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PKG-LLM: A Framework for Predicting GAD and MDD Using Knowledge Graphs and Large Language Models in Cognitive Neuroscience

Ali Sarabadani,Hadis Taherinia,Niloufar Ghadiri,
Ehsan Karimi Shahmarvandi,
Ramin Mousa  *

Abstract
Purpose: This research project has a single purpose: the construction and evaluation of PKG-LLM, a knowledge graph framework whose application is primarily intended for cognitive neuroscience. It also aims to improve predictions of relationships among neurological entities and improve named entity recognition (NER) and relation extraction (RE) from large neurological datasets. Employing the GPT-4 and expert review, we aim to demonstrate how this framework may outperform traditional models by way of precision, recall, and F1 score, intending to provide key insights into possible future clinical and research applications in the field of neuroscience. Method: In the evaluation of PKG-LLM, there were two different tasks primarily: relation extraction (RE) and named entity recognition (NER). Both tasks processed data and obtained performance metrics, such as precision, recall, and F1-score, using GPT-4. Moreover, there was an integration of an expert review process comprising neurologists and domain experts reviewing those extracted relationships and entities and improving such final performance metrics. Model comparative performance was reported against StrokeKG and Heart Failure KG. On the other hand, PKG-LLM evinced itself to link prediction-in-cognition through metrics such as Mean Rank (MR), Mean Reciprocal Rank (MRR), and Precision at K (P@K). The model was evaluated against other link prediction models, including TransE, RotatE, DistMult, ComplEx, ConvE, and HolmE. Findings: PKG-LLM demonstrated competitive performance in both relation extraction and named entity recognition tasks. In its traditional form, PKG-LLM achieved a precision of 75.45\%, recall of 78.60\%, and F1-score of 76.89\% in relation extraction, which improved to 82.34\%, 85.40\%, and 83.85\% after expert review. In named entity recognition, the traditional model scored 73.42\% precision, 76.30\% recall, and 74.84\% F1-score, improving to 81.55\%, 84.60\%, and 82.99\% after expert review. For link prediction, PKG-LLM achieved an MRR of 0.396, P@1 of 0.385, and P@10 of 0.531, placing it in a competitive range compared to models like TransE, RotatE, and ConvE. Conclusion: This study showed that PKG-LLM mainly outperformed the existing models by adding expert reviews in its application in extraction and named entity recognition tasks. Further, the model's competitive edge in link prediction lends credence to its capability in knowledge graph construction and refinement in the field of cognitive neuroscience as well. PKG-LLM's superiority over existing models and its ability to generate more accurate results with clinical relevance indicates that it is a significant tool to augment neuroscience research and clinical applications. The evaluation process entailed using GPT-4 and expert review. This approach ensures that the resulting knowledge graph is scientifically compelling and practically beneficial in more advanced cognitive neuroscience research.


Link: https://www.preprints.org/manuscript/202502.0982/v1
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2.7K viewsedited  Feb 23 at 16:22

PKG-LLM: A Framework for Predicting GAD and MDD Using Knowledge Graphs and Large Language Models in Cognitive Neuroscience

Ali Sarabadani,Hadis Taherinia,Niloufar Ghadiri,
Ehsan Karimi Shahmarvandi,
Ramin Mousa  *

Abstract
Purpose: This research project has a single purpose: the construction and evaluation of PKG-LLM, a knowledge graph framework whose application is primarily intended for cognitive neuroscience. It also aims to improve predictions of relationships among neurological entities and improve named entity recognition (NER) and relation extraction (RE) from large neurological datasets. Employing the GPT-4 and expert review, we aim to demonstrate how this framework may outperform traditional models by way of precision, recall, and F1 score, intending to provide key insights into possible future clinical and research applications in the field of neuroscience. Method: In the evaluation of PKG-LLM, there were two different tasks primarily: relation extraction (RE) and named entity recognition (NER). Both tasks processed data and obtained performance metrics, such as precision, recall, and F1-score, using GPT-4. Moreover, there was an integration of an expert review process comprising neurologists and domain experts reviewing those extracted relationships and entities and improving such final performance metrics. Model comparative performance was reported against StrokeKG and Heart Failure KG. On the other hand, PKG-LLM evinced itself to link prediction-in-cognition through metrics such as Mean Rank (MR), Mean Reciprocal Rank (MRR), and Precision at K (P@K). The model was evaluated against other link prediction models, including TransE, RotatE, DistMult, ComplEx, ConvE, and HolmE. Findings: PKG-LLM demonstrated competitive performance in both relation extraction and named entity recognition tasks. In its traditional form, PKG-LLM achieved a precision of 75.45\%, recall of 78.60\%, and F1-score of 76.89\% in relation extraction, which improved to 82.34\%, 85.40\%, and 83.85\% after expert review. In named entity recognition, the traditional model scored 73.42\% precision, 76.30\% recall, and 74.84\% F1-score, improving to 81.55\%, 84.60\%, and 82.99\% after expert review. For link prediction, PKG-LLM achieved an MRR of 0.396, P@1 of 0.385, and P@10 of 0.531, placing it in a competitive range compared to models like TransE, RotatE, and ConvE. Conclusion: This study showed that PKG-LLM mainly outperformed the existing models by adding expert reviews in its application in extraction and named entity recognition tasks. Further, the model's competitive edge in link prediction lends credence to its capability in knowledge graph construction and refinement in the field of cognitive neuroscience as well. PKG-LLM's superiority over existing models and its ability to generate more accurate results with clinical relevance indicates that it is a significant tool to augment neuroscience research and clinical applications. The evaluation process entailed using GPT-4 and expert review. This approach ensures that the resulting knowledge graph is scientifically compelling and practically beneficial in more advanced cognitive neuroscience research.


Link: https://www.preprints.org/manuscript/202502.0982/v1
@Machine_learn

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