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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 41–50 of 68 papers

TitleStatusHype
PICO: Primitive Imitation for COntrol—0
PICO: Reconstructing 3D People In Contact with Objects—0
PICO: Secure Transformers via Robust Prompt Isolation and Cybersecurity Oversight—0
Pixel-level Correspondence for Self-Supervised Learning from Video—0
Programmable Turbine Failsafe System for Pico-Hydroelectric Power in the Nepal Himalayas—0
Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective—0
Requirements Engineering for Older Adult Digital Health Software: A Systematic Literature Review—0
Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition—0
Synthetic CT image generation from CBCT: A Systematic Review—0
Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction—0
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