Machine learning in infection management using routine electronic health records: Tools, techniques, and reporting of future technologies
Open accessAbstract
BACKGROUND: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. OBJECTIVES: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. SOURCES: A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. CONTENT: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. IMPLICATIONS: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.
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XAI could help the user understand when to trust a model and why an error may occur.
“XAI could help the user understand when to trust a model and why an error may occur [97,116].”
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“Furthermore, Luz et al. [97] argue that a thorough reasoning is required for choosing between transparent and black-box ML algorithms, because post-hoc interpretation methods that develop a mirror model of the original one to add explainability could provide an inaccurate representation of the original model.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [97]
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- Authors
- Luz, C. F., Vollmer, M., Decruyenaere, J., Nijsten, M. W., Glasner, C., & Sinha, B.
- Affiliations
- University Medical Center Groningen (NL)
- Universitätsmedizin Greifswald (DE)
- Ghent University Hospital (BE)
- Corresponding author
- Luz, C.F.
- Venue
- Clinical Microbiology and Infection
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 26
- Issue
- 10
- Pages
- 1291-1299
- ISSN
- 1198-743X (ISSN-L)
- OpenAlex
- W3005677144
- Cited by
- 106
- References
- 88
- Citations by year
- 2026: 2 · 2025: 19 · 2024: 15 · 2023: 15 · 2022: 25 · 2021: 26 · 2020: 4
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Microbiology and Infection (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by-nc-nd)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission Horizon 2020
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- machine learning, infection management, electronic health records, sepsis prediction, nosocomial infections, surgical site infection, bacterial infection, fungal infection, model validation, explainability, reporting quality, antimicrobial prescribing, urinary tract infection
- MeSH terms
- Machine Learning, Algorithms, Cross Infection, Humans, Prognosis, Surgical Wound Infection, Urinary Tract Infections, Sepsis, Decision Support Systems, Clinical, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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