# Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes arXiv: https://arxiv.org/abs/2302.05752 ยท HTML: https://mlsanity.com/paper/2302.05752/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 74 references that are in the map. - First posted on arXiv 11 Feb 2023. ## Builds on (most cited first, 21 of 21) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019) - [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017) - [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020) - [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018) - [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020) - [Interpretations are useful: penalizing explanations to align neural networks with prior knowledge](https://mlsanity.com/paper/1909.13584/) (2019) - [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019) - [Rethinking Explainability as a Dialogue: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01875/) (2022) - [Pre-trained Language Model for Biomedical Question Answering](https://mlsanity.com/paper/1909.08229/) (2019) - [Refining Language Models with Compositional Explanations](https://mlsanity.com/paper/2103.10415/) (2021) - [Principles of Explanation in Human-AI Systems](https://mlsanity.com/paper/2102.04972/) (2021) - [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021) - [Explanation Ontology: A Model of Explanations for User-Centered AI](https://mlsanity.com/paper/2010.01479/) (2020) - [User-Driven Research of Medical Note Generation Software](https://mlsanity.com/paper/2205.02549/) (2022) - [Directions for Explainable Knowledge-Enabled Systems](https://mlsanity.com/paper/2003.07523/) (2020) - [Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction](https://mlsanity.com/paper/2103.02768/) (2021) - [Foundations of Explainable Knowledge-Enabled Systems](https://mlsanity.com/paper/2003.07520/) (2020) - [Making Study Populations Visible through Knowledge Graphs](https://mlsanity.com/paper/1907.04358/) (2019) - [Disease Progression Modeling Workbench 360](https://mlsanity.com/paper/2106.13265/) (2021) - [Knowledge-based XAI through CBR: There is more to explanations than models can tell](https://mlsanity.com/paper/2108.10363/) (2021) ## Built on by (most cited first, 8 of 8) - [A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review](https://mlsanity.com/paper/2405.02559/) (2024) - [Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://mlsanity.com/paper/2312.00567/) (2023) - [A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems](https://mlsanity.com/paper/2502.09849/) (2025) - [Explainability-Driven Quality Assessment for Rule-Based Systems](https://mlsanity.com/paper/2502.01253/) (2025) - [Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice](https://mlsanity.com/paper/2503.04785/) (2025) - [Training and Validating a Treatment Recommender with Partial Verification Evidence](https://mlsanity.com/paper/2406.06654/) (2024) - [An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems](https://mlsanity.com/paper/2410.17504/) (2024) - [MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems](https://mlsanity.com/paper/2508.00300/) (2025) ## Abstract (arXiv) Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inferences to their context of use. However, their importance in improving model usage and understanding has not been extensively studied. Hence, we consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions. We explore how relevant information for such dimensions can be extracted from Medical guidelines to answer typical questions from clinical practitioners. We identify this as a question answering (QA) task and employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Finally, we study the benefits of contextual explanations by building an end-to-end AI pipeline including data cohorting, AI risk modeling, post-hoc model explanations, and prototyped a visual dashboard to present the combined insights from different context dimensions and data sources, while predicting and identifying the drivers of risk of Chronic Kidney Disease - a common type-2 diabetes comorbidity. All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel. We show that LLMs, in particular BERT and SciBERT, can be readily deployed to extract some relevant explanations to support clinical usage. To understand the value-add of the contextual explanations, the expert panel evaluated these regarding actionable insights in the relevant clinical setting. Overall, our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.