ArticleWorld journal of critical care medicine2019
Machine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit.
Article in World journal of critical care medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Data to diagnosis: Machine learning-aided prediction of bloodstream infections.Bioinformation · 2025Article
- 21st century critical care medicine: An overview.World journal of critical care medicine · 2024Review
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- Lung Injury Prediction Model in Bone Marrow Transplantation: A Multicenter Cohort Study.American journal of respiratory and critical care medicine · 2024Article
- Comparative Analyses of the Impact of Different Criteria for Sepsis Diagnosis on Outcome in Patients with Spontaneous Subarachnoid Hemorrhage.Journal of clinical medicine · 2022Article
- Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.BMC infectious diseases · 2022Article
- Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition.Critical care explorations · 2021Article
- Derivation and Validation of an Automated Search Strategy to Retrospectively Identify Acute Respiratory Distress Patients Per Berlin Definition.Frontiers in medicine · 2021Article
- Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.Frontiers in medicine · 2021Article
- Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.Journal of translational medicine · 2020Article
- Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the recent change in the definition (Sepsis-3 Definition) of sepsis and septic shock, an electronic search algorithm was required to identify the cases for data automation. This supervised machine learning method would help screen a large amount of electronic medical records (EMR) for efficient research purposes.
aimTo develop and validate a computable phenotype via supervised machine learning method for retrospectively identifying sepsis and septic shock in critical care patients.
methodsA supervised machine learning method was developed based on culture orders, Sequential Organ Failure Assessment (SOFA) scores, serum lactate levels and vasopressor use in the intensive care units (ICUs). The computable phenotype was derived from a retrospective analysis of a random cohort of 100 patients admitted to the medical ICU. This was then validated in an independent cohort of 100 patients. We compared the results from computable phenotype to a gold standard by manual review of EMR by 2 blinded reviewers. Disagreement was resolved by a critical care clinician. A SOFA score ≥ 2 during the ICU stay with a culture 72 h before or after the time of admission was identified. Sepsis versions as V1 was defined as blood cultures with SOFA ≥ 2 and Sepsis V2 was defined as any culture with SOFA score ≥ 2. A serum lactate level ≥ 2 mmol/L from 24 h before admission till their stay in the ICU and vasopressor use with Sepsis-1 and-2 were identified as Septic Shock-V1 and-V2 respectively.
resultsIn the derivation subset of 100 random patients, the final machine learning strategy achieved a sensitivity-specificity of 100% and 84% for Sepsis-1, 100% and 95% for Sepsis-2, 78% and 80% for Septic Shock-1, and 80% and 90% for Septic Shock-2. An overall percent of agreement between two blinded reviewers had a
conclusionSupervised machine learning for identification of sepsis and septic shock is reliable and an efficient alternative to manual chart review.
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