CoChat
Sign Up
Cited by 49Sepsis Diagnosis and TreatmentMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education

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

Open access
Papareddy, Praveen, Lobo, Thamar Jessurun, Holub, Michal, Bouma, Hjalmar, Maca, Jan, Strodthoff, Nils, Herwald, Heiko·2025·Critical Care
DOI 10.1186/s13054-025-05588-0PMC PMC12366378
Open this paper in CoChat — free accountOpen PDF

Abstract

Sepsis remains a leading cause of mortality worldwide, driven by its clinical complexity and delayed recognition. Artificial intelligence (AI) offers promising solutions to improve sepsis care through earlier detection, risk stratification, and personalized treatment strategies. Key applications include AI-driven early warning systems, subphenotyping based on clinical and biological data, and decision support tools that adapt to real-time patient information. The integration of diverse data types, such as structured clinical data, unstructured notes, waveform signals, and molecular biomarkers, enhances the precision and timeliness of interventions. However, challenges such as algorithmic bias, limited external validation, data quality issues, and ethical considerations continue to hinder clinical implementation. Future directions focus on real-time model adaptation, multi-omics integration, and the development of generalist medical AI capable of personalized recommendations. Successfully addressing these barriers is essential for AI to deliver on its potential to transform sepsis management and support the transition toward precision-driven critical care.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s13054-025-05588-0

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • External evaluations of widely deployed proprietary systems, such as the Epic Sepsis Model, have reported poor discrimination and calibration in routine clinical use despite broad adoption.

    “external evaluations of widely deployed proprietary systems, such as the Epic Sepsis Model, have reported poor discrimination and calibration in routine clinical use despite broad adoption [15,16].”

    From Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems (2026) · cites this paper as [16]

    The Early Detection and Early Warning Systems section reports that the widely adopted Epic Sepsis Model had poor discrimination in independent validation (AUC about 0.63). The Foundational Issues section also reports that an external evaluation found it missed 67% of sepsis cases. The paper discusses poor calibration as a possible limitation of models generally, but does not report a finding of poor calibration for the Epic Sepsis Model specifically; page numbers are unavailable in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 108

  1. [1]Angus DC, van der Poll T. Severe sepsis and septic shock. N Engl J Med. 2013;369(9):840–51. 23984731
  2. [2]Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi DT, Kievlan DR, Colombara DV, Ikuta KS, Kisson N, Finfer S, et al. Global, regional, and National sepsis incidence and mortality, 1990–2017: analysis for the global burden of disease study. Lancet. 2020;395(10219):200–11. 31954465 PMC6970225
  3. [3]Stevenson EK, Rubenstein AR, Radin GT, Wiener RS, Walkey AJ. Two decades of mortality trends among patients with severe sepsis: a comparative meta-analysis. Crit Care Med. 2014;42(3):625–31. 24201173 PMC4313930
  4. [5]Hotchkiss RS, Moldawer LL, Opal SM, Reinhart K, Turnbull IR, Vincent JL. Sepsis and septic shock. Nat Reviews Disease Primers. 2016;2:16045. 28117397 PMC5538252
