The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.
A computational approach to early sepsis detection
Open accessAbstract
Objective To develop high-performance early sepsis prediction technology for the general patient population. Methods Retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission. Results A sepsis early warning algorithm, InSight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode. When applied to a never-before-seen set of test patients, InSight predictions demonstrated a sensitivity of 0.90 (95% CI: 0.89-0.91) and a specificity of 0.81 (95% CI: 0.80-0.82), exceeding or rivaling that of existing biomarker detection methods. Across predictive times up to three hours before a sustained SIRS event, InSight maintained an average area under the ROC curve of 0.83 (95% CI: 0.80-0.86). Analysis of patient sepsis risk showed that contributions from the coevolution of multiple risk factors were more important than the contributions from isolated individual risk factors when making predictions further in advance. Conclusions Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients.
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“numerous models were tested on the freely accessible MIMIC database [27, 33, 52–59]”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [52]
Citations
Cites
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Early goal-directed therapy in the treatment of severe sepsis and septic shock
Rivers, E., Nguyen, B., Havstad, S., Ressler, J., Muzzin, A., Knoblich, B., Peterson, E., & Tomlanovich, M. (2001) · New England Journal of Medicine · doi:10.1056/nejmoa010307 · Cited by 10,743
Angus, D. C., Linde-Zwirble, W. T., Lidicker, J., Clermont, G., Carcillo, J., & Pinsky, M. R. (2001) · Critical Care Medicine · doi:10.1097/00003246-200107000-00002 · Cited by 8,503
Kumar, A., Roberts, D., Wood, K. E., Light, B., Parrillo, J. E., Sharma, S., Suppes, R., Feinstein, D., Zanotti, S., Taiberg, L., et al. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000217961.75225.e9 · Cited by 6,109
2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference
Conference, F. T. I. S. D., Levy, M. M., Fink, M. P., Marshall, J. C., Abraham, E. H., Angus, D. C., Cook, D., Cohen, J., Opal, S. M., Vincent, J., et al. (2003) · Intensive Care Medicine · doi:10.1007/s00134-003-1662-x · Cited by 2,877
A randomized trial of protocol-based care for early septic shock.
The ProCESS Investigators (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1401602 · Cited by 2,414
Goal-Directed Resuscitation for Patients with Early Septic Shock
The ARISE Investigators and the ANZICS Clinical Trials Group (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1404380 · Cited by 2,055
Regional Variation in Out-of-Hospital Cardiac Arrest Incidence and Outcome
Nichol, G. (2008) · JAMA · doi:10.1001/jama.300.12.1423 · Cited by 1,979
Validation of a modified Early Warning Score in medical admissions
Subbe, C. P. (2001) · QJM · doi:10.1093/qjmed/94.10.521 · Cited by 1,788
Trial of early, goal-directed resuscitation for septic shock.
Mouncey, P. R., Osborn, T. M., Power, G. S., Harrison, D. A., Sadique, M. Z., Grieve, R. D., Jahan, R., Harvey, S. E., Bell, D., Bion, J. F., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500896 · Cited by 1,604
Cited by
Sorted by citations
Items 201–244 of 244
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RetractedLightweight large language models for early sepsis prediction via a semantic abstraction rule engine
Yang, Y., Yi, B., & Chen, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-49672-z · Cited by 1
Kanthavel, R., Dhaya, R., Jayanthiladevi, A., & Rutakemwa, M. M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01820-0 · Cited by 2
Rodríguez, E. A., López-Martínez, F., & Martínez-Santos, J. C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47679-3_18 · Cited by 1
Analysis of various health parameters for early and efficient prediction of sepsis
Parashar, A., Mohan, Y., & Rathee, N. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1022/1/012002 · Cited by 1
