  Responding rapidly to a patient who is demonstrating signs of imminent
clinical deterioration is a basic tenet of patient care. This gave rise to a
patient safety intervention philosophy known as a Rapid Response System (RRS),
whereby a patient who meets a pre-determined set of criteria for imminent
clinical deterioration is immediately assessed and treated, with the goal of
mitigating the deterioration and preventing intensive care unit (ICU) transfer,
cardiac arrest, or death. While RRSs have been widely adopted, multiple
systematic reviews have failed to find evidence of their effectiveness.
Typically, RRS criteria are simple, expert (consensus) defined rules that
identify significant physiologic abnormalities or are based on clinical
observation.
  If one can find a pattern in the patient's data earlier than the onset of the
physiologic derangement manifest in the current criteria, intervention
strategies might be more effective. In this paper, we apply machine learning to
electronic medical records (EMR) to infer if patients are at risk for clinical
deterioration. Our models are more sensitive and offer greater advance
prediction time compared with existing rule-based methods that are currently
utilized in hospitals. Our results warrant further testing in the field; if
successful, hospitals can integrate our approach into their existing IT systems
and use the alerts generated by the model to prevent ICU transfer, cardiac
arrest, or death, or to reduce the ICU length of stay.
