Cancer outcomes predicted using AI-extracted data from clinical notes

Electronic health records and tumour-genome profiling offer promising real-world data with which to model cancer outcomes. Models built by using artificial intelligence (AI) to annotate clinical reports from more than 20,000 individuals with cancer can predict survival, as well as identify genomic features associated with metastasis and response to immunotherapy. Applying natural language processing to health records generates a data set that can be used to predict cancer outcomes.

Publication Details

Journal
Nature
Published
2025-02-05
DOI
https://doi.org/10.1038/d41586-025-00335-5
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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article

Cancer outcomes predicted using AI-extracted data from clinical notes

1 citations
Nature
Radiomics and Machine Learning in Medical Imaging
1.36
article

Cancer outcomes predicted using AI-extracted data from clinical notes

article en
1 citations

Abstract

Electronic health records and tumour-genome profiling offer promising real-world data with which to model cancer outcomes. Models built by using artificial intelligence (AI) to annotate clinical reports from more than 20,000 individuals with cancer can predict survival, as well as identify genomic features associated with metastasis and response to immunotherapy. Applying natural language processing to health records generates a data set that can be used to predict cancer outcomes.

Nature
Zero hunger
Openalex Percentile: Top 24%
Radiomics and Machine Learning in Medical Imaging
1.36
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Cancer outcomes predicted using AI-extracted data from clinical notes · Nature (2025) | TGRS Research Map | TGRS