A retrospective analysis of key predictors and patient outcomes: Using artificial intelligence for precision survival prediction in colorectal cancer

This study model's structure emphasizes these predictors' interconnectedness because parent nodes directly connect to child nodes. The model shows how age, smoking status, marital status, cancer stage, and recurrence affect patient survival. The model clarifies these variables' interactions.

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Publication Details

Journal
PubMed
Published
2025-08-29
DOI
https://doi.org/10.3205/000345
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

A retrospective analysis of key predictors and patient outcomes: Using artificial intelligence for precision survival prediction in colorectal cancer

Eslam Bani Mohammad, Muayyad Mustafa Ahmad
PubMed
Radiomics and Machine Learning in Medical Imaging
article

A retrospective analysis of key predictors and patient outcomes: Using artificial intelligence for precision survival prediction in colorectal cancer

Eslam Bani Mohammad, Muayyad Mustafa Ahmad
article en

Abstract

This study model's structure emphasizes these predictors' interconnectedness because parent nodes directly connect to child nodes. The model shows how age, smoking status, marital status, cancer stage, and recurrence affect patient survival. The model clarifies these variables' interactions.

PubMedVol. 23
University of Jordan (JO), Al-Balqa Applied University (JO)
Openalex Percentile: Top 62%
Radiomics and Machine Learning in Medical Imaging
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