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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
Predicting Clinical Trial Results by Implicit Evidence IntegrationCode1
Simulating single-photon detector array sensors for depth imagingCode1
Towards Effective Visual Representations for Partial-Label LearningCode1
Multi-Agent Path Finding with Prioritized Communication LearningCode1
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical EvidenceCode1
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
PiCO: Peer Review in LLMs based on the Consistency OptimizationCode1
Contrastive Label Disambiguation for Partial Label LearningCode1
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span AnnotationsCode0
AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMsCode0
Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review AutomationCode0
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical LiteratureCode0
PICO Element Detection in Medical Text via Long Short-Term Memory Neural NetworksCode0
Pre-trained language models with domain knowledge for biomedical extractive summarizationCode0
Addressing Gap between Training Data and Deployed Environment by On-Device LearningCode0
Advancing PICO Element Detection in Biomedical Text via Deep Neural NetworksCode0
Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic WatersCode0
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO AnnotationCode0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
Object Detection with Pixel Intensity Comparisons Organized in Decision TreesCode0
Computing High Accuracy Power Spectra with PicoCode0
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials LiteratureCode0
Machine Learning in Downlink Coordinated Multipoint in Heterogeneous NetworksCode0
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