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
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
evidence-surveillance/sent2span
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Findings (EMNLP) 2021
The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials.
Truth Discovery in Sequence Labels from Crowds
nasimisu/truth-discovery-in-sequence-labels-from-crowds
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The proposed Aggregation method for Sequential Labels from Crowds ($AggSLC$) jointly considers the characteristics of sequential labeling tasks, workers' reliabilities, and advanced machine learning techniques.
Contrastive Label Disambiguation for Partial Label Learning
hbzju/pico
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ICLR 2022
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
hbzju/pico
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Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
Multi-Agent Path Finding with Prioritized Communication Learning
mail-ecnu/PICO
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The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy.
Addressing Gap between Training Data and Deployed Environment by On-Device Learning
This article introduces a neural network based on-device learning (ODL) approach to address this issue by retraining in deployed environments.
LinkBERT: Pretraining Language Models with Document Links
michiyasunaga/LinkBERT
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ACL 2022
Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks.
Pre-trained language models with domain knowledge for biomedical extractive summarization
xashely/KeBioSum
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Knowledge-Based Systems 2022
Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature.
Simulating single-photon detector array sensors for depth imaging
Our approach accurately generates realistic depth images in a wide range of scenarios, allowing the performance of an optical depth imaging system to be established without the need for costly and laborious field testing.
Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic Waters
Pico-nanophytoplankton organisms are dominant in oceanic oligotrophic areas but their adaptive growth rates make their contribution to the carbon cycle difficult to estimate.