  5. [6]Jentzer JC, Lawler PR, Van Houten HK, Yao X, Kashani KB, Dunlay SM. Cardiovascular events among survivors of sepsis hospitalization: A retrospective cohort analysis. J Am Heart Assoc. 2023;12(3):e027813. 36722388 PMC9973620
  6. [7]Nedeva C. Inflammation and cell death of the innate and adaptive immune system during sepsis. Biomolecules 2021, 11(7). 10.3390/biom11071011 PMC8301842 34356636 · DOI
  7. [8]Iba T, Umemura Y, Wada H, Levy JH. Roles of coagulation abnormalities and microthrombosis in sepsis: pathophysiology, diagnosis, and treatment. Arch Med Res. 2021;52(8):788–97. 34344558
  8. [9]Cajander S, Kox M, Scicluna BP, Weigand MA, Mora RA, Flohe SB, Martin-Loeches I, Lachmann G, Girardis M, Garcia-Salido A, et al. Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine. Lancet Respir Med. 2024;12(4):305–22. 38142698
  9. [10]Im Y, Kang D, Ko RE, Lee YJ, Lim SY, Park S, Na SJ, Chung CR, Park MH, Oh DK, et al. Time-to-antibiotics and clinical outcomes in patients with sepsis and septic shock: a prospective nationwide multicenter cohort study. Crit Care. 2022;26(1):19. 35027073 PMC8756674
  10. [11]Meyer NJ, Prescott HC. Sepsis and septic shock. N Engl J Med. 2024;391(22):2133–46. 39774315
  11. [12]Collaborators GBDAR. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Lancet. 2024;404(10459):1199–226. 39299261 PMC11718157
  12. [13]Vincent JL, van der Poll T, Marshall JC. The end of one size fits all sepsis therapies: toward an individualized approach. Biomedicines 2022, 10(9). 10.3390/biomedicines10092260 PMC9496597 36140361 · DOI
  13. [15]Ward PA. New approaches to the study of sepsis. EMBO Mol Med. 2012;4(12):1234–43. 23208733 PMC3531600
  14. [16]Im Y, Kang D, Ko RE, Lee YJ, Lim SY, Park S, Na SJ, Chung CR, Park MH, Oh DK et al. Time-to-antibiotics and clinical outcomes in patients with sepsis and septic shock: a prospective nationwide multicenter cohort study. Crit Care 2022, 26(1). 10.1186/s13054-021-03883-0 PMC8756674 35027073 · DOI
  15. [17]McMullan RR, McAuley DF, O’Kane CM, Silversides JA. Vascular leak in sepsis: physiological basis and potential therapeutic advances. Crit Care. 2024;28(1):97. 38521954 PMC10961003
  16. [18]Williams B, Zou L, Pittet JF, Chao W. Sepsis-Induced coagulopathy: A comprehensive narrative review of pathophysiology, clinical presentation, diagnosis, and management strategies. Anesth Analg. 2024;138(4):696–711. 38324297 PMC10916756
  17. [19]Gorecki GP, Tomescu DR, Ples L, Panaitescu AM, Dragosloveanu S, Scheau C, Sima RM, Coman IS, Grigorean VT, Cochior D. Implications of using artificial intelligence in the diagnosis of sepsis/sepsis shock. Germs. 2024;14(1):77–84. 39169980 PMC11333838
  18. [20]Giamarellos-Bourboulis EJ, Aschenbrenner AC, Bauer M, Bock C, Calandra T, Gat-Viks I, Kyriazopoulou E, Lupse M, Monneret G, Pickkers P, et al. The pathophysiology of sepsis and precision-medicine-based immunotherapy. Nat Immunol. 2024;25(1):19–28. 38168953
  19. [21]Li F, Wang S, Gao Z, Qing M, Pan S, Liu Y, Hu C. Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring. Front Med (Lausanne). 2024;11:1510792. 39835096 PMC11743359
  20. [22]Stylianides C, Nicolaou A, Sulaiman WA, Alexandropoulou CA, Panagiotopoulos I, Karathanasopoulou K, Dimitrakopoulos G, Kleanthous S, Politi E, Ntalaperas D et al. AI advances in ICU with an emphasis on sepsis prediction: an overview. Mach Learn Know Extr 2025, 7(1). 10.3390/make7010006 · DOI · Paper page
  21. [23]Agnello L, Vidali M, Padoan A, Lucis R, Mancini A, Guerranti R, Plebani M, Ciaccio M, Carobene A. Machine learning algorithms in sepsis. Clin Chim Acta. 2024;553:117738. 38158005
