An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU

By Shamim Nemati, A. Holder, Fereshteh Razmi, Matthew D. Stanley, G. Clifford, T. Buchman. Critical Care Medicine, 2017

684 citations recorded by the source corpus.

Objectives: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Design: Observational cohort study. Setting: Academic medical center from January 2013 to December 2015. Patients: Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. Interventions: None. Measurements and Main Results: High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83–0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Conclusions: Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4–12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.

Subfields: Artificial Intelligence, Clinical Biochemistry, Epidemiology

View Paper: https://www.semanticscholar.org/paper/26feca29b05ac2abb7841f3a4172d0c28583ca33

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

S. Kwon
Bing Xie
Nianzong Hou
Rumin Zhang
Jungrak Hong
Yong Yu
Ming-zhao Li
Lu He

Citations in the corpus

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost (2020, methodology): “Buchman et al. [29] concluded that machine learning-based CDS tools can accurately predict the onset of sepsis in an ICU patient 4–12 h prior to clinical [31] used JMP statistical software to conduct a supervised machine learning for identification of sepsis and septic shock and found it’s a…”
Physician Confidence in Artificial Intelligence: An Online Mobile Survey (2018, background): “The development and utilization of AI programs in the medical field are currently entering the stage of commercialization [8,9].”