Obesity and overweight [internet]. [cited 2024 Mar 15]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight

Robertson AGN, Wiggins T, Robertson FP, et al. Perioperative mortality in bariatric surgery: meta-analysis. Br J Surg. 2021;108(8):892–7.

Article  CAS  PubMed  Google Scholar 

Kassir R, Debs T, Blanc P, Gugenheim J, et al. Complications of bariatric surgery: presentation and emergency management. Int J Surg Lond Engl. 2016;27:77–81.

Article  Google Scholar 

Liu R, Lai X, Wang J, et al. A non-linear ensemble model-based surgical risk calculator for mixed data from multiple surgical fields. BMC Med Inform Decis Mak. 2021;21(2):88.

Article  PubMed  PubMed Central  Google Scholar 

Scotton G, Del Zotto G, Bernardi L, et al. Is the ACS-NSQIP Risk Calculator accurate in predicting adverse postoperative outcomes in the emergency setting? An Italian single-center preliminary study. World J Surg. 2020;44(11):3710–9.

Article  PubMed  PubMed Central  Google Scholar 

Barnett S, Moonesinghe SR. Clinical risk scores to guide perioperative management. Postgrad Med J. 2011;87(1030):535–41.

Article  PubMed  Google Scholar 

Basta MN, Bauder AR, Kovach SJ, et al. Assessing the predictive accuracy of the American College of Surgeons National Surgical Quality Improvement Project Surgical Risk Calculator in open ventral hernia repair. Am J Surg. 2016;212(2):272–81.

Article  PubMed  Google Scholar 

Bilimoria KY, Liu Y, Paruch JL, et al Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013 Nov;217(5):833–842.e1–3.

Grieco A, Huffman KM, Cohen ME, et al. The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program bariatric surgical risk/benefit calculator: 30-day risk. Surg Obes Relat Dis. 2021;17(6):1117–24.

Article  PubMed  Google Scholar 

Nudel J, Bishara AM, de Geus SWL, et al. Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surg Endosc. 2021;35(1):182–91.

Article  PubMed  Google Scholar 

Hashimoto DA, Rosman G, Rus D, et al. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.

Article  PubMed  Google Scholar 

Bellini V, Valente M, Turetti M, et al. Current applications of artificial intelligence in bariatric surgery. Obes Surg. 2022;32(8):2717–33.

Article  PubMed  PubMed Central  Google Scholar 

Kim JS, Merrill RK, Arvind V, et al. Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine. 2018;43(12):853–60.

Article  PubMed  PubMed Central  Google Scholar 

Bertsimas D, Dunn J, Velmahos GC, et al. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based predictive optimal trees in emergency surgery risk (POTTER) calculator. Ann Surg. 2018;268(4):574–83.

Article  PubMed  Google Scholar 

Rahbari NN, Weitz J, Hohenberger W, et al. Definition and grading of anastomotic leakage following anterior resection of the rectum: a proposal by the International Study Group of Rectal Cancer. Surgery. 2010;147(3):339–51.

Article  PubMed  Google Scholar 

Golzarand M, Toolabi K, Parsaei R. Prediction factors of early postoperative bleeding after bariatric surgery. Obes Surg. 2022;32(7):1–8.

Article  PubMed  Google Scholar 

Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care–associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36(5):309–32.

Article  PubMed  Google Scholar 

Clavien PA, Barkun J, de Oliveira ML, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg. 2009;250(2):187.

Article  PubMed  Google Scholar 

Palmisano S, Giuricin M, Casagranda B, et al. Zero frequency of internal hernias after laparoscopic double loop gastric bypass without closure of mesenteric defects. Surg Today. 2014;44(10):1920–4.

Article  PubMed  Google Scholar 

Chu CA, Gagner M, Quinn T, et al. Two-stage laparoscopic biliopancreatic diversion with duodenal switch: an alternative approach to super-super morbid obesity. Surg Endosc. 2002;16(Abs):S069.

Cao Y, Fang X, Ottosson J, et al. A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery. J Clin Med. 2019;8(5):668.

Article  PubMed  PubMed Central  Google Scholar 

Pantelis AG, Stravodimos GK, Lapatsanis DP. A scoping review of artificial intelligence and machine learning in bariatric and metabolic surgery: current status and future perspectives. Obes Surg. 2021;31(10):4555–63.

Article  PubMed  Google Scholar 

Butler LR, Chen KA, Hsu J, et al. Predicting readmission after bariatric surgery using machine learning. Surg Obes Relat Dis Off J Am Soc Bariatr Surg. 2023;19(11):1236–44.

Article  Google Scholar 

Torquati M, Mendis M, Xu H, et al. Using the super learner algorithm to predict risk of 30-day readmission after bariatric surgery in the United States. Surgery. 2022;171(3):621–7.

Article  PubMed  Google Scholar 

Peng X, Zhu T, Wang T, et al. Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study. BMC Anesthesiol. 2022;22(1):284.

Article  PubMed  PubMed Central  Google Scholar 

Hsu JL, Chen KA, Butler LR, et al. Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery. Surg Endosc. 2023;37(9):7121–7.

Article  PubMed  Google Scholar 

Hinton G. Deep learning-a technology with the potential to transform health care. JAMA. 2018;320(11):1101–2.

Article  PubMed  Google Scholar 

Cao Y, Montgomery S, Ottosson J, et al. Deep learning neural networks to predict serious complications after bariatric surgery: analysis of Scandinavian Obesity Surgery Registry Data. JMIR Med Inform. 2020;8(5):e15992.

Article  PubMed  PubMed Central  Google Scholar