𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗦𝗰𝗿𝗲𝗲𝗻𝗶𝗻𝗴

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗦𝗰𝗿𝗲𝗲𝗻𝗶𝗻𝗴

Manual paper screening takes too long. Independent scientists waste weeks on this. You need a faster way.

The goal is high recall. You do not want to miss relevant papers. Train a model to label papers as include or exclude. This creates a discard pile.

Use a scikit-learn pipeline. Use a TF-IDF vectorizer. Use Logistic Regression. Set the threshold to 0.95 recall.

Follow these steps:

• Make a dataset. Use a spreadsheet. Record titles, abstracts, and labels.
• Train the model. Use scikit-learn. Set max features to 5000. Use 1 to 2 n-grams.
• Check the results. Sample the exclude pile. Ensure no good papers are there.

This process saves time. You spend more time on analysis and less on sorting.

Source: https://dev.to/ken_deng_ai/the-first-pass-automating-title-and-abstract-screening-with-classification-models-766 Optional learning community: https://t.me/GyaanSetuAi