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Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings
Murthy V Devarakonda1, Smita Mohanty1, Raja Rao Sunkishala1
1Biomedical Research, Novartis, Cambridge, MA, USA.
This study introduces a novel method using knowledge graph embeddings and inductive inference to recommend clinical trial designs. The approach effectively mines past trial data, improving future clinical trial planning.
Area of Science:
- Biomedical Informatics
- Clinical Trial Design
- Artificial Intelligence
Background:
- Clinical trial design requires numerous complex decisions.
- Mining historical clinical trial data can inform these decisions.
- Existing methods lack comprehensive data-driven recommendations.
Purpose of the Study:
- To develop a recommendation system for clinical trial design.
- To leverage knowledge graph embeddings and inductive inference for this purpose.
- To improve the efficiency and effectiveness of clinical trial planning.
Main Methods:
- Constructed a novel knowledge graph from clinical trials data.
- Applied neural embeddings and evaluated various embedding techniques.
- Utilized a semantics-driven inductive inference method for recommendations.
- Used publicly available data from clinicaltrials.gov.
Main Results:
- Achieved relevance scores for recommendations between 70% and 83%.
- Demonstrated that top-ranked recommendations were highly pertinent.
- Validated the effectiveness of the proposed knowledge graph and inference method.
Conclusions:
- Inductive inference with node semantics is effective for generating clinical trial design recommendations.
- Knowledge graph embeddings offer a powerful approach for mining clinical trial data.
- Potential exists for further enhancement of graph embedding training using node semantics.
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