Title: Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients,
Abstract: Sepsis is a condition caused by the body's overwhelming and life-threatening
response to infection, which can lead to tissue damage, organ failure, and
finally death. Common signs and symptoms include fever, increased heart rate,
increased breathing rate, and confusion. Sepsis is difficult to predict,
diagnose, and treat. Patients who develop sepsis have an increased risk of
complications and death and face higher health care costs and longer
hospitalization. Today, sepsis is one of the leading causes of mortality among
populations in intensive care units (ICUs). In this paper, we look at the
problem of early detection of sepsis by using temporal data mining. We focus on
the use of knowledge-based temporal abstraction to create meaningful
interval-based abstractions, and on time-interval mining to discover frequent
interval-based patterns. We used 2,560 cases derived from the MIMIC-III
database. We found that the distribution of the temporal patterns whose
frequency is above 10% discovered in the records of septic patients during the
last 6 and 12 hours before onset of sepsis is significantly different from that
distribution within a similar period, during an equivalent time window during
hospitalization, in the records of non-septic patients. This discovery is
encouraging for the purpose of performing an early diagnosis of sepsis using
the discovered patterns as constructed features.