Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
arXiv:2602.12542v2 Announce Type: replace Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While domain adaptation (DA) methods can mitigate such shifts, their "black-box" nature prevents widespread adoption in clinical practice where transparency is essential for trust and safety. We propose ExtraCare to decompose patient representations into invariant and covariant components. By supervising these two components and enforcing their orthogonality during trainin
The increasing deployment of deep learning models in healthcare necessitates robust solutions for data distribution shifts, while the 'black-box' nature of current AI methods limits their clinical adoption.
Achieving transparency and reliability in AI for healthcare is critical for regulatory approval, patient trust, and effective clinical decision-making across diverse populations and medical systems.
The proposed 'ExtraCare' framework offers a path toward more explainable and adaptable AI in predictive healthcare, potentially accelerating the development and ethical deployment of such systems.
- · Healthcare AI developers
- · Hospitals and clinics
- · Patients
- · Medical technology sector
- · Developers of 'black-box' healthcare AI models
- · Systems without robust domain adaptation
Improved reliability and transparency of AI-driven clinical prediction lead to higher adoption rates in healthcare settings.
Enhanced trust in AI diagnosis and prognosis results in better patient outcomes and more efficient allocation of medical resources.
The development of clear regulatory frameworks for transparent AI becomes more feasible, setting a precedent for AI across other sensitive sectors.
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Read at arXiv cs.LG