  22. [24]Gao YL, Wang CL, Shen JX, Wang ZY, Liu YC, Chai YF. Systematic review and network meta-analysis of machine learning algorithms in sepsis prediction. Expert Syst Appl 2024, 245. 10.1016/j.eswa.2023.122982 · DOI
  23. [25]Wang D, Li J, Sun Y, Ding X, Zhang X, Liu S, Han B, Wang H, Duan X, Sun T. A machine learning model for accurate prediction of sepsis in ICU patients. Front Public Health. 2021;9:754348. 34722452 PMC8553999
  24. [27]Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4(1):e000234. 29435343 PMC5687546
  25. [28]Tekdos Seker Y, Cukurova Z, Ozel Bilgi D, Hergunsel O. Prognostic impact of early versus late initiation of renal replacement therapy based on early warning algorithm in critical care patients with acute kidney injury. Ther Apher Dial. 2020;24(4):445–52. 31661596
  26. [29]Prasad V, Aydemir B, Kehoe IE, Kotturesh C, O’Connell A, Biebelberg B, Wang Y, Lynch JC, Pepino JA, Filbin MR, et al. Diagnostic suspicion bias and machine learning: breaking the awareness deadlock for sepsis detection. PLOS Digit Health. 2023;2(11):e0000365. 37910497 PMC10619833
  27. [30]Goh KH, Wang L, Yeow AYK, Poh H, Li K, Yeow JJL, Tan GYH. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun 2021, 12(1). 10.1038/s41467-021-20910-4 PMC7846756 33514699 · DOI · Paper page
  28. [31]Schinkel M, van der Poll T, Wiersinga WJ. Artificial intelligence for early sepsis detection A word of caution. Am J Resp Crit Care. 2023;207(7):853–4. PMC10111986 36724366
  29. [32]Nemati S, Shashikumar SP, Holder AL, Wardi G, OR L. Randomized clinical trials or convenient controls: TREWS or FALSE? MedRxiv Preprint. 2022. 10.1038/s41591-41022-01894-41590. · DOI
  30. [33]Goh KH, Wang L, Yeow AYK, Poh H, Li K, Yeow JJL, Tan GYH. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun. 2021;12(1):711. 33514699 PMC7846756
  31. [35]Schinkel M, van der Poll T, Wiersinga WJ. Artificial intelligence for early sepsis detection: A word of caution. Am J Respir Crit Care Med. 2023;207(7):853–4. 36724366 PMC10111986
  32. [36]Zhang Z, Chen L, Xu P, Wang Q, Zhang J, Chen K, Clements CM, Celi LA, Herasevich V, Hong Y. Effectiveness of automated alerting system compared to usual care for the management of sepsis. NPJ Digit Med. 2022;5(1):101. 35854120 PMC9296632
  33. [37]van Calster B, Collins G, Vickers A, Wynants L, Kerr K, Barreñada L, Varoquaux G, Singh K, Moons K, Hernandez-Boussard T, et al. Performance evaluation of predictive AI models to support medical decisions: overview and guidance. ArXiv. 2024. 10.48550/ArXiv.42412.10288.36945687 · DOI
  34. [38]Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716–20. 30349085
  35. [40]Boussina A, Shashikumar SP, Malhotra A, Owens RL, El-Kareh R, Longhurst CA, Quintero K, Donahue A, Chan TC, Nemati S, et al. Impact of a deep learning sepsis prediction model on quality of care and survival. NPJ Digit Med. 2024;7(1):14. 38263386 PMC10805720
  36. [41]Yang J, Hao S, Huang J, Chen T, Liu R, Zhang P, Feng M, He Y, Xiao W, Hong Y, et al. The application of artificial intelligence in the management of sepsis. Med Rev (2021). 2023;3(5):369–80. 38283255 PMC10811352
  37. [42]Seymour CW, Kennedy JN, Wang S, Chang CH, Elliott CF, Xu Z, Berry S, Clermont G, Cooper G, Gomez H, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321(20):2003–17. 31104070 PMC6537818
  38. [43]Bhavani SV, Semler M, Qian ET, Verhoef PA, Robichaux C, Churpek MM, Coopersmith CM. Development and validation of novel sepsis subphenotypes using trajectories of vital signs. Intens Care Med. 2022;48(11):1582–92. PMC9510534 36152041
