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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 51–68 of 68 papers

TitleStatusHype
Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey—0
Automated monitoring of bee colony movement in the hive during winter season—0
Co-existence of Micro, Pico and Atto Cells in Optical Wireless Communication—0
Comparative study on supervised learning methods for identifying phytoplankton species—0
Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems—0
Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach—0
Cost-Effective Robotic Handwriting System with AI Integration—0
DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
Efficient Implicit Neural Compression of Point Clouds via Learnable Activation in Latent Space—0
Evaluation of large language model performance on the Biomedical Language Understanding and Reasoning Benchmark—0
Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study—0
Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification—0
HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations—0
Impact of detecting clinical trial elements in exploration of COVID-19 literature—0
Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction—0
Joint Routing and Resource Allocation for Millimeter Wave Picocellular Backhaul—0
Large language models streamline automated systematic review: A preliminary study—0
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