Towards an Explainable Model for Sepsis Detection Based on Sensitivity Analysis
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Complementary interpretability approaches, including LIME and Grad-CAM, were implemented in select studies, while sensitivity analysis was specifically employed in sepsis detection with deep neural networks.
“Complementary approaches, including LIME and Grad-CAM, were implemented in select studies to provide additional insights into model behavior [14, 17, 19], while sensitivity analysis was specifically employed in sepsis detection with deep neural networks [25].”
From Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction (2026) · cites this paper as [25]
Cites
Time to treatment and mortality during mandated emergency care for sepsis.
Seymour, C. W., Gesten, F., Prescott, H. C., Friedrich, M. E., Iwashyna, T. J., Phillips, G. S., Lemeshow, S., Osborn, T., Terry, K. M., & Levy, M. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1703058 · Cited by 2,225 · see citing works
A computational approach to early sepsis detection
Calvert, J. S., Price, D. A., Chettipally, U. K., Barton, C. W., Feldman, M. D., Hoffman, J. L., Jay, M., & Das, R. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.05.003 · Cited by 244 · see citing works
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AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview
Stylianides, C., Nicolaou, A., Sulaiman, W. A., Alexandropoulou, C.-A., Panagiotopoulos, I., Karathanasopoulou, K., Dimitrakopoulos, G., Kleanthous, S., Politi, E., Ntalaperas, D., et al. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7010006 · Cited by 28 · see citing works
Athukorala, V. S., & Ilmini, W. M. K. S. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03344-0 · Cited by 10 · see citing works
“Complementary approaches, including LIME and Grad-CAM, were implemented in select studies to provide additional insights into model behavior [14, 17, 19], while sensitivity analysis was specifically employed in sepsis detection with deep neural networks [25].”
Influential
- Authors
- Chen, M., & Hernández, A.
- Affiliations
- Southeast University (CN)
- Venue
- IRBM
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 43
- Issue
- 1
- Pages
- 75-86
- ISSN
- 1876-0988 (ISSN-L)
- 1959-0318
- OpenAlex
- W3167342917
- Cited by
- 23
- References
- 2
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 11 · 2023: 3 · 2021: 1
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IRBM (acceptedVersion)
- HAL (submittedVersion, cc-by-nc)
- Funders
- INSERM
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- sepsis detection, explainable machine learning, model interpretability, sensitivity analysis, biomarker discovery, intensive care unit patients, electronic health records, feature selection, real-time prediction
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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.
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