# Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models

arXiv: https://arxiv.org/abs/2502.19625 · HTML: https://mlsanity.com/paper/2502.19625/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations](https://mlsanity.com/paper/2306.04618/) (2023)
- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [The Data Addition Dilemma](https://mlsanity.com/paper/2408.04154/) (2024)
- [NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs](https://mlsanity.com/paper/2312.11803/) (2023)
- [Clustering Interval-Censored Time-Series for Disease Phenotyping](https://mlsanity.com/paper/2102.07005/) (2021)
- [Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models](https://mlsanity.com/paper/2402.03597/) (2024)
- [A large language model-based approach to quantifying the effects of social determinants in liver transplant decisions](https://mlsanity.com/paper/2412.07924/) (2024)
- [Access to care improves EHR reliability and clinical risk prediction model performance](https://mlsanity.com/paper/2412.07712/) (2024)

## Built on by (most cited first, 2 of 2)

- [OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction](https://mlsanity.com/paper/2604.16878/) (2026)
- [Evidence-Unit Fairness and the Limits of Query-Adaptive Sparse-Dense Fusion in Financial Document Retrieval](https://mlsanity.com/paper/2608.00183/) (2026)

## Abstract (arXiv)

Machine learning systems trained on electronic health records (EHRs) increasingly guide treatment decisions, but their reliability depends on the critical assumption that patients follow the prescribed treatments recorded in EHRs. Using EHR data from 3,623 hypertension patients, we investigate how treatment non-adherence introduces implicit bias that can fundamentally distort both causal inference and predictive modeling. By extracting patient adherence information from clinical notes using a large language model (LLM), we identify 786 patients (21.7%) with medication non-adherence. We further uncover key demographic and clinical factors associated with non-adherence, as well as patient-reported reasons including side effects and difficulties obtaining refills. Our findings demonstrate that this implicit bias can not only reverse estimated treatment effects, but also degrade model performance by up to 5% while disproportionately affecting vulnerable populations by exacerbating disparities in decision outcomes and model error rates. This highlights the importance of accounting for treatment non-adherence in developing responsible and equitable clinical machine learning systems.