  39. [45]Komorowski M, Green A, Tatham KC, Seymour C, Antcliffe D. Sepsis biomarkers and diagnostic tools with a focus on machine learning. Ebiomedicine 2022, 86. 10.1016/j.ebiom.2022.104394 PMC9783125 36470834 · DOI
  40. [46]Festor P, Jia Y, Gordon AC, Faisal AA, Habli I, Komorowski M. Assuring the safety of AI-based clinical decision support systems: a case study of the AI clinician for sepsis treatment. BMJ Health Care Inf 2022, 29(1). 10.1136/bmjhci-2022-100549 PMC9289024 35851286 · DOI
  41. [47]Komorowski M, Celi L, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716–. 30349085
  42. [48]Kalimouttou A, Kennedy JN, Feng J, Singh H, Saria S, Angus DC, Seymour CW, Pirracchio R. Optimal vasopressin initiation in septic shock: the OVISS reinforcement learning study. JAMA. 2025;333(19):1688–98. 40098600 PMC11920879
  43. [49]Gupta A, Liu T, Shepherd S. Clinical decision support system to assess the risk of sepsis using tree augmented bayesian networks and electronic medical record data. Health Inf J. 2020;26(2):841–61. 31195874
  44. [50]Jeon E, Choi JH, Suk HI. ADT(2)R: adaptive decision transformer for dynamic treatment regimes in sepsis. IEEE Trans Neural Netw Learn Syst 2024, PP. 10.1109/TNNLS.2024.3442243 39208049 · DOI
  45. [51]Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10. 26903338 PMC4968574
  46. [52]Rhee C, Zhang ZL, Wang R, Kadri S, Fram D, Schaaf R, Klompas M. Sepsis surveillance using sofa versus Esofa criteria based on routine Ehr data. Crit Care Med. 2018;46(1):689–689.
  47. [55]Schinkel M, Nanayakkara PWB, Wiersinga WJ. Sepsis performance improvement programs: from evidence toward clinical implementation. Crit Care. 2022;26(1):77. 35337358 PMC8951662
  48. [56]Moor M, Bennett N, Plecko D, Horn M, Rieck B, Meinshausen N, Bühlmann P, Borgwardt K. Predicting sepsis using deep learning across international sites: a and validation. Eclinicalmedicine 2023, 62. 10.1016/j.eclinm.2023.102124 PMC10425671 37588623 · DOI · Paper page
  49. [57]Boussina A, Shashikumar SP, Malhotra A, Owens RL, El-Kareh R, Longhurst CA, Quintero K, Donahue A, Chan TC, Nemati S et al. Impact of a deep learning sepsis prediction model on quality of care and survival. Npj Digit Med 2024, 7(1). 10.1038/s41746-023-00986-6 PMC10805720 38263386 · DOI · Paper page
  50. [58]Adams R, Henry KE, Sridharan A, Soleimani H, Zhan AD, Rawat N, Johnson L, Hager DN, Cosgrove SE, Markowski A, et al. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nat Med. 2022;28(7):1455–. 35864252
  51. [59]Kheterpal S, Singh K, Topol EJ. Digital medicine digitising the prediction and management of sepsis. Lancet. 2022;399(10334):1459–1459. 35430013
  52. [60]Wong A, Otles E, Donnelly JP. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients (181, Pg 1065, 2021). Jama Intern Med. 2021;181(8):1144–1144. PMC8218233 34152373
  53. [62]Doutreligne M, Struja T, Abecassis J, Morgand C, Celi LA, Varoquaux G. Step-by-step causal analysis of EHRs to ground decision-making. PLOS Digit Health. 2025;4(2):e0000721. 39899627 PMC11790099
  54. [63]Van Vogt E, Gordon AC, Diaz-Ordaz K, Cro S. Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study. BMC Med Res Methodol. 2025;25(1):50. 39987431 PMC11846376
  55. [65]Bhargava A, López-Espina C, Schmalz L, Khan S, Watson GL, Urdiales D, Updike L, Kurtzman N, Dagan A, Doodlesack A et al. FDA-Authorized AI/ML tool for sepsis prediction: development and validation. NEJM AI 2024:10.1056/AIoa2400867 · DOI
