ArticleOpen forum infectious diseases2019
Using Machine Learning and the Electronic Health Record to Predict Complicated
Article in Open forum infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- External validation of clinical prediction rules for complications and mortality following Clostridioides difficile infection.PloS one · 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
- Guiding Clostridioides difficile Infection Prevention Efforts in a Hospital Setting With AI.JAMA network open · 2025Article
- Gut community structure as a risk factor for infection inmSystems · 2024Article
- Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It.Open forum infectious diseases · 2024Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Derivation and internal validation of the multivariate toxigenicAntimicrobial stewardship & healthcare epidemiology : ASHE · 2024Article
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- Graph convolutional network-based fusion model to predict risk of hospital acquired infections.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- The future of automated infection detection: Innovation to transform practice (Part III/III).Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Review
- Leveraging electronic data to expand infection detection beyond traditional settings and definitions (Part II/III).Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Review
- Utilization of Bioinorganic Nanodrugs and Nanomaterials for the Control of Infectious Diseases Using Deep Learning.BioMed research international · 2023Article
- Continually-Adaptive Representation Learning Framework for Time-Sensitive Healthcare Applications.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
- External Validation and Comparison of Clostridioides difficile Severity Scoring Systems.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Healthcare costs and mortality associated with serious fluoroquinolone-related adverse reactions.Pharmacology research & perspectives · 2022Article
- Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms.Frontiers in public health · 2022Article
- Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases.Computational and mathematical methods in medicine · 2022Review
- How does innovative technology impact nursing in infectious diseases and infection control? A scoping review.Nursing open · 2021Article
- Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Pre-Trained Deep Convolutional Neural Network for Clostridioides Difficile Bacteria Cytotoxicity Classification Based on Fluorescence Images.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
background
methodsWe considered adult patients diagnosed with CDI between October 2010 and January 2013 at the University of Michigan hospitals. Cases were labeled complicated if the infection resulted in ICU admission, colectomy, or 30-day mortality. Leveraging EHR data, we trained a model to predict subsequent complications on each of the 3 days after diagnosis. We compared our EHR-based model to one based on a small set of manually curated features. We evaluated model performance using a held-out data set in terms of the area under the receiver operating characteristic curve (AUROC).
resultsOf 1118 cases of CDI, 8% became complicated. On the day of diagnosis, the model achieved an AUROC of 0.69 (95% confidence interval [CI], 0.55-0.83). Using data extracted 2 days after CDI diagnosis, performance increased (AUROC, 0.90; 95% CI, 0.83-0.95), outperforming a model based on a curated set of features (AUROC, 0.84; 95% CI, 0.75-0.91).
conclusionsUsing EHR data, we can accurately stratify CDI cases according to their risk of developing complications. Such an approach could be used to guide future clinical studies investigating interventions that could prevent or mitigate complicated CDI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.