engleski

SEPSIS PREDICTION USING ARTIFICIAL INTELLIGENCE ALGORITHMS

Sepsis is a serious, life-threatening condition. It is an extreme reaction of the body to an infection.When the body starts fighting that infection, it damages its own organs and tissue. Septic shock is the last stage of sepsis, and it leads to dangerously low blood pressure and sometimes even death. Patients with sepsis are usually treated in intensive care units (lCUs). In cooperation with the Clinical Hospital Center in Rijeka (Croatia), and its ICU, data was collected for use in different machine learning algorithms, so that predictions of sepsis occurrence in patients could be recognized by those algorithms. The data consists of 24 different parameters for each patient, where the 24th is a simple binary value representing sepsis occurrence for a patient. An artificial neural network (ANN) is being trained on this data, with 23 of the medical parameters used as inputs and binary sepsis occurrence as outputs. ANNs testing results show that connections between the input parameters and sepsis occurrence clearly exist, and with each new batch of data from the ICU, the accuracy of the neural network increases and therefore the predictions get better. Also, two other machine learning algorithms (classification methods) are used on the same data, so that results could be compared between the three. Support Vector Machine (SVM), based on the aforementioned data, builds a model that classifies patients to one of the two categories - patients with sepsis or patients without sepsis. Unlike SVM, K-Nearest Neighbors (KNN) method uses only part of the training data and accordingly classifies new patients.

artificial neural networks, machine learning algorithms, sepsis, support vector machines

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