  56. [66]Muralidharan V, Adewale BA, Huang CJ, Nta MT, Ademiju PO, Pathmarajah P, Hang MK, Adesanya O, Abdullateef RO, Babatunde AO, et al. A scoping review of reporting gaps in FDA-approved AI medical devices. NPJ Digit Med. 2024;7(1):273. 39362934 PMC11450195
  57. [67]Persson I, Macura A, Becedas D, Sjovall F. Early prediction of sepsis in intensive care patients using the machine learning algorithm NAVOY(R) sepsis, a prospective randomized clinical validation study. J Crit Care. 2024;80:154400. 38245375
  58. [68]Shashikumar SP, Mohammadi S, Krishnamoorthy R, Patel A, Wardi G, Ahn JC, Singh K, Aronoff-Spencer E, Nemati S. Development and prospective implementation of a large Language model based system for early sepsis prediction. NPJ Digit Med. 2025;8(1):290. 40379845 PMC12084535
  59. [69]Valan B, Prakash A, Ratliff W, Gao M, Muthya S, Thomas A, Eaton JL, Gardner M, Nichols M, Revoir M, et al. Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model. NPJ Digit Med. 2025;8(1):350. 40500319 PMC12159134
  60. [71]Yesil MR, Talli I, Pelloso M, Cosma C, Pangrazzi E, Plebani M, Ustundag Y, Padoan A. Impact of analytical bias on machine learning models for sepsis prediction using laboratory data. Clin Chem Lab Med 2025. 10.1515/cclm-2025-0491 40440484 · DOI
  61. [74]Goncalves LS, Amaro MLM, Romero ALM, Schamne FK, Fressatto JL, Bezerra CW. Implementation of an artificial intelligence algorithm for sepsis detection. Rev Bras Enferm. 2020;73(3):e20180421. 32294705
  62. [75]Hassan N, Slight R, Weiand D, Vellinga A, Morgan G, Aboushareb F, Slight SP. Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review. Int J Med Inf. 2021;150:104457. 33878596
  63. [76]Aslan AT, Permana B, Harris PNA, Naidoo KD, Pienaar MA, Irwin AD. The opportunities and challenges for artificial intelligence to improve sepsis outcomes in the paediatric intensive care unit. Curr Infect Dis Rep. 2023;25(11):243–53.
  64. [77]Dusing C, Cimiano P, Rehberg S, Scherer C, Kaup O, Koster C, Hellmich S, Herrmann D, Meier KL, Classen S, et al. Integrating federated learning for improved counterfactual explanations in clinical decision support systems for sepsis therapy. Artif Intell Med. 2024;157:102982. 39277983
  65. [78]Sullivan BA, Beam K, Vesoulis ZA, Aziz KB, Husain AN, Knake LA, Moreira AG, Hooven TA, Weiss EM, Carr NR, et al. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. J Perinatol. 2024;44(1):1–11. 38097685 PMC10872325
  66. [79]Banja JD, Xie Y, Smith JR, Rana S, Holder AL: Mitigating bias in machine learning models with Ethics-Based initiatives: the case of sepsis. Am J Bioeth 2025:1–14. 10.1080/15265161.2025.2497971 PMC12353398 40354171 · DOI
  67. [80]Struja T, Matos J, Lam B, Cao Y, Liu X, Chan Z, Jia Y, Sauer CM, D’Couto H, Dankwa-Mullan I, et al. Evaluating equitable care in the icu:creating a causal inference template to assess the impact of life-sustaining interventions across Racial and ethnic groups. Heart Lung. 2025;72:48–56. 40163946
  68. [81]Lam JY, Boussina A, Shashikumar SP, Owens RL, Nemati S, Josef CS. The impact of laboratory data missingness on sepsis diagnosis timeliness. Jamia Open 2024, 7(3). 10.1093/jamiaopen/ooae085 PMC11418648 39314673 · DOI
  69. [82]Iqbal F, Chandra P, Khan AA, Lewis LES, Acharya D, Vandana KE, Jayashree P, Shenoy PA. Prediction of mortality among neonates with sepsis in the neonatal intensive care unit: A machine learning approach. Clin Epidemiol Glob 2023, 24. 10.1016/j.cegh.2023.101414 · DOI
