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

Machine Learning (1465) Electronic Health Records (1053) Clinical Information and Decision Making (1407) Innovations and Technology in Cancer Care (423) Decision Support for Health Professionals (1185)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23147, first published .
Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study

Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study

Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study

Authors of this article:

Yong-Yeon Jo1 Author Orcid Image ;   JaiHong Han2 Author Orcid Image ;   Hyun Woo Park1 Author Orcid Image ;   Hyojung Jung1 Author Orcid Image ;   Jae Dong Lee1 Author Orcid Image ;   Jipmin Jung3 Author Orcid Image ;   Hyo Soung Cha3 Author Orcid Image ;   Dae Kyung Sohn4 Author Orcid Image ;   Yul Hwangbo1 Author Orcid Image
Article Authors Cited by (14) Tweetations (14) Metrics

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  8. Tran T, Hayden J, Gazendam A, Ghert M, Gundle K, Doung Y. Pediatric and Adult Patients Have Similar Functional Improvement After Endoprosthetic Reconstruction of Lower-Extremity Tumors. Journal of Bone and Joint Surgery 2023;105(Suppl 1):22 View
  9. Vollmer A, Nagler S, Hörner M, Hartmann S, Brands R, Breitenbücher N, Straub A, Kübler A, Vollmer M, Gubik S, Lang G, Wollborn J, Saravi B. Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery. Heliyon 2023;9(11):e20752 View
  10. Zhang G, Liu X, Hu Y, Luo Q, Ruan L, Xie H, Zeng Y. Development and comparison of machine-learning models for predicting prolonged postoperative length of stay in lung cancer patients following video-assisted thoracoscopic surgery. Asia-Pacific Journal of Oncology Nursing 2024;11(6):100493 View
  11. Brydges G, Uppal A, Gottumukkala V. Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians. Current Oncology 2024;31(5):2727 View
  12. Han L, Char D, Aghaeepour N, Grosvenor E, Hong H, Steffner K, Peterson A, Quinonez Z, Saffary R, Schmiesing C, Gostic W, Sun L, Vanneman M, Kadry B, Mudumbai S, Gross E, Wang E, Xie J. Artificial Intelligence in Perioperative Care: Opportunities and Challenges. Anesthesiology 2024;141(2):379 View
  13. Celotto F, Capelli G, Ferrari S, Scarpa M, Pucciarelli S, Spolverato G. Application and use of artificial intelligence in colorectal cancer surgery: where are we?. Artificial Intelligence Surgery 2024;4(4):348 View
  14. Kim J, Chang H, Ryu S, Choi I, Kwon A, Ji H. Determinants of Length of Stay for Medical Inpatients Using Survival Analysis. International Journal of Environmental Research and Public Health 2024;21(11):1424 View