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Machine learning for early detection of sepsis: an internal and temporal validation study
Armando D Bedoya1, Joseph Futoma2,3, Meredith E Clement4
1Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, North Carolina, USA.
JAMIA Open
|August 1, 2020
Summary
A novel deep learning model (MGP-RNN) detects sepsis earlier and more accurately than traditional methods. This advanced sepsis detection system offers improved clinical practice through superior predictive performance.
Area of Science:
- Artificial Intelligence in Medicine
- Clinical Decision Support Systems
- Sepsis Pathophysiology and Detection
Background:
- Sepsis remains a leading cause of mortality in hospitalized patients, necessitating early and accurate detection.
- Existing clinical scores and machine learning models have limitations in sepsis prediction accuracy and timeliness.
- The need for advanced computational methods to improve sepsis identification in clinical settings is critical.
Purpose of the Study:
- To determine if a deep learning model can detect sepsis earlier and with greater accuracy than other predictive models.
- To evaluate the performance of a deep learning model using metrics relevant to clinical practice implementation.
- To compare a novel multi-output Gaussian process and recurrent neural network (MGP-RNN) against established methods.
Main Methods:
- A deep learning model (MGP-RNN) was trained and validated using electronic health record data from adult hospitalized patients.
- Sepsis was defined by systemic inflammatory response syndrome (SIRS) criteria, blood culture order, and end-organ failure.
- Model performance was compared to random forest, Cox regression, penalized logistic regression, SIRS, qSOFA, and NEWS scores using C-statistics and operational metrics.
Main Results:
- The MGP-RNN achieved a C-statistic of 0.88 for predicting sepsis within 4 hours, outperforming all other models.
- The deep learning model detected sepsis a median of 5 hours earlier than other methods.
- Temporal validation confirmed the MGP-RNN's superior performance over 7 other clinical risk scores and machine learning comparisons.
Conclusions:
- A novel deep learning model (MGP-RNN) was successfully developed and validated for sepsis detection.
- The MGP-RNN demonstrated superior predictive accuracy and earlier detection capabilities compared to existing methods.
- This deep learning approach offers a promising advancement for sepsis management in clinical practice.
