Clinical Trials Ontology Engineering with Large Language Models (2412.14387v1)

Abstract: Managing clinical trial information is currently a significant challenge for the medical industry, as traditional methods are both time-consuming and costly. This paper proposes a simple yet effective methodology to extract and integrate clinical trial data in a cost-effective and time-efficient manner. Allowing the medical industry to stay up-to-date with medical developments. Comparing time, cost, and quality of the ontologies created by humans, GPT3.5, GPT4, and Llama3 (8b & 70b). Findings suggest that LLMs (LLM) are a viable option to automate this process both from a cost and time perspective. This study underscores significant implications for medical research where real-time data integration from clinical trials could become the norm.

• The paper presents a methodology leveraging LLMs to semi-automate clinical trial ontology creation, reducing reliance on manual data processing.
• It compares automated methods with human processes using cost, time, and coverage metrics, showing notable improvements in efficiency.
• Evaluation using OQuaRE metrics highlights both the promise and limitations, suggesting future work on relationship mapping and hallucination reduction.

Clinical Trials Ontology Engineering with LLMs

The paper "Clinical Trials Ontology Engineering with LLMs" explores the application of LLMs such as GPT3.5, GPT4, and Llama3 in managing clinical trial information by automating the extraction and integration of data into ontologies. This approach addresses the inefficiencies and high costs associated with manual processing of clinical trial data. This essay examines the methodology and findings of the paper, focusing on the operational aspects, evaluation metrics, and implications for future AI developments in medical research.

Methodology

The proposed methodology centers on leveraging LLMs to semi-automate the conversion of clinical trial data into ontological frameworks, facilitating real-time data integration. This involves the creation of ontologies based on clinical trials data sourced from clinicaltrials.gov, focusing on diseases like diabetes.

The process initiates with the extraction of relevant data using LLMs, followed by ontology creation and merging. Each LLM-generated ontology is integrated into a single main ontology through a novel merging method designed specifically for handling outcomes from clinical trials. Its scalability stems from organizing data at the triple level and employing a sorted synonym list to minimize duplication, thereby maintaining operational efficiency.

Figure 1: High-overview of proposed methodology.

Evaluation

Practical Metrics

The paper evaluates models based on cost, time, and inclusivity of generated ontologies. Cost analysis considers both LLM operation costs and human processing extrapolated from limited samples. Time metrics involve the duration taken per trial and overall generation times across models.

Model	Total Cost / Trial	Total Time / Trial	Included Ontologies
Human	$\approx$ \$5 |$\approx$ 15 min.	100\%
GPT3	\$0.0054	143 sec.	76\%
chainedGPT3	\$0.0072	210 sec.	80\%
GPT4	\$0.0624	107 sec.	26\%
chainedGPT4	\$0.0941	212 sec.	86\%
Llama3 (8b)	\$0.0053	56 sec.	28\%
chainedLlama3 (8b)	\$0.0082	87 sec.	24\%
Llama3 (70b)	\$0.0579	110 sec.	54\%
chainedLlama3 (70b)	\$0.0898	171 sec.	74\%

OQuaRE Metrics

OQuaRE framework evaluates quality metrics like NOCOnto, highlighting extraction efficiency across models. Chained models generally outperform non-chained ones, with GPT4 displaying substantial improvements when prompting techniques are applied.

Figure 2: NOCOnto metric across different LLMs and used techniques.

Figure 3: All OQuaRE metrics across different LLMs and techniques.

Discussion

Implications

The paper suggests that deploying LLMs for clinical trial ontology engineering is feasible both economically and temporally. These models significantly reduce processing costs compared to human efforts, suggesting potential shifts in how medical data management might be operationalized for real-time access.

Limitations

While LLMs exhibit efficiency, limitations revolve around syntax validity and the lack of relationship preservation between concepts. Addressing hallucinations and developing more robust ontology merging protocols to preserve inter-concept relationships are crucial areas for future research.

Conclusion

The integration of LLMs into clinical trials data processing showcases a pragmatic step towards AI-assisted medical data management. By lowering costs and accelerating processing times, models like GPT4, especially when enhanced through strategic prompting, hold promise for transforming large-scale data integration practices in clinical settings. Future work remains necessary to enhance relationship mapping within ontologies and further mitigate data hallucinations.