Toward Automated Early Sepsis Alerting: Identifying Infection Patients from Nursing Notes

Abstract

Severe sepsis and septic shock are conditions that affect millions of patients and have close to 50% mortality rate. Early identification of at-risk patients significantly improves outcomes. Electronic surveillance tools have been developed to monitor structured Electronic Medical Records and automatically recognize early signs of sepsis. However, many sepsis risk factors (e.g. symptoms and signs of infection) are often captured only in free text clinical notes. In this study, we developed a method for automatic monitoring of nursing notes for signs and symptoms of infection. We utilized a creative approach to automatically generate an annotated dataset. The dataset was used to create a Machine Learning model that achieved an F1-score ranging from 79 to 96%.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

“…Among these ML-CDSS, 16 exclusively analysed structured patient variables: vitals (n ¼ 15), laboratory data (n ¼ 12), basic demographic information (n ¼ 7), medical history limited to main comorbidities and date of admission (n ¼ 7), therapeutic data (n ¼ 5) and electrocardiogram waveform (n ¼ 1). Two ML-CDSS added unstructured clinical data to their model, one specifically looked for antibiotic prescription in nursing notes to predict sepsis [35], but the other did not give details [36]. No CDSS for sepsis prediction used symptoms, physical examination findings nor microbiology data.…”

“…A particular attention should be paid to which variables are used by the ML-CDSS to predict their outcome: e.g. we found a ML-CDSS that used the prescription of antibiotics in ICU to predict sepsis [35], which could provide good performance but seems clinically irrelevant.…”

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

“…Of the 9 identified articles, 2 studies aimed at identifying infection, 47 , 48 6 studies focused on early detection of sepsis, 51 , 53 , 55 severe sepsis, 49 or septic shock, 50 , 54 and 1 study considered both identification and early detection for a combination of sepsis, severe sepsis, and septic shock. 52 Most studies focused on intensive care unit (ICU) 48 , 50 , 52–55 or emergency department (ED) 47 , 51 data; only 1 used inpatient care data. 49 Four studies utilized data from hospitals, 47 , 49 , 51 , 52 1 utilized MIMIC-II 54 and 4 utilized MIMIC-III.…”

“…49 Four studies utilized data from hospitals, 47 , 49 , 51 , 52 1 utilized MIMIC-II 54 and 4 utilized MIMIC-III. 48 , 50 , 53 , 55 MIMIC-II and MIMIC-III are publicly available ICU datasets created from Boston’s Beth Israel Deaconess Medical Center; MIMIC-II contains data from 2001–2007 76 and MIMIC-III contains data from 2001–2012. 77 Eight studies used data from the United States 47–51 , 53–55 and 1 study used data from Singapore.…”

“… 52 Sample sizes varied greatly in terms of the number of patients or notes used. To select patient cohorts or notes associated with sepsis, 3 studies used International Statistical Classification of Diseases and Related Health Problems (ICD) codes, 47 , 49 , 52 5 applied sepsis definition criteria, 49–51 , 53 , 55 1 utilized descriptions of antibiotics usage, 48 and another 54 applied criteria from Henry et al 78 that include ICD codes, sepsis criteria, and notes mentioning sepsis or septic shock. Table 1 summarizes the study characteristics and additional details are in Supplementary Table S2 (for Culliton et al, 49 the 8 structured variables for the Modified Baystate clinical definition of severe sepsis and 29 structured variables used in models were provided through personal communications with the corresponding author of Culliton et al, 49 Steve Gallant, on June 4, 2021).…”