Havens JM, Peetz AB, Do WS, Cooper Z, Kelly E, Askari R, et al. The excess morbidity and mortality of emergency general surgery. Journal of Trauma and Acute Care Surgery. 2015;78:306–11. https://doi.org/10.1097/TA.0000000000000517.
Mullen MG, Michaels AD, Mehaffey JH, Guidry CA, Turrentine FE, Hedrick TL, et al. Risk associated with complications and mortality after urgent surgery vs elective and emergency surgery: implications for defining “quality” and reporting outcomes for urgent surgery. JAMA Surg. 2017;152:768–74. https://doi.org/10.1001/jamasurg.2017.0918.
Article PubMed PubMed Central Google Scholar
Villarreal JA, Satyadi W, Tennakoon L, Knowlton LM, Knight A, Forrester JD. Futility thresholds for emergency general surgery in the post-cardiac intensive care unit. J Surg Res. 2025;313:78–83. https://doi.org/10.1016/j.jss.2025.06.007.
Badgwell BD, Cormier JN, Wray CJ, Borthakur G, Qiao W, Rolston KV, et al. Challenges in surgical management of abdominal pain in the neutropenic cancer patient. Ann Surg. 2008;248:104–9. https://doi.org/10.1097/SLA.0b013e3181724fe5.
Abhyankar S, Demner-Fushman D, Callaghan FM, McDonald CJ. Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis. Journal of the American Medical Informatics Association. 2014;21:801–7. https://doi.org/10.1136/amiajnl-2013-001915.
Article PubMed PubMed Central Google Scholar
Boss JM, Narula G, Straessle C, Willms J, Azzati J, Brodbeck D, et al. ICU Cockpit: a platform for collecting multimodal waveform data, AI-based computational disease modeling and real-time decision support in the intensive care unit. Journal of the American Medical Informatics Association. 2022;29:1286–91. https://doi.org/10.1093/jamia/ocac064.
Article PubMed PubMed Central Google Scholar
Mollura M, Lehman L-W, Mark RG, Barbieri R. A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis. Philos Transact A Math Phys Eng Sci. 2021;379:20200252. https://doi.org/10.1098/rsta.2020.0252.
Choukalas CG, Vu T-G. The problem of daily imaging in the intensive care unit: when you care so much it hurts. JAMA Intern Med. 2020;180:1369–70. https://doi.org/10.1001/jamainternmed.2020.2667.
Hyder JA, Reznor G, Wakeam E, Nguyen LL, Lipsitz SR, Havens JM. Risk prediction accuracy differs for emergency versus elective cases in the ACS-NSQIP. Ann Surg. 2016;264:959–65. https://doi.org/10.1097/SLA.0000000000001558.
Vernooij JEM, Koning NJ, Geurts JW, Holewijn S, Preckel B, Kalkman CJ, et al. Performance and usability of pre-operative prediction models for 30-day peri-operative mortality risk: a systematic review. Anaesthesia. 2023;78:607–19. https://doi.org/10.1111/anae.15988.
Article CAS PubMed Google Scholar
Gao J, Merchant AM. A machine learning approach in predicting mortality following emergency general surgery. Am Surg. 2021;87:1379–85. https://doi.org/10.1177/00031348211038568.
Yun K, Oh J, Hong TH, Kim EY. Prediction of mortality in surgical intensive care unit patients using machine learning algorithms. Front Med. 2021;8:621861. https://doi.org/10.3389/fmed.2021.621861.
Parreco J, Hidalgo A, Kozol R, Namias N, Rattan R. Predicting mortality in the surgical intensive care unit using artificial intelligence and natural language processing of physician documentation. Am Surg. 2018;84:1190–4.
Henry KE, Adams R, Parent C, Soleimani H, Sridharan A, Johnson L, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nat Med. 2022;28:1447–54. https://doi.org/10.1038/s41591-022-01895-z.
Article CAS PubMed Google Scholar
Balczewski EA, Lyons PG, Singh K. Alert timing in sepsis prediction models-an opportunity to tailor interventions. JAMA Netw Open. 2023;6:e2329704. https://doi.org/10.1001/jamanetworkopen.2023.29704.
