How AI Can Extract Complex Dosing Schedules From Clinical Trial Protocols

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Clinical trial protocols contain a dense 'schedule of activities' table that maps visits against procedures, presenting significant challenges for automated data extraction. The table is often wider than a page, uses merged header cells across two rows, and carries information purely through cell position rather than explicit values. Column headers follow compressed notations like 'C1D1' (Cycle 1, Day 1) and include event-anchored milestones such as End of Treatment that cannot be mapped to absolute dates. A critical convention is that there is no Day 0 — the first dose day is Day 1, meaning zero-based indexing will consistently produce incorrect calculations. Accurate extraction also requires capturing footnote markers on individual cells, which often convert a scheduled procedure into a conditional one.

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