  70. [83]Bomrah S, Uddin M, Upadhyay U, Komorowski M, Priya J, Dhar E, Hsu SC, Syed-Abdul S. A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability. Crit Care 2024, 28(1). 10.1186/s13054-024-04948-6 PMC11131234 38802973 · DOI
  71. [84]Eisenberg MA, Balamuth F. Pediatric sepsis screening in US hospitals. Pediatr Res. 2022;91(2):351–8. 34417563 PMC8378117
  72. [85]Lamproudis A, Henriksson A, Valik JK, Naucler P. Improving the timeliness of early prediction models for sepsis through utility optimization. Proc Int C Tools Art 2022:1062–9. 10.1109/ICTAI56018.2022.00162 · DOI
  73. [87]van den Berg M, van Beuningen FE, Ter Maaten JC, Bouma HR. Hospital-related costs of sepsis around the world: A systematic review exploring the economic burden of sepsis. J Crit Care. 2022;71:154096. 35839604
  74. [88]Ramesh K, Boussina A, Shashikumar SP, Malhotra A, Longhurst CA, Josef CS, Quintero K, Del Rosso J, Nemati S, Wardi G. Quantifying healthcare provider perceptions of a novel deep learning algorithm to predict sepsis: electronic survey. Crit Care Explor. 2025;7(6):e1276. 40466050 PMC12140755
  75. [90]Rogers P, Boussina AE, Shashikumar SP, Wardi G, Longhurst CA, Nemati S. Optimizing the implementation of clinical predictive models to minimize National costs: sepsis case study. J Med Internet Res. 2023;25:e43486. 36780203 PMC9972209
  76. [91]Zwerwer LR, van der Pol S, Zacharowski K, Postma MJ, Kloka J, Friedrichson B, van Asselt ADI. The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: an early health technology assessment. J Crit Care. 2024;82:154802. 38583302
  77. [92]Saeed U, Insaf RA, Piracha ZZ, Tariq MN, Sohail A, Abbasi UA, Fida Rana MS, Gilani SS, Noor S, Noor E, et al. Crisis averted: a world united against the menace of multiple drug-resistant superbugs -pioneering anti-AMR vaccines, RNA interference, nanomedicine, CRISPR-based antimicrobials, bacteriophage therapies, and clinical artificial intelligence strategies to safeguard global antimicrobial arsenal. Front Microbiol. 2023;14:1270018. 38098671 PMC10720626
  78. [93]Denecke K, Baudoin CR. A review of artificial intelligence and robotics in transformed health ecosystems. Front Med-Lausanne 2022, 9. 10.3389/fmed.2022.795957 PMC9299071 35872767 · DOI
  79. [94]Choi YB, Capitan KE, Krause JS, Streeper MM. Challenges associated with privacy in health care industry: implementation of HIPAA and the security rules. J Med Syst. 2006;30(1):57–64. 16548416
  80. [95]Peregrin T. Managing HIPAA compliance includes legal and ethical considerations. J Acad Nutr Diet. 2021;121(2):327–9. 33487218
  81. [96]Pan W, Xu Z, Rajendran S, Wang F. An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals. Patterns (N Y). 2024;5(1):100898. 38264713 PMC10801228
  82. [97]Mandl KD, Perakslis ED. HIPAA and the leak of deidentified EHR data. N Engl J Med. 2021;384(23):2171–3. 34110112
  83. [98]Petersson L, Vincent K, Svedberg P, Nygren JM, Larsson I. Ethical considerations in implementing AI for mortality prediction in the emergency department: linking theory and practice. Digit Health 2023, 9. 10.1177/20552076231206588 PMC10566278 37829612 · DOI
  84. [99]Santacroce E, D’Angerio M, Ciobanu AL, Masini L, Lo Tartaro D, Coloretti I, Busani S, Rubio I, Meschiari M, Franceschini E et al.: advances and challenges in sepsis management: modern tools and future directions. Cells 2024, 13(5). 10.3390/cells13050439 PMC10931424 38474403 · DOI
  85. [100]Boussina A, Wardi G, Shashikumar SP, Malhotra A, Zheng K, Nemati S. Representation learning and spectral clustering for the development and external validation of dynamic sepsis phenotypes: observational cohort study. J Med Internet Res 2023, 25. 10.2196/45614 PMC10337434 37351927 · DOI
