A computational approach to early sepsis detection

PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals

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 · see citing works

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 · see citing works

Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care

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 · see citing works

Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock

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 · see citing works

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 · see citing works

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 · see citing works

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 2,023 · see citing works

Regional Variation in Out-of-Hospital Cardiac Arrest Incidence and Outcome

Nichol, G. (2008) · JAMA · doi:10.1001/jama.300.12.1423 · Cited by 1,959 · see citing works

Validation of a modified Early Warning Score in medical admissions

Subbe, C. P. (2001) · QJM · doi:10.1093/qjmed/94.10.521 · Cited by 1,767 · see citing works

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 · see citing works

Early lactate clearance is associated with improved outcome in severe sepsis and septic shock*

Nguyen, H. B., Rivers, E. P., Knoblich, B., Jacobsen, G., Muzzin, A., Ressler, J. A., & Tomlanovich, M. C. (2004) · Critical Care Medicine · doi:10.1097/01.ccm.0000132904.35713.a7 · Cited by 1,231 · see citing works

Procalcitonin as a diagnostic marker for sepsis: A systematic review and meta-analysis.

Wacker, C., Prkno, A., Brunkhorst, F. M., & Schlattmann, P. (2013) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(12)70323-7 · Cited by 1,198 · see citing works

Treating Patients with Severe Sepsis

Wheeler, A. P., & Bernard, G. R. (1999) · New England Journal of Medicine · doi:10.1056/nejm199901213400307 · Cited by 992 · see citing works

National Vital Statistics Reports NCHS.pdf

Hoyert, D. (2012) · Medical Entomology and Zoology · Cited by 982 · see citing works

Deaths: final data for 2010.

Murphy, S. L., Xu, J., & Kochanek, K. D. (2013) · PubMed · Cited by 894 · see citing works

Two Decades of Mortality Trends Among Patients With Severe Sepsis

Stevenson, E. K., Rubenstein, A. R., Radin, G. T., Wiener, R. S., & Walkey, A. J. (2013) · Critical Care Medicine · doi:10.1097/ccm.0000000000000026 · Cited by 646 · see citing works

Implementation of a bundle of quality indicators for the early management of severe sepsis and septic shock is associated with decreased mortality*

Nguyen, H. B., Corbett, S. W., Steele, R., Banta, J., Clark, R. T., Hayes, S. R., Edwards, J., Cho, T. W., & Wittlake, W. A. (2007) · Critical Care Medicine · doi:10.1097/01.ccm.0000259463.33848.3d · Cited by 555 · see citing works

Inpatient care for septicemia or sepsis: a challenge for patients and hospitals.

Hall, M. J., Williams, S. N., DeFrances, C. J., & Golosinskiy, A. (2011) · PubMed · Cited by 465 · see citing works

National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2011

Torio, C. M., & Andrews, R. M. (2013) · Cited by 413 · see citing works

Deaths: Final Data for 2003

Hoyert, D. L., Heron, M. P., Murphy, S. L., & Kung, H.-C. (2006) · PsycEXTRA Dataset · doi:10.1037/e518032006-001 · Cited by 362 · see citing works

Systemic inflammatory response syndrome, sepsis, severe sepsis and septic shock: Incidence, morbidities and outcomes in surgical ICU patients

Pittet, D., Rangel-Frausto, S., Li, N., Tarara, D. T., Costigan, M., Rempe, L., JEBSON, P. J. R., & Wenzel, R. P. (1995) · Intensive Care Medicine · doi:10.1007/bf01705408 · Cited by 330 · see citing works

Development, implementation, and impact of an automated early warning and response system for sepsis

Umscheid, C. A., Betesh, J., VanZandbergen, C., Hanish, A., Tait, G., Mikkelsen, M. E., French, B. C., & Fuchs, B. D. (2014) · Journal of Hospital Medicine · doi:10.1002/jhm.2259 · Cited by 167 · see citing works

