MedBertNet: Harvesting the Medical Knowledge Graph with Integrated GAN and Pretrained Language Models
Authors: Jiyun Li, Dingbang Jiang, Chen Qian


Reviewer paper summaries: This paper introduces MedBertNet, a framework that integrates pre-trained LM and GANs to construct high-quality medical KG. MedBertNet includes a novel LM-GAN for enhanced entity pair generation, a dynamic prompt generation module, and an optimized generation strategy. Experimental results show that MedBertNet significantly outperforms existing models like BertNet, demonstrating higher accuracy in generating medical knowledge from datasets like ClinicalTrials.gov and the National Inpatient Sample.
Summary of strengths: `Novel integration of technologies`: The paper introduces MedBertNet, which uniquely combines pre-trained language models (LMs) and Generative Adversarial Networks (GANs) for constructing medical knowledge graphs (KGs). This integration addresses the specific challenges of limited samples and complex relationships in the medical domain, offering a fresh and innovative approach to KG construction.

`Dynamic prompt generation`: The dynamic prompt generation module ensures the creation of semantically consistent and contextually appropriate medical prompts. This component enhances the quality and relevance of the generated medical entity pairs as results shown in Table 3.
Summary of weaknesses: `Overstated Novelty`: In Line 119-120, the paper claims to be the first work to construct a medical knowledge graph (KG), but there is extensive prior work in this area. For example, a recent literature review by Abu-Salih et al. (2023), "Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities," provides an overview of existing medical KGs. Also, the paper lacks a discussion of medical KGs in the Related Work section.

`Clarity Issues in the LM-GAN Section`: 

Section 3.2 LM-GAN is unclear to me, particularly the definitions of medical consistency loss $ L_{csis} $ and knowledge constraint loss $ L_{cstr} $. The abbreviations "csis" and "cstr" are not intuitive. Why were these abbreviations chosen?
 
For line 299, how is $\text{sim}(E)$ calculated? If  $E$ is a vector, how is the "maximum similarity of entity $E$" determined?

The symbol  $ I $ in line 300 is not defined in Equation 2. What does $ I $ represent here?

 How is $ \text{UMLS}(E_1, E_2) $ implemented to check relationships in the UMLS KG?
Is there any reference or related work that supports the concept of the consistency loss defined in Equation 2? If so, it should be cited for the audience’s benefit.

Do you mean $ L_{cstr} $ in line 307?
Comments, suggestions and typos: What’s your reproduction policy? Only the annotation interface is listed in Appendix.

Line 66: do not say “we introduce Generative Adversarial Networks (GANs)” if you’re not the authors of GAN.

Line 113-116: verbose, can change to "Hao et al. (2022) focused on extracting and constructing large-scale KGs using pre-trained models." The clarity of the paper can be improved.
Overall assessment scores: 2.5