Data mining to predict the use of vasopressors in intensive medicine patients - Braga, André (2016)
All rights reserved. The role that decision making process plays in Intensive Medicine is very critical essential due to the bad health condition of the patients that go to Intensive Care Units (ICU) and the need of a quick and accurate decisions. Therefore each decision is crucial, because it can help saving endangered lives. The decision should be always taken in the patient best interest after analyzing all the data available. In the eyes of the intensivists, the ever growing amount of available data concerning the patients, makes it each time more difficult for them to make a decision based on so many information. It is based on this ideal of improving the decision making process, that this work arises and Data Mining models were induced to predict if a patient will need to take a vasopressor, more specifically: Dopamine, Adrenaline or Noradrenaline. This work used real data provided by an Intensive Care Unit and collected in real-time. The data mining model were induced using data from vital sign monitors, laboratory analysis and information about the patient’s Electronic Health Record. This study was based in clinical evidences and provided very useful results with a sensitivity around 90%. These models will reduce the need of vasopressor drugs by helping intensivists to act and take accurate decision before the vasopressor be need by the patient. It will improve the patient condition because when the time comes the predicted necessity of the vasopressor will cease to exist due to the early care provided by the intensivist. The decisions can be for example change the therapeutic plan. Overall, the decision making process becomes more reliable and effective and the quality of care given to patients is better.