Biotechnology Research Company

Fresh Gravity developed a LLAMA-based machine learning model to intelligently process PubMed articles by identifying and contextualizing entities with the goal to generate summaries with traceable source information. The model was able to identify critical data entities in PubMed articles with an accuracy of nearly 85%, and in early testing, this resulted in achieving an efficiency gain of nearly 6%.

Problem

The client faced problems in processing large manual steps that involved sorting, filtering, and curating information that sometimes led to rejection of data and was time consuming. Along with this, identification of entities from paper, which is entirely a manual task, proved to be a time consuming and costly process.

Solution

Fresh Gravity developed a LLAMA-based machine learning model to process PubMed articles that generated a summary for the PubMed article highlighting the extracted entities. The solution identified the required entities in the paper using intelligent algorithm to capture context and dependencies between entities.

Impact

After the implementation the client was able to achieve time efficiency of 94.4% and ~85% accuracy in entity identification of independent entities. The solution generated the summary for the PubMed article and highlighted the criticality of annotated dataset gaps for dependent entities identification.