serious conditions such as fluid and electrolyte disorders (e.g. hypokalaemia, hyponatremia) or acute kidney injury (AKI). Although the corresponding parameters (e.g. fluid volume, fluid composition, urine volume) are known, till today no trend analysis of these parameters is performed in order to support therapy or to monitor the disease status of the patient on a near-automated basis.
Aim of this thesis is to develop and to clinically evaluate a prototypical application providing decision support for electrolyte and fluid management in critically ill patients. To this end, a semi-closed loop control system will be established by introducing a feedback loop considering patient specific data (e.g. electrolytes in blood, urine volume, body weight) and system specific data (e.g. infusion dose, infusion rate).
By linking infusion rates to the monitored health parameters as shown in Figure 1, the system will provide support for the actual disease management process and enhance the efficacy of infusion therapy for critically ill patients.