  86. [102]Yadgarov MY, Landoni G, Berikashvili LB, Polyakov PA, Kadantseva KK, Smirnova AV, Kuznetsov IV, Shemetova MM, Yakovlev AA, Likhvantsev VV. Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis. Front Med-Lausanne 2024, 11. 10.3389/fmed.2024.1491358 PMC11523135 39478824 · DOI
  87. [103]Chen E, Kansal A, Chen J, Jin BT, Reisler JR, Kim DA, Rajpurkar P. Multimodal clinical benchmark for emergency care (MC-BEC): A comprehensive benchmark for evaluating foundation models in emergency medicine. Adv Neur In 2023. 10.48550/arXiv.2311.04937 · DOI
  88. [104]Yan MY, Gustad LT, Nytro O. Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review. J Am Med Inf Assn. 2022;29(3):559–75. PMC8800516 34897469
  89. [105]Li Q, Ma HB, Song D, Bai YP, Zhao LN, Xie KL. Early prediction of sepsis using chatGPT-generated summaries and structured data. Multimed Tools Appl 2024. 10.1007/s11042-024-18378-7 · DOI
  90. [108]Komorowski M, Green A, Tatham KC, Seymour C, Antcliffe D. Sepsis biomarkers and diagnostic tools with a focus on machine learning. EBioMedicine. 2022;86:104394. 36470834 PMC9783125
  91. [109]Huang PF, Liu YQ, Li Y, Xin Y, Nan CC, Luo YH, Feng YT, Jin NN, Peng YH, Wang DW et al. Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury. Bmc Med 2025, 23(1). 10.1186/s12916-025-03920-7 PMC11818193 39934788 · DOI
  92. [110]Mi YX, Burnham KL, Charles PD, Heilig R, Vendrell I, Whalley J, Torrance HD, Antcliffe DB, May SM, Neville MJ et al. High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response. Sci Transl Med 2024, 16(750). 10.1126/scitranslmed.adh0185 38838133 · DOI
  93. [111]Shankar-Hari M, Calandra T, Soares MP, Bauer M, Wiersinga WJ, Prescott HC, Knight JC, Baillie KJ, Bos LDJ, Derde LPG, et al. Reframing sepsis immunobiology for translation: towards informative subtyping andtargeted Immunomodulatory therapies. Lancet Resp Med. 2024;12(4):323–36. PMC11025021 38408467
  94. [112]Boussina A, Wardi G, Shashikumar SP, Malhotra A, Zheng K, Nemati S. Representation learning and spectral clustering for the development and external validation of dynamic sepsis phenotypes: observational cohort study. J Med Internet Res. 2023;25:e45614. 37351927 PMC10337434
  95. [114]Sheini A. A point-of-care testing sensor based on fluorescent nanoclusters for rapid detection of septicemia in children. Sens Actuat B-Chem 2021, 328. 10.1016/j.snb.2020.129029 · DOI
  96. [115]Goyal M, Mascarenhas D, Prashanth RR, Haribalakrishna A. Diagnostic accuracy of Point-of-Care testing of C-Reactive protein, Interleukin-6, and procalcitonin in neonates with clinically suspected sepsis: A prospective observational study. Med Prin Pract. 2024;33(3):291–8. PMC11175603 38320541
  97. [116]Spaeth B, Taylor S, Shephard M, Reed RL, Omond R, Karnon J, Bonevski B, Rissel C, Ullah S, Noutsos T, et al. Point-of-care testing for sepsis in remote Australia and for first nations peoples. Nat Med. 2024;30(8):2105–6. 38816610
  98. [117]Soliman MM, Marshall C, Kimball JP, Choudhary T, Clermont G, Pinsky MR, Buchman TG, Coopersmith CM, Inan OT, Kamaleswaran R. Parsimonious Waveform-derived features consisting of pulse arrival time and heart rate variability predicts the onset of septic shock. Biomed Signal Process Control 2024, 92. 10.1016/j.bspc.2024.105974 PMC10977921 38559667 · DOI
  99. [118]Apalak M, Kiasaleh K. Advancing early detection of sepsis with Temporal convolutional networks using ECG signals. Ieee Access. 2024;12:3417–27.