Lee S-W, Kung H-C, Huang J-F, Hsu C-P, Wang C-C, Wu Y-T, et al. The clinical application of machine learning-based models for early prediction of hemorrhage in trauma intensive care units. J Pers Med. 2022;12:1901. https://doi.org/10.3390/jpm12111901.
Article PubMed PubMed Central Google Scholar
Vali M, Paydar S, Seif M, Sabetian G, Abujaber A, Ghaem H. Prediction prolonged mechanical ventilation in trauma patients of the intensive care unit according to initial medical factors: a machine learning approach. Sci Rep. 2023;13:5925. https://doi.org/10.1038/s41598-023-33159-2.
Article CAS PubMed PubMed Central Google Scholar
Aliaga M, Forel J-M, De Bourmont S, Jung B, Thomas G, Mahul M, et al. Diagnostic yield and safety of CT scans in ICU. Intensive Care Med. 2015;41:436–43. https://doi.org/10.1007/s00134-014-3592-1.
Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, et al. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol. 2023;30:251–65. https://doi.org/10.1007/s10140-023-02120-1.
Article PubMed PubMed Central Google Scholar
Dreizin D, Chen T, Liang Y, Zhou Y, Paes F, Wang Y, et al. Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis. Abdom Radiol. 2021;46:2556–66. https://doi.org/10.1007/s00261-020-02892-x.
Chen H, Unberath M, Dreizin D. Toward automated interpretable AAST grading for blunt splenic injury. Emerg Radiol. 2023;30:41–50. https://doi.org/10.1007/s10140-022-02099-1.
El Khoury D, Pardo E, Cambriel A, Bonnet F, Pham T, Cholley B, et al. Gastric cross-sectional area to predict gastric intolerance in critically ill patients: the Sono-ICU prospective observational bicenter study. Crit Care Explor. 2023;5:e0882. https://doi.org/10.1097/CCE.0000000000000882.
Article PubMed PubMed Central Google Scholar
Xie Y, Zhou C, Gao L, Wu J, Li X, Zhou H-Y et al. MedTrinity-25 M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine [Internet]. arXiv; 2025 [cited 2026 Mar 23]. https://doi.org/10.48550/arXiv.2408.02900
Choi J, Chen Y, Sivura A, Vendrow EB, Wang J, Spain DA. TraumaICD bidirectional encoder representation from Transformers: a Natural Language Processing algorithm to extract injury International Classification of Diseases, 10th Edition diagnosis code from free text. Ann Surg. 2024;280:150–5. https://doi.org/10.1097/SLA.0000000000006107.
Jung JJ, Jüni P, Lebovic G, Grantcharov T. First-year analysis of the operating room black box study. Ann Surg. 2020;271:122–7. https://doi.org/10.1097/SLA.0000000000002863.
Dai W, Adeli E, Luo Z, Dash D, Lakshmikanth S, Durante Z, et al. Developing ICU clinical behavioral atlas using ambient intelligence and computer vision. NEJM AI. 2025;2:AIoa2400590. https://doi.org/10.1056/AIoa2400590.
Yeung S, Rinaldo F, Jopling J, Liu B, Mehra R, Downing NL, et al. A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. npj Digit Med. 2019;2:11. https://doi.org/10.1038/s41746-019-0087-z.
Article PubMed PubMed Central Google Scholar
Chan PY, Tay A, Chen D, De Freitas M, Millet C, Nguyen-Duc T, et al. Ambient intelligence-based monitoring of staff and patient activity in the intensive care unit. Aust Crit Care. 2023;36:92–8. https://doi.org/10.1016/j.aucc.2022.08.011.
Filho I, Aquino G, Malaquias RS, Girão G, Melo SRM. An IoT-based healthcare platform for patients in ICU beds during the COVID-19 outbreak. IEEE Access. 2021;9:27262–77. https://doi.org/10.1109/ACCESS.2021.3058448.
Article PubMed PubMed Central Google Scholar
Vladu A, Ghitea TC, Daina LG, Țîrț DP, Daina MD. Enhancing operating room efficiency: the impact of computational algorithms on surgical scheduling and team dynamics. Healthcare. 2024;12:1906. https://doi.org/10.3390/healthcare12191906.
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