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Author Notes:

Corresponding author: Shamim Nemati, PhD, Assistant Professor, Emory Department of Biomedical Informatics, Woodruff Memorial Research Building, 101 Woodruff Circle, Room 4131, Atlanta, GA 30322, Phone: (405) 850-4751, Email: shamim.nemati@alum.mit.edu.

Drs. Nemati, Stanley, and Clifford received support for article research from the National Institutes of Health (NIH).

Dr. Clifford c/f (disclosed: Dr Buchman’s research is partially supported by the Surgical Critical Care Initiative (SC2i), funded through the Department of Defense’s Health Program – Joint Program Committee 6 / Combat Casualty Care (USUHS HT9404-13-1-0032 and HU0001-15-2-0001).; Dr. Buchman’s institution received funding from the Henry M. Jackson Foundation for his role as site director in Surgical Critical Care Institute, www.sc2i.org, funded through the Department of Defense’s Health Program – Joint Program Committee 6 / Combat Casualty Care (USUHS HT9404-13-1-0032 and HU0001-15-2-0001).; from Society of Critical Care Medicine for his role as Editor-in-Chief of Critical Care Medicine; and from Philips Corporation (unrestricted educational grant to a physician education association in South Korea so he could present the results of his research in eICU).

Dr. Buchman received support for article research from the Henry M Jackson Foundation.

The remaining authors have disclosed that they do not have any remaining conflicts of interest.

Subjects:

Research Funding:

Dr. Nemati is funded by the National Institutes of Health, award #K01ES025445.

Dr. Holder received support from CR Bard.

Dr. Buchman’s research is partially supported by the Surgical Critical Care Initiative (SC2i), funded through the Department of Defense’s Health Program – Joint Program Committee 6 / Combat Casualty Care (USUHS HT9404-13-1-0032 and HU0001-15-2-0001). He is also the Editor-in-Chief of Critical Care Medicine.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Critical Care Medicine
  • General & Internal Medicine
  • informatics
  • machine learning
  • organ failure
  • prognostication
  • sepsis
  • INTENSIVE-CARE-UNIT
  • SEPTIC SHOCK
  • EPIDEMIOLOGY
  • STATES

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

Tools:

Journal Title:

Critical Care Medicine

Volume:

Volume 46, Number 4

Publisher:

, Pages 547-553

Type of Work:

Article | Post-print: After Peer Review

Abstract:

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.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.

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