Baptista W

Language: Spanish
References: 9
Page: 493-495
PDF size: 730.83 Kb.

Text Extraction

No abstract.

REFERENCES

1. Ren Y, Loftus TJ, Datta S, et al. Performance of a machine learningalgorithm using electronic health record data to predict postoperativecomplications and report on a mobile platform. JAMA Netw Open.2022;5:e2211973.

2. Wijnberge M, Geerts BF, Hol L, et al. Effect of a machine learningderivedearly warning system for intraoperative hypotension vs standardcare on depth and duration of intraoperative hypotension during electivenoncardiac surgery: the HYPE randomized clinical trial. JAMA.2020;323:1052-1060.

3. Ripollés-Melchor J, Carrasco-Sánchez L, Tomé-Roca JL, et al.Hemodynamic management guided by the Hypotension PredictionIndex in abdominal surgery: a multicenter randomized clinical trial.Anesthesiology. 2025;142:639-654.

4. Ke YH, Jin L, Elangovan K, et al. Real-world deployment and evaluationof PEri-operative AI CHatbot (PEACH): a large language model chatbotfor peri-operative medicine. Anaesthesia. 2026;81:62-71.

5. Hasjim BJ, Azarfar G, Lee FG, et al. A multiagent large languagemodel-based system to simulate the liver transplant selectioncommittee: a retrospective cohort study. Lancet Digit Health.2026;8:100966.

6. Arina P, Kaczorek MR, Hofmaenner DA, et al. Prediction ofcomplications and prognostication in perioperative medicine: asystematic review and PROBAST assessment of machine learning tools.Anesthesiology. 2024;140:85-101.

7. Tripodi A, Mannucci PM. The coagulopathy of chronic liver disease. NEngl J Med. 2011;365:147-156.

8. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement:updated guidance for reporting clinical prediction models that useregression or machine learning methods. BMJ. 2024;385:e078378.

9. World Health Organization. Ethics and governance of artificialintelligence for health: guidance on large multi-modal models. Geneva:WHO; 2024.