Automated data extraction of unstructured and grey literature data in health research: a mapping review of the current research literature
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Background: The amount of grey literature and ‘softer’ intelligence from social media or websites is increasing. Compared with the long lead-times of producing high-quality peer-reviewed health information this is causing a demand for new and creative ways to provide prompt input for secondary research. Automated data extraction could potentially make unstructured data more accessible to individuals conducting various types of literature reviews. This is the first review of automated data extraction from health-related grey literature and soft intelligence, with a focus on (semi)automating horizon scans, health technology assessments, evidence maps, or other secondary literature reviews.Methods: We searched MEDLINE (PubMed), Scopus, ACL Anthology, dblp, arXiv (computer science), and MedRxiv to cover both health- and computer-science li' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Artificial intelligence","Literature review","Natural language processing"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Background: The amount of grey literature and ‘softer’ intelligence from social media or websites is increasing. Compared with the long lead-times of producing high-quality peer-reviewed health information this is causing a demand for new and creative ways to provide prompt input for secondary research. Automated data extraction could potentially make unstructured data more accessible to individuals conducting various types of literature reviews. This is the first review of automated data extraction from health-related grey literature and soft intelligence, with a focus on (semi)automating horizon scans, health technology assessments, evidence maps, or other secondary literature reviews.Methods: We searched MEDLINE (PubMed), Scopus, ACL Anthology, dblp, arXiv (computer science), and MedRxiv to cover both health- and computer-science li' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Background: The amount of grey literature and ‘softer’ intelligence from social media or websites is increasing. Compared with the long lead-times of producing high-quality peer-reviewed health information this is causing a demand for new and creative ways to provide prompt input for secondary research. Automated data extraction could potentially make unstructured data more accessible to individuals conducting various types of literature reviews. This is the first review of automated data extraction from health-related grey literature and soft intelligence, with a focus on (semi)automating horizon scans, health technology assessments, evidence maps, or other secondary literature reviews.Methods: We searched MEDLINE (PubMed), Scopus, ACL Anthology, dblp, arXiv (computer science), and MedRxiv to cover both health- and computer-science li
Artificial intelligence; Literature review; Natural language processing