An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

Abstract Background The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM. Aim Our study presents a novel and significant contribution by developing an interpretable fusion model t...

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Bibliographic Details
Main Authors: Jia Li, Xinghao Wang, Linkun Cai, Jing Sun, Zhenghan Yang, Wenjuan Liu, Zhenchang Wang, Han Lv
Format: Article
Language:English
Published: Wiley 2023-09-01
Series:Cancer Medicine
Subjects:
artificial intelligence
bidirectional encoding representation of transformer
electronic health records
interpretable deep learning
natural language processing
Online Access:https://doi.org/10.1002/cam4.6523

Internet

https://doi.org/10.1002/cam4.6523

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