Prognostic and diagnostic value of eosinopenia, C-reactive protein, procalcitonin, and circulating cell-free DNA in critically ill patients admitted with suspicion of sepsis

Garnacho‐Montero, J., Huici-Moreno, M. J., Gutiérrez-Pizarraya, A., López, I., Márquez-Vácaro, J. A., Macher, H. C., Guerrero, J. M., & Puppo-Moreno, A. M. (2014) · Critical Care · doi:10.1186/cc13908 · Cited by 145 · see citing works

Prospective Trial of Real-Time Electronic Surveillance to Expedite Early Care of Severe Sepsis

Nelson, J. L., Smith, B. L., Jared, J. D., & Younger, J. G. (2011) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2010.12.008 · Cited by 122 · see citing works

Diagnostic Characteristics of a Clinical Screening Tool in Combination With Measuring Bedside Lactate Level in Emergency Department Patients With Suspected Sepsis

Singer, A. J., Taylor, M., Domingo, A., Ghazipura, S., Khorasonchi, A., Thode, H. C., & Shapiro, N. I. (2014) · Academic Emergency Medicine · doi:10.1111/acem.12444 · Cited by 88 · see citing works

Detection of procalcitonin (PCT) in healthy controls and patients with local infection by a sensitive ILMA.

Morgenthaler, N. G., Struck, J., Fischer-Schulz, C., Seidel-Mueller, E., Beier, W., & Bergmann, A. (2002) · PubMed · Cited by 88 · see citing works

Use of serum procalcitonin to detect bacterial infection in patients with autoimmune diseases: A systematic review and meta‐analysis

Wu, J.-Y., Lee, S., Shen, C., Hsieh, Y.-C., Yo, P.-H., Cheng, H., Chan, R., Lee, C., & Chang, S. (2012) · Arthritis & Rheumatism · doi:10.1002/art.34512 · Cited by 70 · see citing works

Enrollment into a time sensitive clinical study in the critical care setting: results from computerized septic shock sniffer implementation

Herasevich, V., Pieper, M. S., Pulido, J. N., & Gajic, O. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000228 · Cited by 69 · see citing works

Automated electronic medical record sepsis detection in the emergency department

Nguyen, S. Q., Mwakalindile, E., Booth, J. S., Hogan, V., Morgan, J., Prickett, C. T., Donnelly, J. P., & Wang, H. E. (2014) · PeerJ · doi:10.7717/peerj.343 · Cited by 68 · see citing works

Septic shock : providing early warnings through multivariate logistic regression models

Shavdia, D. (2007) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 27 · see citing works

Deaths: final data for 2003, United States

Hoyert, D. L., Heron, M. P., Murphy, S. L., & Kung, H.-C. (2006) · National vital statistics reports · Cited by 21 · see citing works

Implementación de un sistema de alarmas automático para la detección precoz de los pacientes con sepsis grave

Ferreras, J. M., Júdez, D., Tirado, G., Aspíroz, C., Martínez-Álvarez, R., Dorado, P., Galindo, A. I. E., Marrón, R., Gargallo, B., & Herranz, C. (2015) · Enfermedades Infecciosas y Microbiología Clínica · doi:10.1016/j.eimc.2015.01.002 · Cited by 16 · see citing works

Discharge recommendation based on a novel technique of homeostatic analysis

Calvert, J. S., Price, D. A., Barton, C. W., Chettipally, U. K., & Das, R. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw014 · Cited by 15 · see citing works

Implementación de un sistema de alarmas automático para la detección precoz de los pacientes con sepsis grave

Ferreras, J. M., Dorado, P., Aspíroz, C., & Martínez, R. (2016) · Enfermedades Infecciosas y Microbiología Clínica · doi:10.1016/j.eimc.2015.11.014 · Cited by 9 · see citing works

Artificial intelligence, bias and clinical safety

Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956 · see citing works

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Fleuren, L. M., Klausch, T. L. T., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., Swart, E. L., Girbes, A. R. J., Thoral, P., Ercole, A., et al. (2020) · Intensive Care Medicine · doi:10.1007/s00134-019-05872-y · Cited by 755 · see citing works

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2019.09.009 · Cited by 561 · see citing works

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., Shimabukuro, D., Chettipally, U., Feldman, M. D., Barton, C., et al. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.5909 · Cited by 509 · see citing works

Explainable artificial intelligence model to predict acute critical illness from electronic health records

Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: A randomised clinical trial.

