This paper is in the following e-collection/theme issue:

Machine Learning (1501) Clinical Information and Decision Making (1434) Perioperative Risk Assessment (14)

Published on in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15992, first published .
Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data

Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data

Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data

Authors of this article:

Yang Cao1 Author Orcid Image ;   Scott Montgomery1, 2, 3 Author Orcid Image ;   Johan Ottosson4 Author Orcid Image ;   Erik Näslund5 Author Orcid Image ;   Erik Stenberg4 Author Orcid Image
Article Authors Cited by (22) Tweetations (2) Metrics

Journals

  1. Cao Y, Raoof M, Szabo E, Ottosson J, Näslund I. Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry. Journal of Clinical Medicine 2020;9(6):1895 View
  2. Jeong Y, Kim J, Kim D, Woo J, Kim M, Choi H, Kang A, Park S. Prediction of Postoperative Complications for Patients of End Stage Renal Disease. Sensors 2021;21(2):544 View
  3. Bianchi A, Pagan-Pomar A, Jimenez-Segovia M, Gonzalez-Argenté F. Long-term comparative study on open versus laparoscopic biliopancreatic diversion. Surgical Endoscopy 2022;36(2):1164 View
  4. Cao Y, Näslund I, Näslund E, Ottosson J, Montgomery S, Stenberg E. Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register. JMIR Medical Informatics 2021;9(8):e25612 View
  5. Pantelis A, Stravodimos G, Lapatsanis D. A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives. Obesity Surgery 2021;31(10):4555 View
  6. Chen P, Lee C, Liang C, Pao S, Huang K, Lin K. General deep learning model for detecting diabetic retinopathy. BMC Bioinformatics 2021;22(S5) View
  7. Stam W, Goedknegt L, Ingwersen E, Schoonmade L, Bruns E, Daams F. The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review. Surgery 2022;171(4):1014 View
  8. Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, Bignami E. Current Applications of Artificial Intelligence in Bariatric Surgery. Obesity Surgery 2022;32(8):2717 View
  9. Stam W, Ingwersen E, Ali M, Spijkerman J, Kazemier G, Bruns E, Daams F. Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery. Surgery Today 2023;53(10):1209 View
  10. Bektaş M, Reiber B, Pereira J, Burchell G, van der Peet D. Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives. Obesity Surgery 2022;32(8):2772 View
  11. Enodien B, Taha-Mehlitz S, Saad B, Nasser M, Frey D, Taha A. The development of machine learning in bariatric surgery. Frontiers in Surgery 2023;10 View
  12. Hsu J, Chen K, Butler L, Bahraini A, Kapadia M, Gomez S, Farrell T. Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery. Surgical Endoscopy 2023;37(9):7121 View
  13. Butler L, Chen K, Hsu J, Kapadia M, Gomez S, Farrell T. Predicting readmission after bariatric surgery using machine learning. Surgery for Obesity and Related Diseases 2023;19(11):1236 View
  14. Sundbom M, Näslund I, Ottosson J, Stenberg E, Näslund E. Results from the Scandinavian Obesity Surgery Registry: A narrative review. Obesity Reviews 2024;25(2) View
  15. Scott A, Amateau S, Leslie D, Ikramuddin S, Wise E. Prediction of 30-Day Morbidity and Mortality After Conversion of Sleeve Gastrectomy to Roux-en-Y Gastric Bypass: Use of an Artificial Neural Network. The American Surgeon™ 2024;90(6):1202 View
  16. Hashemi S, Yousefzadeh Z, Abin A, Ejmalian A, Nabavi S, Dabbagh A. Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications. Journal of Cellular & Molecular Anesthesia 2024;9(1) View
  17. Ali H, Inayat F, Moond V, Chaudhry A, Afzal A, Anjum Z, Tahir H, Anwar M, Dahiya D, Afzal M, Nawaz G, Sohail A, Aziz M. Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning. World Journal of Gastrointestinal Surgery 2024;16(4):1097 View
  18. Zucchini N, Capozzella E, Giuffrè M, Mastronardi M, Casagranda B, Crocè S, de Manzini N, Palmisano S. Advanced Non-linear Modeling and Explainable Artificial Intelligence Techniques for Predicting 30-Day Complications in Bariatric Surgery: A Single-Center Study. Obesity Surgery 2024;34(10):3627 View
  19. Nopour R. Comparison of machine learning models to predict complications of bariatric surgery: A systematic review. Health Informatics Journal 2024;30(3) View
  20. Kim S, Park S, Seo H, Woo J. Feature selection integrating Shapley values and mutual information in reinforcement learning: An application in the prediction of post-operative outcomes in patients with end-stage renal disease. Computer Methods and Programs in Biomedicine 2024;257:108416 View

Books/Policy Documents

  1. G. Pantelis A. Bariatric Surgery - Past and Present. View
  2. Gbobaniyi O, Tincani D, Emelone P. Impact of AI and Robotics on the Medical Tourism Industry. View