ArticleOpen forum infectious diseases2014
Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile.
Article in Open forum infectious diseases, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.Infectious disease clinics of North America · 2025Review
- A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.PloS one · 2024Article
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- Machine learning applications on neonatal sepsis treatment: a scoping review.BMC infectious diseases · 2023Article
- Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review.Journal of clinical and translational research · 2021Review
- Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research.Journal of clinical microbiology · 2021Review
- Machine Learning to Assess the Risk of Multidrug-Resistant Gram-Negative Bacilli Infections in Febrile Neutropenic Hematological Patients.Infectious diseases and therapy · 2021Article
- A Risk Score to PredictOpen forum infectious diseases · 2021Article
- Using Machine Learning and the Electronic Health Record to Predict ComplicatedOpen forum infectious diseases · 2019Article
- Learning Credible Models.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2018Article
- A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers.Infection control and hospital epidemiology · 2018Article
- Learning the Probability of Activation in the Presence of Latent Spreaders.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2018Article
- Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models.Journal of biomedical informatics · 2018Article
- Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2018Review
- Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.Infection control and hospital epidemiology · 2017Article
- How next-generation sequencing and multiscale data analysis will transform infectious disease management.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2015Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile.
methodsWe consider electronic medical record (EMR) data from patients admitted for ≥24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables automatically extracted from the EMR. Each model was evaluated on holdout admission data from the following year. A total of 34 846 admissions with 372 cases of CDI was used to train the model.
resultsApplied to the separate validation set of 34 722 admissions with 355 cases of CDI, the model that made use of the additional EMR data yielded an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% confidence interval [CI], .79-.83), and it significantly outperformed the model that considered only the small set of known clinical risk factors, AUROC of 0.71 (95% CI, .69-.75).
conclusionsAutomated risk stratification of patients based on the contents of their EMRs can be used to accurately identify a high-risk population of patients. The proposed method holds promise for enabling the selective allocation of interventions aimed at reducing the rate of CDI.
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