# Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies

arXiv: https://arxiv.org/abs/2605.22243 · HTML: https://mlsanity.com/paper/2605.22243/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions](https://mlsanity.com/paper/2202.11912/) (2022)
- [SurvSHAP(t): Time-dependent explanations of machine learning survival models](https://mlsanity.com/paper/2208.11080/) (2022)
- [Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data](https://mlsanity.com/paper/2004.00407/) (2020)
- [Explainable AI in Healthcare: to Explain, to Predict, or to Describe?](https://mlsanity.com/paper/2508.05753/) (2025)
- [Machine Learning for Medicine Must Be Interpretable, Shareable, Reproducible and Accountable by Design](https://mlsanity.com/paper/2508.16097/) (2025)
- [Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing](https://mlsanity.com/paper/2510.06165/) (2025)

## Built on by (most cited first, 1 of 1)

- [Large Language Models Extend Known Research But Do Not Create New Research Directions](https://mlsanity.com/paper/2610.17439/) (2026)

## Abstract (arXiv)

Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to design optimally by hand when tens or even hundreds of features require selection, transformation, or interaction modelling. While complex machine learning models offer high performance, their "black-box" nature limits the clinical trust, transparency, and interpretability required for decision-making. We developed and evaluated an Exploratory AI Recommender that provides data-driven recommendations to improve predictive performance of existing interpretable statistical models. The developed framework uses flexible AI modelling to capture complex data patterns and explainable AI techniques to translate the patterns into three recommendation types: feature exclusion, non-linear terms, and feature interactions. We evaluated the framework by comparing predictive performance of a baseline (i.e., no interactions or non-linear terms) Cox Proportional Hazards (CPH) model against an augmented CPH incorporating recommendations suggested by our method. The primary analysis predicts the time to the first occurrence of a fall or related injury in 245,614 patients. Our method recommended excluding 23 features, including non-linear terms for two features, and including 221 suggested feature interactions. The C-index improved from 0.805 (95% CI 0.798-0.812) to 0.815 (95% CI 0.809-0.822), and so did calibration (intercept: -0.006 to 0.003; slope: 1.063 to 0.950). All recommendations were supported by existing literature. The method also proved effective on two additional public datasets, demonstrating wider applicability. The proposed Exploratory AI Recommender demonstrates the potential of explainable AI and data-driven study design to improve the process of developing, and the performance of high-dimensional transparent predictive models.