Identifying Evidence-Based Nudges in Biomedical Literature with Large Language Models

• URL: http://arxiv.org/abs/2602.10345v1
• Date: Tue, 10 Feb 2026 22:36:07 GMT
• Title: Identifying Evidence-Based Nudges in Biomedical Literature with Large Language Models
• Authors: Jaydeep Chauhan, Mark Seidman, Pezhman Raeisian Parvari, Zhi Zheng, Zina Ben-Miled, Cristina Barboi, Andrew Gonzalez, Malaz Boustani,
• Abstract summary: We present a scalable, AI-powered system that identifies and extracts evidence-based behavioral nudges from unstructured biomedical literature.<n>Nudges are subtle, non-coercive interventions that influence behavior without limiting choice, showing strong impact on health outcomes like medication adherence.
• Score: 2.2015514798912412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a scalable, AI-powered system that identifies and extracts evidence-based behavioral nudges from unstructured biomedical literature. Nudges are subtle, non-coercive interventions that influence behavior without limiting choice, showing strong impact on health outcomes like medication adherence. However, identifying these interventions from PubMed's 8 million+ articles is a bottleneck. Our system uses a novel multi-stage pipeline: first, hybrid filtering (keywords, TF-IDF, cosine similarity, and a "nudge-term bonus") reduces the corpus to about 81,000 candidates. Second, we use OpenScholar (quantized LLaMA 3.1 8B) to classify papers and extract structured fields like nudge type and target behavior in a single pass, validated against a JSON schema. We evaluated four configurations on a labeled test set (N=197). The best setup (Title/Abstract/Intro) achieved a 67.0% F1 score and 72.0% recall, ideal for discovery. A high-precision variant using self-consistency (7 randomized passes) achieved 100% precision with 12% recall, demonstrating a tunable trade-off for high-trust use cases. This system is being integrated into Agile Nudge+, a real-world platform, to ground LLM-generated interventions in peer-reviewed evidence. This work demonstrates interpretable, domain-specific retrieval pipelines for evidence synthesis and personalized healthcare.

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