# Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applications

arXiv: https://arxiv.org/abs/2303.09322 · HTML: https://mlsanity.com/paper/2303.09322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges](https://mlsanity.com/paper/1803.07517/) (2018)
- [Causal interpretation rules for encoding and decoding models in neuroimaging](https://mlsanity.com/paper/1511.04780/) (2015)
- [Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations](https://mlsanity.com/paper/1904.03959/) (2019)
- [Understanding surrogate explanations: the interplay between complexity, fidelity and coverage](https://mlsanity.com/paper/2107.04309/) (2021)

## Abstract (arXiv)

The use of machine learning (ML) techniques in the biomedical field has become increasingly important, particularly with the large amounts of data generated by the aftermath of the COVID-19 pandemic. However, due to the complex nature of biomedical datasets and the use of black-box ML models, a lack of trust and adoption by domain experts can arise. In response, interpretable ML (IML) approaches have been developed, but the curse of dimensionality in biomedical datasets can lead to model instability. This paper proposes a novel computational strategy for the stratification of biomedical problem datasets into k-fold cross-validation (CVs) and integrating domain knowledge interpretation techniques embedded into the current state-of-the-art IML frameworks. This approach can improve model stability, establish trust, and provide explanations for outcomes generated by trained IML models. Specifically, the model outcome, such as aggregated feature weight importance, can be linked to further domain knowledge interpretations using techniques like pathway functional enrichment, drug targeting, and repurposing databases. Additionally, involving end-users and clinicians in focus group discussions before and after the choice of IML framework can help guide testable hypotheses, improve performance metrics, and build trustworthy and usable IML solutions in the biomedical field. Overall, this study highlights the potential of combining advanced computational techniques with domain knowledge interpretation to enhance the effectiveness of IML solutions in the context of complex biomedical datasets.