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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 2650 of 68 papers

TitleStatusHype
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications0
Programmable Turbine Failsafe System for Pico-Hydroelectric Power in the Nepal Himalayas0
Novel 3D Geometry-Based Stochastic Models for Non-Isotropic MIMO Vehicle-to-Vehicle Channels0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework0
Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review AutomationCode0
Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic WatersCode0
A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices0
3D Scene Inference from Transient Histograms0
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation0
DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems0
Pre-trained language models with domain knowledge for biomedical extractive summarizationCode0
Pixel-level Correspondence for Self-Supervised Learning from Video0
Low-complexity Three-dimensional Discrete Hartley Transform Approximations for Medical Image Compression0
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials LiteratureCode0
Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction0
Addressing Gap between Training Data and Deployed Environment by On-Device LearningCode0
Truth Discovery in Sequence Labels from CrowdsCode0
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span AnnotationsCode0
Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey0
Impact of detecting clinical trial elements in exploration of COVID-19 literature0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node0
PICO: Primitive Imitation for COntrol0
Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification0
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO AnnotationCode0
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