Machine learning approach for improving decision support in ICU

Abstract

Patients in the intensive care unit (ICU) receive a deep observation for controlling and responding to their rapidly changing physiological conditions. The quality of their care depends on clinical staff combining large amounts of clinical data to understand the severity of their illness. Actually in real time doctors and nurses have to take care of huge amount of data. Sometimes, they cannot focus on all the parameters at the same time. After collecting all the parameters, they take decisions. A lack of early recognition of physiologic decline can play a major role in failure to rescue patients. Early prediction is one of the important tasks in the ICU. In this paper, we propose a machine learning approach to improve decision support in ICU. In the proposed model decision tree will be used to predict the future health condition of patients. The curve of the decision tree of the proposed model will show how severe the patient‘s condition is. It will also show the health improvements and decrements. In this model there is option for controlling the lifesaving machines of ICU like ventilation machine, blood warmer machine and syringe pump machine. To control the machines this model uses logistic regression algorithm. It will use some independent variables to predict the decision of automatic intervention. Using the proposed model doctors can easily monitor the health of ICU patients. As it predicts risks, doctors can take early preparation for worst situation. Automatic intervention decisions for ICU machines can save lives in critical moments. As a whole, the model is specially designed for coronary care unit of ICU.