Benchmarking emergency department prediction models with machine learning and public electronic health records

F Xie, J Zhou, JW Lee, M Tan, S Li, LSO Rajnthern… - Scientific Data, 2022 - nature.com
The demand for emergency department (ED) services is increasing across the globe,
particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have …

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

Z Rahmatinejad, T Dehghani, B Hoseini… - Scientific Reports, 2024 - nature.com
This study addresses the challenges associated with emergency department (ED)
overcrowding and emphasizes the need for efficient risk stratification tools to identify high …

Use of machine learning to differentiate children with Kawasaki disease from other febrile children in a pediatric emergency department

CM Tsai, CHR Lin, HC Kuo, FJ Cheng, HR Yu… - JAMA Network …, 2023 - jamanetwork.com
Importance Early awareness of Kawasaki disease (KD) helps physicians administer
appropriate therapy to prevent acquired heart disease in children. However, diagnosing KD …

Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients

WC Tsai, CF Liu, YS Ma, CJ Chen, HJ Lin… - International Journal of …, 2023 - Elsevier
Background Artificial intelligence (AI) holds significant potential to be a valuable tool in
healthcare. However, its application for predicting bacteremia among adult febrile patients in …

Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters

F Zhang, H Wang, L Liu, T Su, B Ji - BMC Infectious Diseases, 2023 - Springer
Background Bacterial bloodstream infection is responsible for the majority of cases of sepsis
and septic shock. Early recognition of the causative pathogen is pivotal for administration of …

Bacteremia detection from complete blood count and differential leukocyte count with machine learning: complementary and competitive with C-reactive protein and …

F Lien, HS Lin, YT Wu, TS Chiueh - BMC infectious diseases, 2022 - Springer
Background Biomarkers, such as leukocyte count, C-reactive protein (CRP), and
procalcitonin (PCT), have been commonly used to predict the occurrence of life-threatening …

An artificial intelligence approach to bloodstream infections prediction

KC Pai, MS Wang, YF Chen, CH Tseng, PY Liu… - Journal of clinical …, 2021 - mdpi.com
This study aimed to develop an early prediction model for identifying patients with
bloodstream infections. The data resource was taken from 2015 to 2019 at Taichung …

A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients

R Murri, G De Angelis, L Antenucci, B Fiori, R Rinaldi… - Diagnostics, 2024 - mdpi.com
The aim of the study was to build a machine learning-based predictive model to discriminate
between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data …

Combination of machine learning algorithms with natural language processing may increase the probability of bacteremia detection in the emergency department: A …

G Ben-Haim, M Yosef, E Rowand, J Ben-Yosef… - Digital …, 2024 - journals.sagepub.com
Background Prompt diagnosis of bacteremia in the emergency department (ED) is of utmost
importance. Nevertheless, the average time to first clinical laboratory finding range from 1 to …

Predicting bacteremia among septic patients based on ED information by machine learning methods: a comparative study

V Goh, YJ Chou, CC Lee, MC Ma, WYC Wang, CH Lin… - Diagnostics, 2022 - mdpi.com
Introduction: Bacteremia is a common but life-threatening infectious disease. However, a
well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is …