Prediction of Pediatric Sepsis Using a Deep Encoding Network with Cross Features
Chen, X., Zhang, R., Tang, X., & Qian, J. (2022) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-022-2499-1 · Cited by 1
MACHINE LEARNING TO DEVELOP A MODEL THAT PREDICTS EARLY IMPENDING SEPSIS IN NEUROSURGICAL PATIENTS
Vlachos, E., Gkinis, A. S., Papastergiou, V., Tsitsipanis, C., & Giannakopoulos, G. (2022) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3549737.3549795 · Cited by 1
Böhnke, J., Zapf, A., Kramer, K., Weber, P., ELISE Study Group, Karch, A., Rübsamen, N., Bode, L., Mast, M., Wulff, A., et al. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111314 · Cited by 1
Early prediction of Sepsis: A comparative assessment on patients’ covariates
Mutlu, B., Yeşilyurt, M. E., Shahbazi, N., Güzel, M. S., & Sezer, E. A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106400 · Cited by 1
Using Features Extracted From Vital Time Series for Early Prediction of Sepsis
Yu, Q., Huang, X., Li, W., Wang, C., Chen, Y., & Ge, Y. J. (2019) · Computing in cardiology · doi:10.23919/cinc49843.2019.9005646 · Cited by 1
Time-Specific Metalearners for the Early Prediction of Sepsis
Vollmer, M., Luz, C. F., Sodmann, P., Sinha, B. N. M., & Kuhn, S. (2019) · Computing in cardiology · doi:10.23919/cinc49843.2019.9005610 · Cited by 1
A Multi-task LSTM Framework for Improved Early Sepsis Prediction
Tsiligkaridis, T., & Sloboda, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_5 · Cited by 1
Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study
Lee, J. Y., Molani, S., Fang, C., Jade, K., O’Mahony, D. S., Kornilov, S. A., Mico, L., & Hadlock, J. (2021) · JMIR Medical Informatics · doi:10.2196/29986 · Cited by 2
Non-Invasive Prediction Model to Detect Sepsis using Supervised Machine Learning Algorithms
(2020) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.e1012.0285s20 · Cited by 1
Using Gated Recurrent Units Models for Early Prediction of Sepsis in The Intensive Care UnitPreprint
Zhao, X., & Qu, B. (2021) · Research Square · doi:10.21203/rs.3.rs-259370/v1 · Cited by 1
Detection of Sepsis Patients Using Biomarkers Based on Machine LearningPreprint
Eskandari, M. A., Moridani, M. K., & Mohammadi, S. (2021) · Research Square · doi:10.21203/rs.3.rs-769430/v1 · Cited by 1
Maharjan, J., Thapa, R., Calvert, J. S., Attwood, M. M., Shokouhi, S., Chetty, S. C., Iqbal, Z., Singh, N. P., Arnold, R., Hoffman, J. L., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4130480 · Cited by 1
Self-citationTsang, G. (2022) · Cronfa (Swansea University) · doi:10.23889/suthesis.61814 · Cited by 1
Prediction of Intensive Care Unit Mortality Based on Missing Events
Shoji, T., Yonekura, H., Sato, Y., & Kawasaki, Y. (2021) · Archives of Clinical and Biomedical Research · doi:10.26502/acbr.50170168
Predicting Respiratory Decompensation in Mechanically Ventilated Adult ICU PatientsPreprint
Tan, Y., Young, M., Girish, A., Hu, B., Kurian, Z., Greenstein, J. L., Kim, H., Winslow, R. L., Fackler, J. C., & Bergmann, J. P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4132336
An Interpretable Approach to Prediction of Sepsis Using Multivariate Time SeriesPreprint
Baghaei, K. T., Rahimi, S., & Bozorgzad, S. (2022) · Research Square · doi:10.21203/rs.3.rs-520052/v1
The data driven intensive care unit
Fleuren, L. M. (2023) · doi:10.5463/thesis.116
Sepsis Prediction Method Based on Neural Network Combined with Multiple Life Characteristics
何, 亚. (2023) · Modeling and Simulation · doi:10.12677/mos.2023.123212
Desafios e Tendências na Predição de Sepse
Silva, L. G., Jr., Andrade, R. M. C., & Celestino, J. (2023) · doi:10.5753/sbcas.2023.230163
Vandana, & Hari, P. B. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220741
Window-Controlled Sepsis Prediction Using a Model Selection Approach
Su, S., Lan, S., Zhang, Z., & Zhu, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46677-9_31
Early Prediction of Sepsis Using Machine Learning Algorithms: A Review
Shanthi, N., Aadhishri, A., Suganthe, R. C., & Gao, X. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50993-3_10
