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

Clinical Cancer Research (88) Artificial Intelligence (1421) Machine Learning (1501) Electronic Health Records (1068) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210)

Published on in Vol 7, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19812, first published .
Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model

Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model

Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model

Authors of this article:

Chia-Wei Liang1 Author Orcid Image ;   Hsuan-Chia Yang1 Author Orcid Image ;   Md Mohaimenul Islam1 Author Orcid Image ;   Phung Anh Alex Nguyen1 Author Orcid Image ;   Yi-Ting Feng1 Author Orcid Image ;   Ze Yu Hou1 Author Orcid Image ;   Chih-Wei Huang1 Author Orcid Image ;   Tahmina Nasrin Poly1 Author Orcid Image ;   Yu-Chuan Jack Li1 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

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  2. Zhang K, Hu B, Zhou F, Song Y, Zhao X, Huang X. Graph-based structural knowledge-aware network for diagnosis assistant. Mathematical Biosciences and Engineering 2022;19(10):10533 View
  3. Hsu J, Nguyen P, Phuc P, Lo T, Hsu M, Hsieh M, Le N, Cheng C, Chang T, Chen C. Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival. Cancers 2022;14(22):5562 View
  4. Wu Z, Xu D, Hu P, Huang T. A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients. Journal of the American Medical Informatics Association 2023;30(5):846 View
  5. Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of Healthcare Informatics Research 2023;7(3):313 View
  6. Li J, Ge M, Deng P, Wu X, Shi L, Yang Y. Withaferin A suppressed hepatocellular carcinoma progression through inducing IGF2BP3/FOXO1/JAK2/STAT3 pathway-mediated ROS production. Immunopharmacology and Immunotoxicology 2024;46(1):40 View
  7. Wu C, Su C, Islam M, Liao M. Artificial Intelligence in Dementia: A Bibliometric Study. Diagnostics 2023;13(12):2109 View
  8. Mansur A, Vrionis A, Charles J, Hancel K, Panagides J, Moloudi F, Iqbal S, Daye D. The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers 2023;15(11):2928 View
  9. Shan D. Expanding the dialogue: A closer look at AIH management and HCC risk. Journal of Hepatology 2024;81(3):e129 View
  10. Daher H, Punchayil S, Ismail A, Fernandes R, Jacob J, Algazzar M, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024 View
  11. Asif A, Ahmed F, Zeeshan , Khan J, Allogmani E, Rashidy N, Manzoor S, Anwar M. Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma. IEEE Access 2024;12:37557 View
  12. Mostafa G, Mahmoud H, Abd El-Hafeez T, E.ElAraby M. The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review. BMC Medical Informatics and Decision Making 2024;24(1) View
  13. Kazi I, Jahagirdar V, Kabir B, Syed A, Kabir A, Perisetti A. Role of Imaging in Screening for Hepatocellular Carcinoma. Cancers 2024;16(19):3400 View

Books/Policy Documents

  1. Yang H, Li Y. Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases. View