AI models need up to 18 years of EHR data for colorectal cancer prediction

Researchers have introduced the concept of "time-context" to evaluate how far back in time an AI model needs to look for predictive signals in electronic health records (EHRs). A study using a transformer model on EHR data from over 3.6 million patients found that predictive accuracy for colorectal cancer increased up to 18 years of lookback, with most gains achieved within 10 years. The model's performance plateaued thereafter, suggesting that while long-term historical data is crucial, there are limits to its predictive utility. AI

IMPACT Introduces 'time-context' as a key metric for clinical prediction models, suggesting a need for longer historical data in EHR foundation models.

RANK_REASON The cluster contains an academic paper detailing a new methodology and experimental results for AI model performance. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• colorectal cancer
• electronic health records
• London North West University Healthcare NHS Trust
• Rotary Position Embeddings
• transformer
• XGBoost

• paper
• model release
• other

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