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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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TopicForest: embedding-driven hierarchical clustering and labeling for biomedical literature.

Chia-Hsuan Chang¹, Brian Ondov¹, Bin Choi²

¹Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, USA.

Journal of Biomedical Informatics

|November 16, 2025

Summary

TopicForest effectively organizes biomedical literature into hierarchical topic trees, offering multi-scale insights beyond flat clustering. This novel framework uses language models for robust topic discovery and interpretation.

Keywords:

Biomedical literature Large language model Topic labeling Topic modeling

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Area of Science:

Biomedical Informatics
Computational Biology
Data Science

Background:

The rapid growth of biomedical literature presents challenges for organizing and interpreting complex research topics.
Existing topic modeling methods often produce flat clusters, failing to capture the hierarchical nature of scientific subjects.
There is a need for advanced techniques to model the intricate hierarchies within biomedical research.

Purpose of the Study:

To develop TopicForest, an embedding-driven framework for hierarchical topic modeling in biomedical literature.
To create a forest of topic trees, enabling exploration from broad areas to narrow specialties.
To improve the organization and interpretation of complex biomedical research topics.

Main Methods:

Utilized contrastively trained large language models (LLMs) to embed biomedical abstracts.
Applied manifold learning for dimensionality reduction and visualization.
Implemented hierarchical clustering using binary partitioning and dendrogram cutting.
Employed recursive LLM-based summarization for topic labeling at multiple granularities.

Main Results:

TopicForest achieved competitive or superior Adjusted Mutual Information (AMI) scores compared to flat clustering methods like BERTopic.
The framework outperformed the deep hierarchical topic model HyperMiner in clustering performance.
LLM-based recursive labeling demonstrated higher label diversity and hierarchical affinity than existing methods.
TopicForest exhibited stable clustering quality across different embedding models, indicating robustness.

Conclusions:

TopicForest offers an effective and interpretable approach to hierarchical topic modeling for biomedical literature.
The integration of LLMs, dimension reduction, and hierarchical clustering facilitates multi-scale exploration and visualization of research corpora.
This framework enhances the ability to navigate and understand the complex landscape of biomedical research.