TreeText-CTS: Compact, Source-Traceable Tree-Path Evidence for Irregular Clinical Time-Series Prediction
arXiv:2605.20292v1 Announce Type: new Abstract: Numerical time-series models can effectively process irregular electronic health record (EHR) trajectories, but they do not naturally expose the measurements and temporal patterns supporting each risk estimate as readable evidence. Existing text-based interfaces improve readability, but typically rely on either raw serialization, which is lengthy and redundant, or patient-level free-form summaries, which are difficult to trace to source measurements and time windows. To bridge this gap, we introduce TreeText-CTS (Clinical Time-Series), which conv
The proliferation of irregular electronic health record (EHR) data combined with advancements in AI interpretability is driving demand for verifiable prediction methods.
This development addresses a critical barrier to AI adoption in healthcare: the need for transparent, traceable, and reliable explanations for clinical risk predictions.
Clinical AI models can now provide more compact and source-traceable evidence for their predictions, moving beyond black-box approaches or overly complex explanations.
- · Healthcare AI developers
- · Medical practitioners
- · Patients
- · Regulatory bodies
- · Opaque AI systems in healthcare
- · Developers solely focused on prediction accuracy over interpretability
Increased trust and adoption of AI in clinical decision-making due to enhanced explainability.
Faster and more efficient integration of AI tools into standard medical workflows, potentially reducing diagnostic errors and improving patient outcomes.
The establishment of new industry standards and regulatory frameworks for transparent and verifiable AI in high-stakes applications, influencing other regulated sectors.
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Read at arXiv cs.LG