arXiv:2607.01391v1 Announce Type: new Abstract: How do we encode numeric values in transformer-based sequence processing, particularly in electronic health record (EHR) data? We systematically compare discrete, continuous, and hybrid value encoding strategies using synthetic arithmetic tasks embedded within real-world EHR data, as well as real-world clinical prediction tasks. Our study reveals trade-offs between numeric precision, optimisation stability, and architectural flexibility. We find that approaches that explicitly model value-concept interactions perform best on precision-sensitive a
The increasing adoption of transformer models in sensitive domains like healthcare necessitates robust and precise data representation, making this research timely as AI systems move from theoretical to practical application.
Optimal encoding of numeric values is critical for AI systems performing complex analytical tasks, impacting accuracy in diagnoses, treatment plans, and overall system reliability in healthcare.
This research provides a systematic comparison and guidance on specific encoding strategies, moving the field towards more precise and stable AI models for numerical data in EHRs, potentially increasing trust and deployment in clinical settings.
- · AI developers in healthcare
- · Healthcare providers adopting AI
- · Patients receiving AI-assisted care
- · Medical research
- · AI models with suboptimal numeric encoding
- · Data scientists ignoring encoding nuances
Improved performance and reliability of AI systems processing electronic health records.
Accelerated integration of AI into clinical decision support and personalized medicine.
Enhanced patient outcomes and cost efficiencies in healthcare through more accurate AI diagnoses and predictions.
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Read at arXiv cs.LG