Transferability and interpretability of the sepsis prediction models in the intensive care unit
https://pubmed.ncbi.nlm.nih.gov/36581881/
Using machine learning for the early prediction of sepsis-associated ARDS in the ICU and identification of clinical phenotypes with differential responses to treatment
https://pubmed.ncbi.nlm.nih.gov/36579020/
OpenSep: a generalizable open source pipeline for SOFA score calculation and Sepsis-3 classification
EHR-based sepsis research often uses heterogeneous definitions of sepsis leading to poor generalizability and difficulty in comparing studies to each other. We have developed OpenSep, an open-source pipeline for sepsis phenotyping according to the Sepsis-3 definition, as well as determination of time of sepsis onset and SOFA scores. The Minimal Sepsis Data Model was developed alongside the pipeline to enable the execution of the pipeline to diverse sources of electronic health record data. The pipeline’s accuracy was validated by applying it to the MIMIC-IV version 1.0 data and comparing sepsis onset and SOFA scores to those produced by the pipeline developed by the curators of MIMIC. We demonstrated high reliability between both the sepsis onsets and SOFA scores, however the use of the Minimal Sepsis Data model developed for this work allows our pipeline to be applied to more broadly to data sources beyond MIMIC.
https://pubmed.ncbi.nlm.nih.gov/36570030/
Introduction of an emergency medicine pharmacist-led sepsis alert response system in the emergency department: A cohort study
https://pubmed.ncbi.nlm.nih.gov/36634917/
FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices
https://pubmed.ncbi.nlm.nih.gov/36679766/
Development and validation of risk-adjusted quality indicators for the long-term outcome of acute sepsis care in German hospitals based on health claims data
https://pubmed.ncbi.nlm.nih.gov/36698828/
Artificial Intelligence for Early Sepsis Detection - A Word of Caution
https://pubmed.ncbi.nlm.nih.gov/36724366/
Exploring valuation practices in diagnosis-as-category: The rising dominance of clinical practice in the categorisation of Sepsis, 1991-2016