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LLM framework automates cohort discovery in scientific literature

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel framework that leverages large language models (LLMs) to automate the discovery of specific participant cohorts within scientific literature. This system generates targeted queries for databases like PubMed, then uses an LLM to extract explicit cohort names from retrieved abstracts. In a test case for youth aggression genetics, the framework identified 44 eligible cohorts, with its LLM-based extraction performing comparably to human annotators and uncovering 17 cohorts not found in existing catalogues. AI

IMPACT Automates literature review for cohort identification, potentially accelerating multi-study analysis and biomedical research.

RANK_REASON The cluster contains an academic paper detailing a new framework and its evaluation.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM framework automates cohort discovery in scientific literature

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Moritz Sturm, Lisa M. Berg, Inken Berg, Harishny Sarma, Jasmin Hartmann, Denissa Girschik, Gemma Roig, Christine M. Freitag, Andreas G. Chiocchetti ·

    Large language model-assisted discovery of cohorts from scientific literature

    arXiv:2608.15909v1 Announce Type: cross Abstract: Background: Planning multi-study analyses requires identifying cohorts with the relevant participants, phenotypes, and data modalities. This process commonly relies on prior knowledge, cohort catalogues, and manual literature sear…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Andreas G. Chiocchetti ·

    Large language model-assisted discovery of cohorts from scientific literature

    Background: Planning multi-study analyses requires identifying cohorts with the relevant participants, phenotypes, and data modalities. This process commonly relies on prior knowledge, cohort catalogues, and manual literature searches. We developed a complementary question-driven…

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