PICO
25 papers with code • 1 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in PICO
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Most implemented papers
Towards Effective Visual Representations for Partial-Label Learning
Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance of PLL on vision tasks, attributed to representations learned by contrasting the same/different classes of entities.
Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review Automation
Medical systematic reviews can be very costly and resource intensive.
PiCO: Peer Review in LLMs based on the Consistency Optimization
PKU-YuanGroup/Peer-review-in-LLMs
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Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations.
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
But how factual are these summaries in a high-stakes domain like medicine?
