{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/review-for-dynamic-prediction-in-clinical","title":"Review for Dynamic Prediction in Clinical Survival Analysis","arxiv_id":"2311.15743","date":"2023-11-27","proceeding":null,"authors":["He Weiyi"],"abstract":"The accurate prediction of patient prognosis is a critical challenge in clinical practice. With the availability of various patient information, physicians can optimize medical care by closely monitoring disease progression and therapy responses. To enable better individualized treatment, dynamic prediction models are required to continuously update survival probability predictions as new information becomes available. This article aims to offer a comprehensive survey of current methods in dynamic survival analysis, encompassing both classical statistical approaches and deep learning techniques. Additionally, it will also discuss the limitations of existing methods and the prospects for future advancements in this field.","url_abs":"https://arxiv.org/abs/2311.15743v1","url_pdf":"https://arxiv.org/pdf/2311.15743v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"review-for-dynamic-prediction-in-clinical","repo_url":"https://github.com/chl8856/dynamic-deephit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}