Acute Care Sepsis Prediction: Analyzing the Influence of Social and Behavioral Determinants

Abstract

Within the hospital setting, sepsis is a leading cause of mortality, affecting more than 1.7 million adults annually. It is also present in about 30 to 50 percent of hospitalizations that end with death. Despite the high incidence and prevalence, detection and diagnosis of sepsis remain a challenge due to its non-specific early stage symptoms. However, as it can quickly progress to a life-threatening stage, it is important to detect sepsis patients earlier to improve outcomes. With the recently increased adoption of EHRs, many institutions now have large amounts of patient data being collected and have created their own customized sepsis detection and mortality tools using various modeling or machine learning (ML) techniques. Additionally, those who experience more socioeconomic challenges are more susceptible to chronic illnesses, including sepsis. However, structured coding of social or behavioral features is often underutilized and unreliable. First, in order to understand the current environment of predictive analytics solutions for sepsis, we systematically identified various studies that utilize different models or ML techniques and analyzed their approach and results. Second, we developed a framework that utilizes natural language processing text classification from clinical notes to extract social and behavioral determinants of health (SBDH). Third, we assessed classification methods that utilize currently established sepsis definitions or clinical scores to establish a baseline and integrated the SBDH data extracted from clinical notes described earlier and determined if SBDH features can help enhance predictive performance for sepsis detection in the acute care setting.