PICO
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The proliferation of healthcare data has contributed to the widespread usage of the PICO paradigm for creating specific clinical questions from RCT.
PICO is a mnemonic that stands for:
Population/Problem: Addresses the characteristics of populations involved and the specific characteristics of the disease or disorder. Intervention: Addresses the primary intervention (including treatments, procedures, or diagnostic tests) along with any risk factors. Comparison: Compares the efficacy of any new interventions with the primary intervention. Outcome: Measures the results of the intervention, including improvements or side effects. PICO is an essential tool that aids evidence-based practitioners in creating precise clinical questions and searchable keywords to address those issues. It calls for a high level of technical competence and medical domain knowledge, but it’s also frequently very time-consuming.
Automatically identifying PICO elements from this large sea of data can be made easier with the aid of machine learning (ML) and natural language processing (NLP). This facilitates the development of precise research questions by evidence-based practitioners more quickly and precisely.
Empirical studies have shown that the use of PICO frames improves the specificity and conceptual clarity of clinical problems, elicits more information during pre-search reference interviews, leads to more complex search strategies, and yields more precise search results.
논문
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Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition
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AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs
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HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
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A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications
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PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
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Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
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Impact of detecting clinical trial elements in exploration of COVID-19 literature
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