Published on in Vol 5, No 9 (2021): September
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/28000, first published
.

Journals
- . The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden. Journal of Health Economics and Outcomes Research 2022;9(1):101 View
- Chen Q, Li R, Lin C, Lai C, Chen D, Qu H, Huang Y, Lu W, Tang Y, Li L. Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Medical Informatics and Decision Making 2022;22(1) View
- Chen Q, Li R, Lin C, Lai C, Huang Y, Lu W, Li L. SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis. Applied Clinical Informatics 2023;14(01):65 View
- Yang Z, Cui X, Song Z. Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis. BMC Infectious Diseases 2023;23(1) View
- Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of Healthcare Informatics Research 2023;7(3):313 View
- Wang Z, Qi Y, Wang F, Zhang B, Jianguo T. Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis. Frontiers in Immunology 2023;14 View