ArticleJournal of medical Internet research2018
A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study.
Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing-Assisted Intelligent Systematic Literature Review System.JMIR medical informatics · 2024Article
- The global geography of artificial intelligence in life science research.Nature communications · 2024Article
- Boosting efficiency in a clinical literature surveillance system with LightGBM.PLOS digital health · 2024Article
- The McMaster Health Information Research Unit: Over a Quarter-Century of Health Informatics Supporting Evidence-Based Medicine.Journal of medical Internet research · 2024Article
- Automating Quality Assessment of Medical Evidence in Systematic Reviews: Model Development and Validation Study.Journal of medical Internet research · 2023Article
- Bat4RCT: A suite of benchmark data and baseline methods for text classification of randomized controlled trials.PloS one · 2023Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images.Multimedia tools and applications · 2022Article
- Preliminary comparison of the performance of the National Library of Medicine's systematic review publication type and the sensitive clinical queries filter for systematic reviews in PubMed.Journal of the Medical Library Association : JMLA · 2022Article
- The scientific publication score - a new tool for summarizing evidence and data quality criteria of biomedical publications.Journal of bone and joint infection · 2022Article
- A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation.JMIR research protocols · 2021Article
- Using neural networks to support high-quality evidence mapping.BMC bioinformatics · 2021Review
- Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review.JMIR medical informatics · 2021Review
- Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.Journal of medical Internet research · 2020Article
- Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study.JMIR medical informatics · 2019Article
- Improving reference prioritisation with PICO recognition.BMC medical informatics and decision making · 2019Article
- The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials.Contemporary clinical trials communications · 2019Article
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
- A High Recall Classifier for Selecting Articles for MEDLINE Indexing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Automatic identification of recent high impact clinical articles in PubMed to support clinical decision making using time-agnostic features.Journal of biomedical informatics · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundA major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic.
objectiveTo investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature.
methodsWe trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard.
resultsThe deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster's textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster's textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001).
conclusionsDeep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.