Simplified Organ Failure Assessment

Diagnosing a patient 's illness state is always a critical phase in medication. Patients admitted in the Intensive Care Unit are vulnerable to adverse effects including in-ICU morbidities and mortality. Patients in ICU are connected to equipments like Pulse oximeter, Cardiac Monitor, Face mask, Nasogastric tube to monitor their oxygen level in blood, heart rate, blood pressure and support their breathe. They are closely monitored by Cardiothoracic nurses who records these readings from the equipments several times a day. Presently, health care researchers are more focused on developing techniques to improve the effectiveness of the treatment. It has become a major interest in all developed countries to provide a cost-effective intensive care to patients and limit health care costs.

In this situation, not only predicting the mortality of patients is a crucial task, but also evaluating the efficacy of medications. Hence, to address this, critical illness severity assessment scores such as Simplified Acute Physiology score (SAPS II), Acute Physiology and Chronic Health Evaluation System (APACHE II), Sequential Organ Failure Assessment score (SOFA) have been developed and widely used over the few decades because of its reliability, cost-effectiveness and relatively easy to calculate. However, the limitations like assuming the missing parameters are normal has made this rationale obsolete and are not sufficient precision to make an effective decision. Therefore, machine learning techniques are proved to be useful in automatically extracting information from the raw data and predict more accurately. These techniques can handle huge data from different sources and incorporate background knowledge in