To Use AI as Dice of Possibilities with Timing Computation

TL;DR

This paper proposes a novel verb-based paradigm and timing computation framework for AI, enabling data-driven discovery of patient trajectories and counterfactual timing deduction from longitudinal health data.

Contribution

It introduces a new paradigm and definitions that allow AI to model the future as an open temporal dimension, demonstrated on breast cancer patient data.

Findings

Automatic discovery of clinically significant patient trajectories

Counterfactual timing deduction from data

First data-driven demonstration of these capabilities in ML literature

Abstract

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.