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PICO

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.

Papers

Showing 125 of 68 papers

TitleStatusHype
PiCO+: Contrastive Label Disambiguation for Robust Partial Label LearningCode2
LinkBERT: Pretraining Language Models with Document LinksCode2
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical EvidenceCode1
Predicting Clinical Trial Results by Implicit Evidence IntegrationCode1
PiCO: Peer Review in LLMs based on the Consistency OptimizationCode1
Towards Effective Visual Representations for Partial-Label LearningCode1
Multi-Agent Path Finding with Prioritized Communication LearningCode1
Simulating single-photon detector array sensors for depth imagingCode1
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
Contrastive Label Disambiguation for Partial Label LearningCode1
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node0
Automated monitoring of bee colony movement in the hive during winter season0
Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study0
Co-existence of Micro, Pico and Atto Cells in Optical Wireless Communication0
Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification0
HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations0
A Theory of Fermat Paths for Non-Line-Of-Sight Shape Reconstruction0
Cost-Effective Robotic Handwriting System with AI Integration0
A Study on Agreement in PICO Span Annotations0
3D Scene Inference from Transient Histograms0
Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach0
DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework0
Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey0
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications0
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