Shimabukuro, D. W., Barton, C. W., Feldman, M. D., Mataraso, S. J., & Das, R. (2017) · BMJ Open Respiratory Research · doi:10.1136/bmjresp-2017-000234 · Cited by 382 · see citing works

Axes of a revolution: challenges and promises of big data in healthcare

Shilo, S., Rossman, H., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0727-5 · Cited by 370 · see citing works

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Mao, Q., Jay, M., Hoffman, J. L., Calvert, J., Barton, C., Shimabukuro, D., Shieh, L., Chettipally, U., Fletcher, G., Kerem, Y., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-017833 · Cited by 336 · see citing works

Emerging Technologies for Molecular Diagnosis of Sepsis

Sinha, M., Jupe, J., Mack, H., Coleman, T. P., Lawrence, S. M., & Fraley, S. I. (2018) · Clinical Microbiology Reviews · doi:10.1128/cmr.00089-17 · Cited by 332 · see citing works

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Sendak, M. P., Ratliff, W., Sarro, D., Alderton, E., Futoma, J., Gao, M., Nichols, M., Revoir, M., Yashar, F., Miller, C., et al. (2020) · JMIR Medical Informatics · doi:10.2196/15182 · Cited by 287 · see citing works

Prediction of sepsis patients using machine learning approach: a meta-analysis

Islam, M. M., Nasrin, T., Walther, B. A., Wu, C.-C., Yang, H.-C., & Li, Y.-C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.12.027 · Cited by 259 · see citing works

Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review

Moor, M., Rieck, B., Horn, M., Jutzeler, C. R., & Borgwardt, K. M. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.607952 · Cited by 234 · see citing works

Learning representations for the early detection of sepsis with deep neural networks

Kam, H. J., & Kim, H. Y. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.015 · Cited by 218 · see citing works

Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis

Delahanty, R. J., Alvarez, J., Flynn, L. M., Sherwin, R. L., & Jones, S. S. (2019) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2018.11.036 · Cited by 206 · see citing works

Early detection of sepsis utilizing deep learning on electronic health record event sequences

Lauritsen, S. M., Kalør, M. E., Kongsgaard, E. L., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101820 · Cited by 193 · see citing works

Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data

Masino, A. J., Harris, M. C., Forsyth, D., Ostapenko, S., Srinivasan, L., Bonafide, C. P., Balamuth, F., Schmatz, M., & Grundmeier, R. W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212665 · Cited by 183 · see citing works

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Barton, C., Chettipally, U., Zhou, Y., Jiang, Z., Lynn-Palevsky, A., Le, S., Calvert, J., & Das, R. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.027 · Cited by 175 · see citing works

Predicting sepsis with a recurrent neural network using the MIMIC III database

Scherpf, M., Gräßer, F., Malberg, H., & Zaunseder, S. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103395 · Cited by 167 · see citing works

Clinician perception of a machine learning–based early warning system designed to predict severe sepsis and septic shock

Ginestra, J. C., Giannini, H. M., Schweickert, W. D., Meadows, L., Lynch, M. J., Pavan, K., Chivers, C. J., Draugelis, M., Donnelly, P. J., Fuchs, B. D., et al. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000003803 · Cited by 159 · see citing works

Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units

McCoy, A., & Das, R. (2017) · BMJ Open Quality · doi:10.1136/bmjoq-2017-000158 · Cited by 148 · see citing works

Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data

Mohamadlou, H., Lynn-Palevsky, A., Barton, C., Chettipally, U. K., Shieh, L. Y., Calvert, J. S., Saber, N., & Das, R. (2018) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358118776326 · Cited by 161 · see citing works

Timing of antibiotic therapy in the ICU

Kollef, M. H., Shorr, A. F., Bassetti, M., Timsit, J.-F., Micek, S. T., Michelson, A. P., & Garnacho-Montero, J. (2021) · Critical Care · doi:10.1186/s13054-021-03787-z · Cited by 161 · see citing works

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients

Wang, D., Li, J., Sun, Y., Ding, X., Zhang, X., Liu, S., Han, B., Wang, H., Duan, X., & Sun, T. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.754348 · Cited by 146 · see citing works

Clinical applications of artificial intelligence in sepsis: A narrative review

Schinkel, M., Paranjape, K., Nannan Panday, R. S., Skyttberg, N., & Nanayakkara, P. W. B. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103488 · Cited by 130 · see citing works

Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach

Desautels, T., Das, R., Calvert, J. S., Trivedi, M., Summers, C., Wales, D. J., & Ercole, A. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-017199 · Cited by 126 · see citing works

LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock

Fagerström, J., Bång, M., Wilhelms, D., & Chew, M. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-51219-4 · Cited by 125 · see citing works

Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review

Islam, K. R., Prithula, J., Kumar, J., Tan, T. L., Reaz, M. B. I., Sumon, M. S. I., & Chowdhury, M. E. H. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12175658 · Cited by 124 · see citing works

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis

Yang, M., Liu, C., Wang, X., Li, Y., Gao, H., Liu, X., & Li, J. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004550 · Cited by 120 · see citing works

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

Tsopra, R., Fernández, X. M., Luchinat, C., Alberghina, L., Lehrach, H. R., Vanoni, M., Dreher, F., Sezerman, O. U., Marc Cuggia, A., de Tayrac, M., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01634-3 · Cited by 117 · see citing works

Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence

Bates, D. W., Auerbach, A. D., Schulam, P., Wright, A. T., & Saria, S. (2020) · Annals of Internal Medicine · doi:10.7326/m19-0872 · Cited by 122 · see citing works

Pediatric Severe Sepsis Prediction Using Machine Learning

Le, S., Hoffman, J. L., Barton, C., Fitzgerald, J. C., Allen, A. O., Pellegrini, E., Calvert, J. S., & Das, R. (2019) · Frontiers in Pediatrics · doi:10.3389/fped.2019.00413 · Cited by 112 · see citing works

Machine learning for early detection of sepsis: an internal and temporal validation study

Bedoya, A. D., Futoma, J., Clement, M. E., Corey, K., Brajer, N., Lin, A., Simons, M. G., Gao, M., Nichols, M., Balu, S., et al. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa006 · Cited by 109 · see citing works

Machine learning in infection management using routine electronic health records: Tools, techniques, and reporting of future technologies

Luz, C. F., Vollmer, M., Decruyenaere, J., Nijsten, M. W., Glasner, C., & Sinha, B. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2020.02.003 · Cited by 106 · see citing works

Machine learning for prediction of septic shock at initial triage in emergency department

Kim, J., Chang, H., Kim, D., Jang, D., Park, I., & Kim, K. (2019) · Journal of Critical Care · doi:10.1016/j.jcrc.2019.09.024 · Cited by 104 · see citing works

Optimal intensive care outcome prediction over time using machine learning

Meiring, C., Dixit, A., Harris, S. K., MacCallum, N. S., Brealey, D. A., Watkinson, P., Jones, A., Ashworth, S., Beale, R. J., Brett, S. J., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206862 · Cited by 96 · see citing works

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

Chiew, C. J., Liu, N., Tagami, T., Wong, T. H., Koh, Z. X., & Ong, M. E. H. (2019) · Medicine · doi:10.1097/md.0000000000014197 · Cited by 92 · see citing works

