We are organizing the development of a text-mining assisted systematic review framework and conducting an exploratory study aimed at advancing methodologies for evidence synthesis. Our initiative aim to integrate text mining/data mining techniques to enhance the efficiency, accuracy, and scalability of systematic reviews across diverse domains.

We are seeking collaborators with extensive experience in text mining and/or data mining to contribute to the following objectives:

1) Development of automated or semi-automated pipelines for literature retrieval, screening, and data extraction (Proficiency with tools such as KNIME, Python (NLTK, spaCy, scikit-learn), R (tm, text2vec), or other relevant frameworks);

2) Implementation of advanced text analysis methodologies, including but not limited to topic modeling, named entity recognition (NER), trend analysis, and semantic analysis;

3) Contribution to peer-reviewed publications and open methodological resources.

Outputs

Co-authorship on publications, contributions to an open-access methodological handbook, and potential for continued collaboration on future knowledge synthesis projects.

If you are interested in contributing to this initiative, please include your contact information in your application so that we may follow up with you.