Biomedical Evidence Generation Engine
This addresses the need for scalable verification of insights in precision medicine, though it appears incremental as it builds on existing NLP components.
The paper tackles the problem of inefficient manual verification of data-driven biomedical insights by proposing a biomedical evidence generation task and developing an engine with a three-module pipeline for literature retrieval, skeleton information identification, and text summarization.
With the rapid development of precision medicine, a large amount of health data (such as electronic health records, gene sequencing, medical images, etc.) has been produced. It encourages more and more interest in data-driven insight discovery from these data. It is a reasonable way to verify the derived insights in biomedical literature. However, manual verification is inefficient and not scalable. Therefore, an intelligent technique is necessary to solve this problem. In this paper, we propose a task of biomedical evidence generation, which is very novel and different from existing NLP tasks. Furthermore, we developed a biomedical evidence generation engine for this task with the pipeline of three components which are a literature retrieval module, a skeleton information identification module, and a text summarization module.