Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments

BMC Cancer

Ethan Steinberg¹  Nigam Shah¹  Conor Corbin¹  George A. Tomlinson²  Emily Vettese³  Lillian Sung³  Aaron Campigotto⁴  Loreto Lecce⁵ 

[1]Biomedical Informatics Research, Stanford University

[2]Department of Medicine, University Health Network

[3]Division of Haematology/Oncology, The Hospital for Sick Children

[4]Division of Infectious Diseases, The Hospital for Sick Children

[5]Division of Neonatology, The Hospital for Sick Children

关键词: Machine learning;    Classifier;    Bloodstream infection;    Children;    Cancer;   

DOI  :  10.1186/s12885-020-07618-2

来源: DOAJ

【 摘 要 】

Abstract Background Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hematopoietic stem cell transplantation (HSCT) recipients, and to compare this approach with presence of neutropenia to identify BSI. Methods We included patients 0–18 years of age at cancer diagnosis or HSCT between January 2009 and November 2018. Eligible blood cultures were those with no previous blood culture (regardless of result) within 7 days. The primary outcome was BSI. Four machine learning algorithms were used: elastic net, support vector machine and two implementations of gradient boosting machine (GBM and XGBoost). Model training and evaluation were performed using temporally disjoint training (60%), validation (20%) and test (20%) sets. The best model was compared to neutropenia alone in the test set. Results Of 11,183 eligible blood cultures, 624 (5.6%) were positive. The best model in the validation set was GBM, which achieved an area-under-the-receiver-operator-curve (AUROC) of 0.74 in the test set. Among the 2236 in the test set, the number of false positives and specificity of GBM vs. neutropenia were 508 vs. 592 and 0.76 vs. 0.72 respectively. Among 139 test set BSIs, six (4.3%) non-neutropenic patients were identified by GBM. All received antibiotics prior to culture result availability. Conclusions We developed a machine learning algorithm to classify BSI. GBM achieved an AUROC of 0.74 and identified 4.3% additional true cases in the test set. The machine learning algorithm did not perform substantially better than using presence of neutropenia alone to predict BSI.

【 授权许可】

Unknown