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BLINKtextsubscriptLSTM: BioLinkBERT and LSTM based approach for extraction of PICO frame from Clinical Trial Text paper by Independent / Community
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Paper

BLINKtextsubscriptLSTM: BioLinkBERT and LSTM based approach for extraction of PICO frame from Clinical Trial Text

by Independent / Community 00b68a19a60a0611df19da97365b2cf40c6224b0
Free2AITools Nexus Index
63.2
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 70
P: Popularity 45
R: Recency 100
Q: Quality 65
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Rapid growth in publication of clinical trial reports has made it extremely challenging to conduct systematic reviews. Automatic extraction of Population, Intervention, Comparator, and Outcome (PICO) from clinical trial reports can alleviate the traditionally adopted time-consuming process of systematic reviews. In this paper, we propose a novel approach for automatically detecting the PICO-related terms from clinical trial reports. Our techniques use BioLinkBERT as our base model with two Bi...

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Semantic Scholar 4 Citations
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Registry ID 00b68a19a60a0611df19da97365b2cf40c6224b0
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{00b68a19a60a0611df19da97365b2cf40c6224b0,
  author = {Unknown},
  title = {BLINKtextsubscriptLSTM: BioLinkBERT and LSTM based approach for extraction of PICO frame from Clinical Trial Text Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00b68a19a60a0611df19da97365b2cf40c6224b0}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). BLINKtextsubscriptLSTM: BioLinkBERT and LSTM based approach for extraction of PICO frame from Clinical Trial Text [Paper]. Free2AITools. https://api.semanticscholar.org/00b68a19a60a0611df19da97365b2cf40c6224b0

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 70
Popularity (P) 45
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for BLINKtextsubscriptLSTM: BioLinkBERT and LSTM based approach for extraction of PICO frame from Clinical Trial Text: Authority (A:70), Popularity (P:45), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
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πŸ“ Executive Summary

"Rapid growth in publication of clinical trial reports has made it extremely challenging to conduct systematic reviews. Automatic extraction of Population, Intervention, Comparator, and Outcome (PICO) from clinical trial reports can alleviate the traditionally adopted time-consuming process of systematic reviews. In this paper, we propose a novel approach for automatically detecting the PICO-related terms from clinical trial reports. Our techniques use BioLinkBERT as our base model with two Bi..."

❝ Cite Node 

@article{Unknown2026BLINKtextsubscriptLSTM:,
  title={BLINKtextsubscriptLSTM: BioLinkBERT and LSTM based approach for extraction of PICO frame from Clinical Trial Text},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“ˆ4CitationsSemantic Scholar
πŸ›οΈ70AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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author
Unknown
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ArXiv
tags
paper, research, academic

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architecture
null
params billions
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