To Use AI as Dice of Possibilities with Timing Computation
For AI researchers and clinicians, this work proposes a new paradigm to represent temporal dynamics, but the empirical demonstration is limited to a single dataset and the novelty is more conceptual than practical.
The paper introduces a verb-based paradigm for AI that models timing and possibility, enabling automatic discovery of clinically significant patient trajectories and counterfactual timing deduction from EHR data of 3,276 breast cancer patients, achieving first-of-its-kind results without prior domain knowledge.
The dominant noun-based modeling paradigm has fundamentally constrained AI development, precluding any adequate representation of the future as an open temporal dimension. This paper introduces a verb-based paradigm, together with precise definitions of \emph{timing computation} and \emph{possibility}, that enables AI to function as an effective instrument for realizing the grammar of our thought. Applied to longitudinal EHR data from 3,276 breast cancer patients, the framework empirically demonstrates: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction. Both results are purely data-driven, require no prior domain knowledge, and, to our knowledge, represent the first such demonstrations in the machine learning literature.