Коровкина, А. О., Yen, V. H., Nv, B., Vybornyi, A. Y., & Zyablov, A. N. (2024) · Journal of Analytical Chemistry · doi:10.1134/s1061934824701065
Kim, Y. K., Choi, I., Seo, W.-D., Kim, G. C., Song, H. S., & Lee, M. (2024) · doi:10.2196/preprints.68323
Ezeobi, D. P., Musiimenta, A., Wasswa, W., & Kyoyagala, S. (2024) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2024.100195
Unplanned intensive care unit admissions in trauma patients: A critical appraisal
Swain, A., Nag, D. S., Laik, J. K., Sahu, S., Panchal, M., & Srirala, S. (2025) · World Journal of Critical Care Medicine · doi:10.5492/wjccm.v14.i3.105147
Burdick, H., Pino, E., Gabel-Comeau, D., Gu, C., Roberts, J., Le, S., Slote, J., Saber, N., Pellegrini, E., Green‐Saxena, A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13152208
Self-citationPredictive Analytics for Sepsis Using Time-Series Data and Recurrent Neural Networks
Althubiti, S. A. (2025) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc65530.2025.11137017
Addasi, R., Safadi, W. A., Sorour, S., Altawil, Z., Dawud, Y., Ammori, R., Alshamasneh, M., & Hamam, S. A. (2026) · Journal of Robotic Surgery · doi:10.1007/s11701-026-03276-x · Cited by 1
Early Sepsis Prediction using Explainable Machine Learning on Vital Sign Data
Kalaivani, P., R, S. S. M., Trisha, S., & K, S. S. (2026) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears67481.2026.11416658
A Systematic Review of Machine Learning Models For Sepsis Prediction: An Appraise-Ai Approach
Wang, T., Li, Q., Wang, Z., Yu, C., Yao, L., Zhou, Z., Yin, J., Liu, Y., Chen, Y., Wei, X., et al. (2026) · Shock · doi:10.1097/shk.0000000000002850
Geetha, A., Nisha, K. S., Sankar, M. S. A., & Rajeev, S. P. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8060150
CTSM: A novel cumulative-time-based stacking algorithm for predicting sepsis onset
Liu, X., Liu, S., He, W., & Mao, S. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110764
Parzniewski, M., Ostruszka, N., Marusińska, M., Karpiński, M., Czyz, W., Kolawa, S., Markiewicz, W., Borowiec, A., Pilecka, J., & Wojciechowski, J. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.3(51).2026.5768
Zhu, K., Wei, X., Wang, X., & Geng, N. (2026) · Decision Support Systems · doi:10.1016/j.dss.2026.114756
Multisource Intelligence for Early Sepsis Prediction: An Ensemble Modeling Approach
Camacho-Cogollo, J. E., Bonet, I., & Gil, B. (2026) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-37739-5_62
Predicting Sepsis in ICU Patients Using GRU-D and LightGBM for Clinical Decision Support
Goel, M., & Popli, R. (2026) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2026-0072
Microbial traits have minimal power for predicting death from bloodstream infectionPreprint
Gilliland, R., Valdes-Tresanco, M. E., Plakhotnyk, A., Westlund, A., Smith, J. T., Pham, T. M., Mortimer, T. D., Walker, B. J., Earl, A. M., Grad, Y. H., et al. (2026) · medRxiv · doi:10.64898/2026.10.08.26365081
Metadata
- Authors
- Calvert, J. S., Price, D. A., Chettipally, U. K., Barton, C. W., Feldman, M. D., Hoffman, J. L., Jay, M., & Das, R.
- Affiliations
- Dascena (United States) (US)
- University of California, San Francisco (US)
- Corresponding author
- Hoffman, Jana L.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 74
- Pages
- 69-73
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2372800617
- Cited by
- 244
- References
- 36
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 24 · 2023: 21 · 2022: 24 · 2021: 39 · 2020: 35 · 2019: 38 · 2018: 11 · 2017: 15 · 2016: 6
- FWCI
- 21.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1549867)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Keywords
- sepsis prediction, early sepsis detection, systemic inflammatory response syndrome, vital signs, intensive care unit patients, higher-order correlations, insight
- MeSH terms
- Adult, Critical Care, Diagnosis, Computer-Assisted, Female, Humans, Male, Retrospective Studies, Time Factors, Biomarkers, Sepsis
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