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

Decision Support for Health Professionals (1460) Clinical Information and Decision Making (1802) Advanced Data Analytics in eHealth (192) Machine Learning (1954) Natural Language Processing (866) Visualization in eHealth (65) Postoperative Monitoring and Telemonitoring (177) Clinical Information and Decision Making (1802) Natural Language Processing (866) Machine Learning (1954) Advanced Data Analytics in eHealth (192) Postoperative Monitoring and Telemonitoring (177) Visualization in eHealth (65)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49138, first published .
A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study

A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study

A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study

Authors of this article:

Haomin Li1 Author Orcid Image ;   Mengying Zhou1, 2 Author Orcid Image ;   Yuhan Sun1, 2 Author Orcid Image ;   Jian Yang1, 2 Author Orcid Image ;   Xian Zeng1, 2 Author Orcid Image ;   Yunxiang Qiu3 Author Orcid Image ;   Yuanyuan Xia3 Author Orcid Image ;   Zhijie Zheng3 Author Orcid Image ;   Jin Yu4 Author Orcid Image ;   Yuqing Feng1 Author Orcid Image ;   Zhuo Shi5 Author Orcid Image ;   Ting Huang5 Author Orcid Image ;   Linhua Tan3 Author Orcid Image ;   Ru Lin5 Author Orcid Image ;   Jianhua Li5 Author Orcid Image ;   Xiangming Fan5 Author Orcid Image ;   Jingjing Ye4 Author Orcid Image ;   Huilong Duan2 Author Orcid Image ;   Shanshan Shi3 Author Orcid Image ;   Qiang Shu5 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

Journals

  1. Van den Eynde J. CHDmap: One Step Further Toward Integrating Medicine-Based Evidence Into Practice. JMIR Medical Informatics 2024;12:e52343 View
  2. Manuilova I, Bossenz J, Weise A, Boehm D, Strantz C, Unberath P, Reimer N, Metzger P, Pauli T, Werle S, Schulze S, Hiemer S, Ustjanzew A, Kestler H, Busch H, Brors B, Christoph J. Identifications of Similarity Metrics for Patients With Cancer: Protocol for a Scoping Review. JMIR Research Protocols 2024;13:e58705 View
  3. Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial Intelligence in Surgery: A Systematic Review of Use and Validation. Journal of Clinical Medicine 2024;13(23):7108 View
  4. Mohammadi I, Rajai Firouzabadi S, Hosseinpour M, Akhlaghpasand M, Hajikarimloo B, Zeraatian-Nejad S, Sardari Nia P. Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review. BMC Cardiovascular Disorders 2024;24(1) View
  5. Li D, Shukla A, Chandaka S, Taylor B, Xu J, Liu M. Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study. JMIR Medical Informatics 2025;13:e68830 View
  6. Li D, Yu A, Fuhrman D, Liu M. SMART: a new patient similarity estimation framework for enhanced predictive modeling in acute kidney injury. Journal of the American Medical Informatics Association 2025 View
  7. Zhang H, Liu Q, Han X, Niu L, Sun W. TN5000: An Ultrasound Image Dataset for Thyroid Nodule Detection and Classification. Scientific Data 2025;12(1) View

Conference Proceedings

  1. Ouakasse F, Mosaif A. 2025 International Conference on Circuit, Systems and Communication (ICCSC). Integrating IoMT and AI for Real-Time Surgical Risk Assessment and Decision Support View