  100. [119]Alge OP, Pickard J, Zhang W, Cheng S, Derksen H, Omenn GS, Gryak J, VanEpps JS, Najarian K. Continuous sepsis trajectory prediction using tensor-reduced physiological signals. Sci Rep. 2024;14(1):18155. 39103488 PMC11300462
  101. [120]Strodthoff N, Alcaraz JML, Haverkamp W. Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care. Eur Heart J-Digit Hl. 2024;5(4):454–60. PMC11284007 39081937
  102. [121]Mataraso SJ, Espinosa CA, Seong D, Reincke SM, Berson E, Reiss JD, Kim Y, Ghanem M, Shu CH, James T, et al. A machine learning approach to leveraging electronic health records for enhanced omics analysis. Nat Mach Intell. 2025;7(2):293–306. 40008295 PMC11847705
  103. [123]Sundrani S, Chen J, Jin BT, Abad ZSH, Rajpurkar P, Kim D. Predicting patient decompensation from continuous physiologic monitoring in the emergency department. NPJ Digit Med. 2023;6(1):60. 37016152 PMC10073111
  104. [125]Sundrani S, Chen JL, Jin BT, Abad ZSH, Rajpurkar P, Kim D. Predicting patient decompensation from continuous physiologic monitoring in the emergency department. Npj Digit Med 2023, 6(1). 10.1038/s41746-023-00803-0 PMC10073111 37016152 · DOI
  105. [126]Mataraso SJ, Espinosa CA, Seong D, Reincke SM, Berson E, Reiss JD, Kim Y, Ghanem M, Shu CH, James T et al. A machine learning approach to leveraging electronic health records for enhanced omics analysis. Nat Mach Intell 2025, 7(2). 10.1038/s42256-024-00974-9 PMC11847705 40008295 · DOI
  106. [127]Zhao TY, Liu JX, Zeng X, Wang W, Li S, Zang TY, Peng JJ, Yang Y. Prediction and collection of protein-metabolite interactions. Brief Bioinform 2021, 22(5). 10.1093/bib/bbab014 33554247 · DOI
  107. [129]AlGain S, Marra AR, Kobayashi T, Marra PS, Celeghini PD, Hsieh MK, Shatari MA, Althagafi S, Alayed M, Ranavaya JI, et al. Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review. Antimicrob Steward Healthc Epidemiol. 2025;5(1):e90. 40226293 PMC11986881
  108. [130]Gupta CB, Basu D, Williams TK, Neff LP, Johnson MA, Patel NT, Ganapathy AS, Lane MR, Radaei F, Chuah CN, et al. Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data. Sci Rep. 2024;14(1):2227. 38278825 PMC10817926

Cited by

Sorted by citations

Authors
Papareddy, P., Lobo, T. J., Holub, M., Bouma, H., Maca, J., Strodthoff, N., & Herwald, H.
Affiliations
  • Lund University (SE)
  • University Medical Center Groningen (NL)
  • Charles University (CZ)
  • Carl von Ossietzky Universität Oldenburg (DE)
Corresponding author
Herwald, Heiko
ORCID
Venue
Critical Care
Publisher
Springer Science and Business Media LLC
Published
2025-08-19
Volume
29
Issue
1
Pages
366-366
ISSN
1364-8535 (ISSN-L)
DOI
10.1186/s13054-025-05588-0
PMC
PMC12366378
OpenAlex
W4413311951
Cited by
49
References
22
Citations by year
2026: 41 · 2025: 8
FWCI
7.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Critical Care (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Carl von Ossiezky University of Oldenburg (acceptedVersion)
  • Lund University Publications (Lund University) (submittedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
Lund University
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
sepsis, artificial intelligence, early detection, early warning systems, risk stratification, personalized treatment, clinical decision support, subphenotyping, multimodal data integration, algorithmic bias, external validation, data quality, AI ethics, multi-omics, precision medicine
MeSH terms
Artificial Intelligence, Humans, Sepsis, Early Diagnosis, Precision Medicine

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 ›

Are you an author, or do you see a problem with this page? Request a review