PICO
24 papers with code • 1 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in PICO
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Most implemented papers
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
We describe a method for visual object detection based on an ensemble of optimized decision trees organized in a cascade of rejectors.
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
We present a corpus of 5, 000 richly annotated abstracts of medical articles describing clinical randomized controlled trials.
PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks
Successful evidence-based medicine (EBM) applications rely on answering clinical questions by analyzing large medical literature databases.
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation
The network is then fine-tuned on a combination of real and these newly constructed artificial labeled instances.
Computing High Accuracy Power Spectra with Pico
This paper presents the second release of Pico (Parameters for the Impatient COsmologist).
Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networks
We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks.
Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks
jind11/Deep-PICO-Detection
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One is the PubMed-PICO dataset, where our best results outperform the previous best by 5. 5%, 7. 9%, and 5. 8% for P, I, and O elements in terms of F1 score, respectively.
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
bionlu-coling2024/biomed-ner-intent_detection
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In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
Predicting Clinical Trial Results by Implicit Evidence Integration
Alibaba-NLP/EBM-Net
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EMNLP 2020
In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i. e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population.
