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

Reviews in Medical Informatics (93) Secondary Use of Clinical Data for Research and Surveillance (375) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (591) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Innovations and Technology in Cancer Care (431) Machine Learning (1501) Electronic Health Records (1068)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33182, first published .
Machine Learning–Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal

Machine Learning–Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal

Machine Learning–Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal

Authors of this article:

Sheng-Chieh Lu1 Author Orcid Image ;   Cai Xu1 Author Orcid Image ;   Chandler H Nguyen2 Author Orcid Image ;   Yimin Geng3 Author Orcid Image ;   André Pfob4 Author Orcid Image ;   Chris Sidey-Gibbons1 Author Orcid Image
Article Authors Cited by (22) Tweetations (25) Metrics

Journals

  1. Xu C, Subbiah I, Lu S, Pfob A, Sidey-Gibbons C. Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data. Quality of Life Research 2023;32(3):713 View
  2. Pfob A, Lu S, Sidey-Gibbons C. Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Medical Research Methodology 2022;22(1) View
  3. Sidey-Gibbons C, Sun C, Schneider A, Lu S, Lu K, Wright A, Meyer L. Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data. Scientific Reports 2022;12(1) View
  4. Danilatou V, Nikolakakis S, Antonakaki D, Tzagkarakis C, Mavroidis D, Kostoulas T, Ioannidis S. Outcome Prediction in Critically-Ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms: External Validation and Comparison with Scoring Systems. International Journal of Molecular Sciences 2022;23(13):7132 View
  5. Taber P, Armin J, Orozco G, Del Fiol G, Erdrich J, Kawamoto K, Israni S. Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies. Current Oncology Reports 2023;25(5):387 View
  6. Lu S, Swisher C, Chung C, Jaffray D, Sidey-Gibbons C. On the importance of interpretable machine learning predictions to inform clinical decision making in oncology. Frontiers in Oncology 2023;13 View
  7. Lu S, Knafl M, Turin A, Offodile A, Ravi V, Sidey-Gibbons C. Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients. JCO Clinical Cancer Informatics 2023;(7) View
  8. Wehkamp K, Krawczak M, Schreiber S. The quality and utility of artificial intelligence in patient care. Deutsches Ärzteblatt international 2023 View
  9. SenthilKumar G, Madhusudhana S, Flitcroft M, Sheriff S, Thalji S, Merrill J, Clarke C, Maduekwe U, Tsai S, Christians K, Gamblin T, Kothari A. Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer. Scientific Reports 2023;13(1) View
  10. Han S, Sohn T, Ng B, Park C. Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach. Scientific Reports 2023;13(1) View
  11. Xu C, Pfob A, Mehrara B, Yin P, Nelson J, Pusic A, Sidey-Gibbons C. Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004). Annals of Surgical Oncology 2023;30(12):7046 View
  12. Franklin I, Bhuvaneswari R, Vasanthi V, Jerald M. Replica controlled sensor enabled architecture for management of electronic health records. International Journal of Information Technology 2023;15(7):3643 View
  13. Choi J, Yang M, Kim J, Shin Y, Shin Y, Park S. Prognostic prediction of sepsis patient using transformer with skip connected token for tabular data. Artificial Intelligence in Medicine 2024;149:102804 View
  14. Metzcar J, Jutzeler C, Macklin P, Köhn-Luque A, Brüningk S. A review of mechanistic learning in mathematical oncology. Frontiers in Immunology 2024;15 View
  15. Zhuang Q, Zhang A, Cong R, Yang G, Neo P, Tan D, Chua M, Tan I, Wong F, Eng Hock Ong M, Shao Wei Lam S, Liu N. Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction. BMC Palliative Care 2024;23(1) View
  16. Park S, Park Y, Lee E, Chae H, Park P, Choi D, Choi Y, Hwang J, Ahn S, Kim K, Kim W, Kong S, Jung S, Kim H. Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning. Cancers 2024;16(22):3799 View
  17. Rhanoui M, Mikram M, Amazian K, Ait-Abderrahim A, Yousfi S, Toughrai I. Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients. Journal of Imaging 2024;10(12):297 View

Books/Policy Documents

  1. Pradhan B, Biswas D, Neelapu B, Sivaraman J, Pal K. Advances in Artificial Intelligence. View