Memristor Models for Early Detection of Sepsis in ICU patients

Paper in proceedings, 2019

A supervised learning technique is used to carefully train memristor models to predict at an early stage whether a patient in intensive care unit (ICU) has the sepsis. A memristor behaves as a resistor, with a (mem)resistance that changes over time within a bounded interval. The resistance value depends on the full history of an applied voltage difference across the element, in the same way as the state of the brain depends on what a person has experienced in the past. The information contained in a voltage difference time series can be encoded in the resistance value. Clinical variables measured subsequently each hour since the patient's admittance in ICU are transformed into voltage difference signals with transformation functions. The training procedure involves the optimization of the transformation functions. The decision of whether to predict sepsis or not is taken by reading the value of the resistance. The authors have participated in the Physionet 2019 challenge with the name called "the memristive agents" and their best submission resulted to a utility score 0.20 on a hidden test data-set.