Source-Audited Mining and Visualization of Real-World Traditional Chinese Medicine Case Records Using Hegan Jianpi Formula.

Real-world traditional Chinese medicine (TCM) outpatient records contain longitudinal information on diagnoses, syndrome elements, symptom narratives, and individualized prescriptions, but their semi-structured format complicates reproducible analysis. This article presents a source-audited protocol for converting de-identified case records into traceable aggregate tables and publication-ready visualizations. The workflow defines eligibility criteria and analysis units, preserves mappings between original and normalized terminology, verifies all reported numerators and denominators, and regenerates figures from versioned source tables. Applied to records from January 2015 through June 2024, the protocol identified 555 eligible prescription visits from 396 patients. The final dataset contained 324 normalized herb labels and 9,339 herb-use occurrences. The workflow generated parallel Western medicine and TCM disease spectra, an explicit syndrome-element spectrum and co-occurrence matrix, herb-frequency and materia medica profiles, a 37-term history-derived symptom spectrum, 15 directed association rules, hierarchical clustering, and diagnosis-stratified herb-use heatmaps. At least one prespecified history keyword was detected in 474 visits (85.41%). When permitted by institutional policy, a human-supervised artificial intelligence (AI) option may assist with terminology checks, cross-file consistency review, and caption-figure alignment. Any use of AI must be documented in a separate audit record and must not replace source-data review. By integrating governance, normalization, computation, clinical interpretation, and visual output into a single reproducible workflow, the method transforms dispersed clinical documentation into a reviewable research resource. The protocol can be adapted to other expert-practice datasets, multicenter terminology projects, and prospective clinical data platforms.