Prediction of sepsis for the intensive care unit patients with stream mining and machine learning

Authors : Melike Akyüz, Yunus Doğan, Atakan Koçyiğit, Ayşe Pınar Miran

Pages : 354-365

View : 57 | Download : 46

Publication Date : 2024-06-29

Article Type : Research Paper

Abstract :Sepsis, which is known as multiple organ failure, is the primary cause of mortality for all patients in intensive care units, regardless of their other illnesses. An intensive care unit decision support system that can predict sepsis in intensive care patients early and warns the doctor has been developed. Since the COVID-19 virus, the variant and number of intensive care patients have increased, so this study has been developed as a precaution to worsen the situation with sepsis. A user-friendly interface and system have been designed to help the physician better monitor the patient\'s sepsis status. It has been developed in order to meet the need for a decision support system that makes sepsis estimation in accordance with the reference intervals of Turkish patients\' values. For a better result of predicting sepsis early, it has been concluded how the data obtained and used in a certain period of time should be analyzed and what methods could be used to estimate higher performance. In the study, machine learning (classification and regression), deep learning algorithms have been used for estimation and the results obtained have been compared. As an impact of research, an intensive care sepsis decision support system, which consists of 122400 hourly data of 300 intensive care patients and estimates with approximately between 88% and 94% successful results in accordance with the reference intervals of Turkish patients, has been developed.
Keywords : Derin öğrenme, Karar destek sistemleri, Makine öğrenmesi, Tıbbi bilişim sistemleri, Akış madenciliği