𝗔𝗜 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗘𝘅𝗧𝗥𝗔𝗖𝗧𝗜𝗢𝗡

𝗔𝗜 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗘𝘅𝗧𝗥𝗔𝗖𝗧𝗜𝗢𝗡

Reading dozens of papers for one number is hard. You miss details. Your research gap stays hidden.

Use the I/E-O-M-P framework. Treat every study as four slots.

• I/E: Intervention or Exposure.
• O: Outcome.
• M: Methods.
• P: Population.

Fill these slots with data like age, dosage, and effect size.

Use SciSpaCy for this. It finds dates and numbers. It finds drug names and sample sizes.

A PhD student runs SciSpaCy on 30 diabetes papers. The tool finds sample sizes and effect sizes. The student finds a gap in long-term follow-up.

Follow these steps:

• Gather PDFs and clean the text.
• Map entities to the I/E-O-M-P slots.
• Let a human check critical data.

Verify primary outcome effect sizes. Human checks must be 100 percent.

AI handles routine work. Experts handle critical data. You get a clean dataset to find research gaps.

Source: https://dev.to/ken_deng_ai/deep-dive-extraction-using-ai-to-pull-key-findings-methods-and-populations-from-full-texts-136n Optional learning community: https://t.me/GyaanSetuAi