Automated prediction of sepsis using temporal convolutional network

Kok, C., Jahmunah, V., Oh, S. L., Zhou, X., Gururajan, R., Tao, X., Cheong, K. H., Gururajan, R., Molinari, F., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103957 · Cited by 90 · see citing works

Biomarkers for Point-of-Care Diagnosis of Sepsis

Teggert, A., Datta, H. K., & Ali, Z. (2020) · Micromachines · doi:10.3390/mi11030286 · Cited by 93 · see citing works

SSP: Early prediction of sepsis using fully connected LSTM-CNN model

Rafiei, A., Rezaee, A., Hajati, F., Gheisari, S., & Golzan, M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104110 · Cited by 87 · see citing works

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring

Li, F., Wang, S., Gao, Z., Qing, M., Pan, S. L., Liu, Y., & Hu, C. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2024.1510792 · Cited by 87 · see citing works

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

Burdick, H., Pino, E., Gabel-Comeau, D., McCoy, A., Gu, C., Roberts, J., Le, S., Slote, J., Pellegrini, E., Green-Saxena, A., et al. (2020) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-100109 · Cited by 80 · see citing works

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability

León, C., Carrault, G., Pladys, P., & Beuchée, A. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3021662 · Cited by 79 · see citing works

Using electronic health record collected clinical variables to predict medical intensive care unit mortality

Calvert, J. S., Mao, Q., Hoffman, J. L., Jay, M., Desautels, T., Mohamadlou, H., Chettipally, U. K., & Das, R. (2016) · Annals of Medicine and Surgery · doi:10.1016/j.amsu.2016.09.002 · Cited by 77 · see citing works

Machine Learning To Predict Serious Bacterial Infections in Young Febrile Infants

Ramgopal, S., Horvat, C. M., Yanamala, N. V. K., & Alpern, E. R. (2020) · PEDIATRICS · doi:10.1542/peds.2019-4096 · Cited by 75 · see citing works

Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship—A Systematic Review

Pinto-de-Sá, R., Sousa‐Pinto, B., & Costa‐de‐Oliveira, S. (2024) · Antibiotics · doi:10.3390/antibiotics13040307 · Cited by 73 · see citing works

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework

van der Vegt, A. H., Scott, I. A., Dermawan, K., Schnetler, R. J., Kalke, V. R., & Lane, P. J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad075 · Cited by 72 · see citing works

A deep learning approach for sepsis monitoring via severity score estimation

Aşuroğlu, T., & Oğul, H. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105816 · Cited by 69 · see citing works

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Chicco, D., & Jurman, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-73558-3 · Cited by 65 · see citing works

Early recognition of multiple sclerosis using natural language processing of the electronic health record

Chase, H. S., Mitrani, L. R., Lu, G. G., & Fulgieri, D. J. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0418-4 · Cited by 68 · see citing works

Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems

Kindle, R. D., Badawi, O., Celi, L. A., & Sturland, S. (2019) · Critical Care Clinics · doi:10.1016/j.ccc.2019.02.005 · Cited by 67 · see citing works

Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction

Bloch, E., Rotem, T., Cohen, J. D., Singer, P., & Aperstein, Y. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/5930379 · Cited by 66 · see citing works

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Giacobbe, D. R., Signori, A., Puente, F. D., Mora, S., Carmisciano, L., Briano, F., Vena, A., Ball, L., Robba, C., Pelosi, P. P., et al. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.617486 · Cited by 68 · see citing works

A time series driven model for early sepsis prediction based on transformer module

Tang, Y., Zhang, Y., & Li, J. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02138-6 · Cited by 63 · see citing works

High-performance detection and early prediction of septic shock for alcohol-use disorder patients

