---
title: "BFHD: Bidirectional Feature Harmonization Decomposition for Heterogeneous Clinical Assessments"
authors:
  - "Yuanhao Zhuo"
  - "Zixi Qin"
  - "Ling Qin"
  - "Wanqing Li"
doi: "10.24963/ijcai.2026/784"
url: "https://ijcai.org/proceedings/2026/784"
pdf: "https://ijcai.org/proceedings/2026/0784.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7046-7054"
track: "AI and Health"
keywords:
  - "Multimodal data: Multimodal data"
  - "Public health: Public health"
  - "Clinical decision support system: Clinical decision support system"
---

# BFHD: Bidirectional Feature Harmonization Decomposition for Heterogeneous Clinical Assessments

Yuanhao Zhuo, Zixi Qin, Ling Qin, Wanqing Li

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7046-7054. DOI: [10.24963/ijcai.2026/784](https://doi.org/10.24963/ijcai.2026/784)

## Abstract

Clinical assessments are often collected using heterogeneous assessment systems across centers and time, leading to records that mix different but related sets of measurements. This motivates harmonization beyond total-score linking. We formulate clinical harmonization at the measurement level as a bidirectional recoverability problem: given paired observations from two assessment systems, the goal is to identify which measurements can be reliably translated in both directions within an application-defined tolerance, while separating non-translatable components. We propose Bidirectional Feature Harmonization Decomposition (BFHD), a feasibility-driven framework that enforces bidirectionally coupled translation and uses feature-wise output gating to produce an explicit decomposition in the original measurement space. Experiments on synthetic data and real clinical assessment pairs show that BFHD achieves broader feasible harmonization coverage and improved subset stability compared to baselines.