ArticleFrontiers in oncology2021
Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms.MethodsX · 2025Article
- Artificial Intelligence (AI) and Drug-Induced and Idiosyncratic Cytopenia: The Role of AI in Prevention, Prediction, and Patient Participation.Hematology reports · 2025Review
- Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.Journal of pediatric hematology/oncology · 2025Article
- Artificial intelligence in oncology drug development and management: a precision medicine perspective.Frontiers in oncology · 2025Review
- A scoping review on pediatric sepsis prediction technologies in healthcare.NPJ digital medicine · 2024Article
- Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care.International journal of environmental research and public health · 2022Article
- Using Machine Learning Algorithms to Predict Hospital Acquired Thrombocytopenia after Operation in the Intensive Care Unit: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe purpose of this article was to establish and validate clinically applicable septic shock early warning model (SSEW model) that can identify septic shock in hospitalized children with onco-hematological malignancies accompanied with fever or neutropenia.
methodsData from EMRs were collected from hospitalized pediatric patients with hematological and oncological disease at Shanghai Children's Medical Center. Medical records of patients (>30 days and <19 years old) with fever (≥38°C) or absolute neutrophil count (ANC) below 1.0 × 10 MAIN
resultsA total of 64 observation periods in the septic shock group and 2191 in the control group were included. AU-ROC of the SSEW model had higher predictive value for septic shock compared with the pSOFA score (0.93 vs. 0.76, Z = -2.73, P = 0.006). Further analysis showed that the AU-ROC of the SSEW model was superior to the pSOFA score at the observation points 4, 8, 12, and 24 h before septic shock. At the 24 h observation point, the SSEW model incorporated 14 module root features and 23 derived features.
conclusionThe SSEW model for hematological or oncological pediatric patients could help clinicians to predict the risk of septic shock in patients with fever or neutropenia 24 h in advance. Further prospective studies on clinical application scenarios are needed to determine the clinical utility of this AI model.
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