Calvert, J., Desautels, T., Chettipally, U., Barton, C., Hoffman, J., Jay, M., Mao, Q., Mohamadlou, H., & Das, R. (2016) · Annals of Medicine & Surgery · doi:10.1016/j.amsu.2016.04.023 · Cited by 62 · see citing works

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Rosnati, M., & Fortuin, V. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251248 · Cited by 60 · see citing works

Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning

El-Rashidy, N. M., Abuhmed, T., Alarabi, L., El-Bakry, H. M., Abdelrazek, S. M., Ali, F., & El–Sappagh, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06631-1 · Cited by 57 · see citing works

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis

Rockenschaub, P., Akay, E. M., Carlisle, B. G., Hilbert, A., Wendland, J., Meyer-Eschenbach, F., Näher, A.-F., Frey, D., & Madai, V. I. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02830-7 · Cited by 58 · see citing works

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

Li, K., Shi, Q., Liu, S., Xie, Y., & Liu, J. (2021) · Medicine · doi:10.1097/md.0000000000025813 · Cited by 53 · see citing works

Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting

Desautels, T., Calvert, J. S., Hoffman, J. L., Mao, Q., Jay, M., Fletcher, G., Barton, C. W., Chettipally, U. K., Kerem, Y., & Das, R. (2017) · Biomedical Informatics Insights · doi:10.1177/1178222617712994 · Cited by 52 · see citing works

Utilization of the Signature Method to Identify the Early Onset of Sepsis From Multivariate Physiological Time Series in Critical Care Monitoring

Morrill, J., Kormilitzin, A., Nevado‐Holgado, A. J., Swaminathan, S., Howison, S. D., & Lyons, T. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004510 · Cited by 50 · see citing works

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Rehm, G. B., Han, J., Kuhn, B., Delplanque, J., Anderson, N., Adams, J. Y., & Chuah, C. (2018) · Methods of Information in Medicine · doi:10.3414/me17-02-0012 · Cited by 50 · see citing works

Transforming sepsis management: AI-driven innovations in early detection and tailored therapies

Papareddy, P., Lobo, T. J., Holub, M., Bouma, H., Maca, J., Strodthoff, N., & Herwald, H. (2025) · Critical Care · doi:10.1186/s13054-025-05588-0 · Cited by 49 · see citing works

Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients

Alanazi, A., Aldakhil, L., Aldhoayan, M. D., & Aldosari, B. (2023) · Medicina · doi:10.3390/medicina59071276 · Cited by 49 · see citing works

Multi-Branching Temporal Convolutional Network for Sepsis Prediction

Wang, Z., & Yao, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3092835 · Cited by 47 · see citing works

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review

Wulff, A., Montag, S., Marschollek, M., & Jack, T. (2019) · Methods of Information in Medicine · doi:10.1055/s-0039-1695717 · Cited by 49 · see citing works

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

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) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01284-x · Cited by 47 · see citing works

Phycotoxins by Harmful Algal Blooms (HABS) and Human Poisoning: An Overview

Pulido, O. M. (2016) · International Clinical Pathology Journal · doi:10.15406/icpjl.2016.02.00062 · Cited by 43 · see citing works

Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults

Mohammed, A., van Wyk, F., Chinthala, L. K., Khojandi, A., Davis, R. L., Coopersmith, C. M., & Kamaleswaran, R. (2020) · Shock · doi:10.1097/shk.0000000000001670 · Cited by 43 · see citing works

Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review

Hassan, N., Slight, R., Weiand, D., Vellinga, A., Morgan, G., Aboushareb, F., & Slight, S. P. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104457 · Cited by 42 · see citing works

Supervised classification techniques for prediction of mortality in adult patients with sepsis

Rodríguez, A., Mendoza, D., Ascuntar, J., & Jaimes, F. A. (2020) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2020.09.013 · Cited by 42 · see citing works

Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features

Liu, S., Fu, B., Wang, W., Liu, M., & Sun, X. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3171673 · Cited by 41 · see citing works

Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis

Yadgarov, M. Y., Landoni, G., Бондоевич, Б. Л., Polyakov, P. A., Kadantseva, K., Smirnova, A. V., Kuznetsov, I. V., Shemetova, M. M., Yakovlev, A. А., & Likhvantsev, V. V. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1491358 · Cited by 41 · see citing works

A Review of Predictive Analytics Solutions for Sepsis Patients

Teng, A. K., & Wilcox, A. B. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1710525 · Cited by 45 · see citing works

Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis

Nesaragi, N., Patidar, S., & Aggarwal, V. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104430 · Cited by 39 · see citing works

A neural network approach to predict early neonatal sepsis

Martínez, F., Núñez‐Valdéz, E. R., Gómez, J. L., & García‐Díaz, V. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.04.015 · Cited by 39 · see citing works

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice

Petersson, L., Vincent, K., Svedberg, P., Nygren, J., & Larsson, I. (2023) · Digital Health · doi:10.1177/20552076231206588 · Cited by 35 · see citing works

Predicting Infections Using Computational Intelligence – A Systematic Review

Baldominos, A., Puello, A., Oğul, H., Aşuroğlu, T., & Colomo‐Palacios, R. (2020) · IEEE Access · doi:10.1109/access.2020.2973006 · Cited by 35 · see citing works

Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets

Camacho-Cogollo, J. E., Bonet, I., Gil, B., & Iadanza, E. (2022) · Electronics · doi:10.3390/electronics11091507 · Cited by 35 · see citing works

Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features

Nesaragi, N., & Patidar, S. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004691 · Cited by 35 · see citing works

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset

Sadasivuni, S., Saha, M., Bhatia, N. K., Banerjee, I., & Sanyal, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-09712-w · Cited by 35 · see citing works

Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients

Calvert, J. S., Saber, N., Hoffman, J. L., & Das, R. (2019) · Diagnostics · doi:10.3390/diagnostics9010020 · Cited by 33 · see citing works

Heart Rate Variability as a Biomarker of Neurocardiogenic Injury After Subarachnoid Hemorrhage

Megjhani, M., Kaffashi, F., Terilli, K., Alkhachroum, A. M., Esmaeili, B., Doyle, K. W., Murthy, S. B., Velazquez, A. G., Connolly, E. S., Roh, D. J., et al. (2019) · Neurocritical Care · doi:10.1007/s12028-019-00734-3 · Cited by 32 · see citing works

Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring

Keim‐Malpass, J., Clark, M., Lake, D. E., & Moorman, J. R. (2019) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-019-00361-5 · Cited by 32 · see citing works

Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation

Lin, P.-C., Chen, K.-T., Chen, H.-C., Islam, M. M., & Lin, M. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11111055 · Cited by 32 · see citing works

Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant

Lind, M. L., Mooney, S. J., Carone, M., Althouse, B. M., Liu, C., Evans, L., Patel, K. B., Vo, P., Pergam, S. A., & Phipps, A. I. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.4514 · Cited by 31 · see citing works

A review of recent advances in data analytics for post-operative patient deterioration detection

Petit, C., Bezemer, R., & Atallah, L. (2017) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-017-0054-7 · Cited by 30 · see citing works

Early prediction of sepsis using double fusion of deep features and handcrafted features

Duan, Y., Huo, J., Chen, M., Hou, F., Yan, G., Li, S., & Wang, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04425-z · Cited by 30 · see citing works

Cost and mortality impact of an algorithm-driven sepsis prediction system

Calvert, J. S., Hoffman, J. L., Barton, C., Shimabukuro, D. W., Ries, M. H., Chettipally, U. K., Kerem, Y., Jay, M., Mataraso, S. J., & Das, R. (2017) · Journal of Medical Economics · doi:10.1080/13696998.2017.1307203 · Cited by 29 · see citing works

Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study

Wang, S., Liu, X., Yuan, S., Bian, Y., Wu, H. M., & Ye, Q. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01643-w · Cited by 27 · see citing works

