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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 5168 of 68 papers

TitleStatusHype
Joint Routing and Resource Allocation for Millimeter Wave Picocellular Backhaul0
Large language models streamline automated systematic review: A preliminary study0
Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic WatersCode0
AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMsCode0
Pre-trained language models with domain knowledge for biomedical extractive summarizationCode0
Advancing PICO Element Detection in Biomedical Text via Deep Neural NetworksCode0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
Computing High Accuracy Power Spectra with PicoCode0
Machine Learning in Downlink Coordinated Multipoint in Heterogeneous NetworksCode0
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical LiteratureCode0
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials LiteratureCode0
Truth Discovery in Sequence Labels from CrowdsCode0
Object Detection with Pixel Intensity Comparisons Organized in Decision TreesCode0
Addressing Gap between Training Data and Deployed Environment by On-Device LearningCode0
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO AnnotationCode0
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span AnnotationsCode0
Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review AutomationCode0
PICO Element Detection in Medical Text via Long Short-Term Memory Neural NetworksCode0
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