# HELIOT: LLM-Based CDSS for Adverse Drug Reaction Management

arXiv: https://arxiv.org/abs/2409.16395 · HTML: https://mlsanity.com/paper/2409.16395/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://mlsanity.com/paper/2402.10588/) (2024)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results](https://mlsanity.com/paper/2305.07685/) (2023)

## Built on by (most cited first, 3 of 3)

- [LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison](https://mlsanity.com/paper/2502.06890/) (2025)
- [Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies](https://mlsanity.com/paper/2503.07306/) (2025)
- [Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models](https://mlsanity.com/paper/2605.20523/) (2026)

## Abstract (arXiv)

Medication errors significantly threaten patient safety, leading to adverse drug events and substantial economic burdens on healthcare systems. Clinical Decision Support Systems (CDSSs) aimed at mitigating these errors often face limitations when processing unstructured clinical data, including reliance on static databases and rule-based algorithms, frequently generating excessive alerts that lead to alert fatigue among healthcare providers. This paper introduces HELIOT, an innovative CDSS for adverse drug reaction management that processes free-text clinical information using Large Language Models (LLMs) integrated with a comprehensive pharmaceutical data repository. HELIOT leverages advanced natural language processing capabilities to interpret medical narratives, extract relevant drug reaction information from unstructured clinical notes, and learn from past patient-specific medication tolerances to reduce false alerts, enabling more nuanced and contextual adverse drug event warnings across primary care, specialist consultations, and hospital settings. An initial evaluation using a synthetic dataset of clinical narratives and expert-verified ground truth shows promising results. HELIOT achieves high accuracy in a controlled setting. In addition, by intelligently analyzing previous medication tolerance documented in clinical notes and distinguishing between cases requiring different alert types, HELIOT can potentially reduce interruptive alerts by over 50% compared to traditional CDSSs. While these preliminary findings are encouraging, real-world validation will be essential to confirm these benefits in clinical practice.