Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients.
Jacob CalvertNicholas SaberJana L HoffmanRitankar DasPublished in: Diagnostics (Basel, Switzerland) (2019)
Sepsis, a dysregulated host response to infection, is a major health burden in terms of both mortality and cost. The difficulties clinicians face in diagnosing sepsis, alongside the insufficiencies of diagnostic biomarkers, motivate the present study. This work develops a machine-learning-based sepsis diagnostic for a high-risk patient group, using a geographically and institutionally diverse collection of nearly 500,000 patient health records. Using only a minimal set of clinical variables, our diagnostics outperform common severity scoring systems and sepsis biomarkers and benefit from being available immediately upon ordering.
Keyphrases
- septic shock
- machine learning
- acute kidney injury
- intensive care unit
- healthcare
- public health
- end stage renal disease
- mental health
- case report
- chronic kidney disease
- artificial intelligence
- ejection fraction
- newly diagnosed
- palliative care
- cardiovascular disease
- prognostic factors
- health information
- peritoneal dialysis
- deep learning
- cardiovascular events
- patient reported outcomes
- human health
- climate change