The Interplay between Host Defense, Infection, and Clinical Status in Septic Patients: A Narrative Review

Lazzaro, A., Girolamo, G. D., Filippi, V., Innocenti, G. P., Santinelli, L., Ceccarelli, G., Trecarichi, E. M., Torti, C., Mastroianni, C. M., d’Ettorre, G., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23020803 · Cited by 29 · see citing works

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning

Bock, C., Moor, M., Jutzeler, C. R., & Borgwardt, K. M. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_2 · Cited by 26 · see citing works

Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review

Kausch, S. L., Moorman, J. R., Lake, D. E., & Keim‐Malpass, J. (2021) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2021.103035 · Cited by 26 · see citing works

Development and clinical impact assessment of a machine-learning model for early prediction of late-onset sepsis

van den Berg, M., Medina, O. A. G., Loohuis, I., van der Flier, M., Dudink, J., Benders, M. J. N. L., Bartels, R., & Vijlbrief, D. C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107156 · Cited by 26 · see citing works

LSTM for septic shock: Adding unreliable labels to reliable predictions

Zhang, Y., Lin, C., Chi, M., Ivy, J. S., Capan, M., & Huddleston, J. M. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258049 · Cited by 25 · see citing works

Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis

Plate, J. D. J., van de Leur, R. R., Leenen, L. P. H., Hietbrink, F., Peelen, L. M., & Eijkemans, M. J. C. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0847-0 · Cited by 25 · see citing works

A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction

Liu, Z., Khojandi, A., Li, X., Mohammed, A., Davis, R. L., & Kamaleswaran, R. (2022) · INFORMS journal on computing · doi:10.1287/ijoc.2022.1176 · Cited by 24 · see citing works

Bi-Directional Gated Recurrent Unit Based Ensemble Model for the Early Detection of Sepsis

Wickramaratne, S., & Mahmud, M. S. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175223 · Cited by 23 · see citing works

Towards an Explainable Model for Sepsis Detection Based on Sensitivity Analysis

Chen, M., & Hernández, A. (2022) · IRBM · doi:10.1016/j.irbm.2021.05.006 · Cited by 23 · see citing works

Association mapping in biomedical time series via statistically significant shapelet mining

Bock, C., Gumbsch, T., Moor, M., Rieck, B., Roqueiro, D., & Borgwardt, K. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty246 · Cited by 22 · see citing works

Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

He, W., Xie, Y., Lu, H., Wang, M., & Chen, H. (2020) · Symmetry · doi:10.3390/sym12101651 · Cited by 22 · see citing works

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., Shimabukuro, D., Chettipally, U., Feldman, M. D., Barton, C., et al. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.5909 · Cited by 509 · see citing works

Early Prediction of Sepsis Considering Early Warning Scoring Systems

Biglarbeigi, P., McLaughlin, D., Rjoob, K., Abdullah, A., McCallan, N., Piadlo, A., Bond, R., Finlay, D., Ng, K. Y., Kennedy, A., et al. (2019) · Computing in cardiology · doi:10.22489/cinc.2019.051 · Cited by 6 · see citing works

Performance effectiveness of vital parameter combinations for early warning of sepsis—an exhaustive study using machine learning

Rangan, E. S., Pathinarupothi, R. K., Anand, K. J. S., & Snyder, M. P. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac080 · Cited by 26 · see citing works

A Comprehensive Machine Learning Based Pipeline for an Accurate Early Prediction of Sepsis in ICU

Srimedha, B. C., Raj, R. N., & Mayya, V. (2022) · IEEE Access · doi:10.1109/access.2022.3210575 · Cited by 17 · see citing works

Assessment of the timeliness and robustness for predicting adult sepsis

Guan, Y., Wang, X., Chen, X., Yi, D., Chen, L., & Jiang, X. (2021) · iScience · doi:10.1016/j.isci.2021.102106 · Cited by 7 · see citing works