https://github.com/Alibaba-NLP/EBM-Net
공식 코드Predicting Clinical Trial Results by Implicit Evidence Integration
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Predicting Clinical Trial Results by Implicit Evidence Integration
Simulating single-photon detector array sensors for depth imaging
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
PiCO: Peer Review in LLMs based on the Consistency Optimization
Towards Effective Visual Representations for Partial-Label Learning
Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review Automation
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networks
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
Multi-Agent Path Finding with Prioritized Communication Learning
Addressing Gap between Training Data and Deployed Environment by On-Device Learning
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.