Artificial intelligence based analysis of patient data in the intensive care unit for early diagnosis and intervention

The aim of this thesis is to provide doctors a monitoring and reporting system for early diagnosis and intervention with the analysis of the big data collected from patients by using artificial intelligence algorithms In this study, it is aimed to develop an estimation system with the analysis of big data including parameter values for heart rate, blood pressure, pulse, respiratory counts which are collected through the software used in the intensive care unit from patient monitors and artificial respiratory device. In addition, it is aimed to make a prediction of timing when the weaning from mechanical ventilator should happen for patients by processing the retrospective data including respiratory values of patients with artificial respiration, hemodynamic monitoring values. The decision support system which processes patients' data will make it possible for busy clinicians to work more efficiently and to care more patients more safely. In order to analyze the big and rich data collected from the patients in the intensive care units, cause-effect relationships will be observed and machine learning algorithms will be used to explore the relationships between parameters.