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

Tsai, Wei-Chun; Liu, Chung-Feng; Yushan,; Chen, Chia‐Jung; Lin, Hung‐Jung; Hsu, Chien‐Chin; Chou, Julie Chi; Chien, Yu Wen; Huang, Chien‐Cheng

doi:10.1016/j.ijmedinf.2023.105176

Cited by 7 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within inpatient settings, nine studies examined general populations [20][21][22][23][24][25][26][27][28], and others focused on specific patient cohorts including those with central venous catheters (CVC) [30,31], systemic inflammatory response syndrome (SIRS) [29], and various other conditions. In ED settings, seven studies were identified [37][38][39][40][41][42][43], two of which addressed specific patient groups. ICU-focused research numbered six studies [44][45][46][47][48][49], again with two concentrating on specific patient subsets.…”

Section: Study Characteristicsmentioning

confidence: 99%

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Bopche,

Damås

2024

Preprint

View full text Add to dashboard Cite

Abstract. Purpose: Bloodstream infections (BSIs) present significant public health challenges. With the advent of machine learning (ML), promising predictive models have been developed. This study evaluates their performance through a systematic review and meta-analysis. Methods: We performed a comprehensive systematic review across multiple databases, including PubMed, IEEE Xplore, ScienceDirect, ACM Digital Library, SpringerLink, Web of Science, Scopus, and Google Scholar. Eligible studies focused on BSIs within any hospital setting, employing ML models as the diagnostic test. We evaluated the risk of bias with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist and assessed the quality of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Models reporting the area under the receiver operating characteristic curve (AUROC) were included in the meta-analysis to identify key performance drivers. Results: After screening, a total of 30 studies were eligible for synthesis, from which 41 models and 8 data types were extracted. Most of the studies were carried out in the inpatient settings (n=17; 56%), followed by the emergency department (ED) settings (n=7; 23%), and followed by the ICU settings (n=6; 20%). The reported AUROCs in the hospital inpatients settings, ranged from 0.51-0.866, in the ICU settings AUROCs ranged from 0.668-0.970, and in the emergency department (ED) settings the AUROCs of the models ranged from 0.728-0.844. One study reported prospective cohort study, while two prospectively validated their models. In the meta-analysis, laboratory tests, Complete Blood Count/Differential Count (CBC/DC), and ML model type contributed the most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual ML models can accurately predict BSIs at different stages of patient trajectory. Although they enable early prediction of BSI, a comprehensive approach to integrate data types and models is necessary. Systematic reporting, externally validated, and clinical implementation studies are needed to establish clinical confidence.

show abstract

Section: Study Characteristicsmentioning

confidence: 99%

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Bopche,

Damås

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In the past, most AI/ML studies have centered on evaluating model quality, with only a limited number of predictive systems developed for medical condition prognosis [12][13][14]. Furthermore, predictive systems in the realm of nutrition and healthcare AI remain sparse [15].…”

Section: Introductionmentioning

confidence: 99%

Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control

Liu,

Lin

et al. 2023

Bioengineering

Self Cite

View full text Add to dashboard Cite

(1) Background: Persistent hyperglycemia in diabetes mellitus (DM) increases the risk of death and causes cardiovascular disease (CVD), resulting in significant social and economic costs. This study used a machine learning (ML) technique to build prediction models with the factors of lifestyle, medication compliance, and self-control in eating habits and then implemented a predictive system based on the best model to forecast whether blood glucose can be well-controlled within 1 year in diabetic patients attending a DM nutritional clinic. (2) Methods: Data were collected from outpatients aged 20 years or older with type 2 DM who received nutrition education in Chi Mei Medical Center. Multiple ML algorithms were used to build the predictive models. (3) Results: The predictive models achieved accuracies ranging from 0.611 to 0.690. The XGBoost model with the highest area under the curve (AUC) of 0.738 was regarded as the best and used for the predictive system implementation. SHAP analysis was performed to interpret the feature importance in the best model. The predictive system, evaluated by dietitians, received positive feedback as a beneficial tool for diabetes nutrition consultations. (4) Conclusions: The ML prediction model provides a promising approach for diabetes nutrition consultations to maintain good long-term blood glucose control, reduce diabetes-related complications, and enhance the quality of medical care.

show abstract

Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study

Huang,

Kuo,

Shen

et al. 2024

International Journal of Medical Informatics

View full text Add to dashboard Cite

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

Cited by 7 publications

References 35 publications

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control

Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study

Contact Info

Product

Resources

About