ArticleJournal of the American Medical Informatics Association : JAMIA2021
Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. 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, 50 citations in OpenAlex.
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Real-time automated billing for tobacco treatment: performance evaluation of the CigStopper machine learning framework.JAMIA open · 2026Article
- Temporal Annotation of German Clinical Language in Real and Synthetic Clinical Documents: Corpus Development and Baseline Tagger Validation Study.Journal of medical Internet research · 2026Article
- Deep learning in the early diagnosis of acute aortic dissection.Frontiers in cardiovascular medicine · 2026Review
- A Markov Chain Replacement Strategy for Surrogate Identifiers: Minimizing Re-Identification Risk While Preserving Text Reuse.Electronics · 2025Article
- Enhancing and Not Replacing Clinical Expertise: Improving Named-Entity Recognition in Colonoscopy Reports Through Mixed Real-Synthetic Training Sources.Journal of personalized medicine · 2025Article
- Review
- Evaluating GPT models for clinical note de-identification.Scientific reports · 2025Article
- Leveraging reddit data for context-enhanced synthetic health data generation to identify low self esteem.Frontiers in psychiatry · 2025Article
- Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data.Frontiers in artificial intelligence · 2025Article
- De-identification is not enough: a comparison between de-identified and synthetic clinical notes.Scientific reports · 2024Article
- Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study.JMIR formative research · 2024Article
- Dataset of miRNA-disease relations extracted from textual data using transformer-based neural networks.Database : the journal of biological databases and curation · 2024Article
- End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility.BMC medical informatics and decision making · 2024Article
- FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.Journal of biomedical informatics · 2024Article
- Large language models to identify social determinants of health in electronic health records.NPJ digital medicine · 2024Article
- Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2023Article
- A study of generative large language model for medical research and healthcare.NPJ digital medicine · 2023Article
- Leveraging the potential of synthetic text for AI in mental healthcare.Frontiers in digital health · 2022Article
- Privacy Protection and Secondary Use of Health Data: Strategies and Methods.BioMed research international · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
objective: Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks. MATERIALS AND
methods: We implemented 4 state-of-the-art text generation models, namely CharRNN, SegGAN, GPT-2, and CTRL, to generate clinical text for the History and Present Illness section. We then manually annotated clinical entities for randomly selected 500 History and Present Illness notes generated from the best-performing algorithm. To compare the utility of natural and synthetic corpora, we trained named entity recognition (NER) models from all 3 corpora and evaluated their performance on 2 independent natural corpora.
results: Our evaluation shows GPT-2 achieved the best BLEU (bilingual evaluation understudy) score (with a BLEU-2 of 0.92). NER models trained on synthetic corpus generated by GPT-2 showed slightly better performance on 2 independent corpora: strict F1 scores of 0.709 and 0.748, respectively, when compared with the NER models trained on natural corpus (F1 scores of 0.706 and 0.737, respectively), indicating the good utility of synthetic corpora in clinical NER model development. In addition, we also demonstrated that an augmented method that combines both natural and synthetic corpora achieved better performance than that uses the natural corpus only.
conclusions: Recent advances in text generation have made it possible to generate synthetic clinical notes that could be useful for training NER models for information extraction from natural clinical notes, thus lowering the privacy concern and increasing data availability. Further investigation is needed to apply this technology to practice.
Indexed as
Identifiers
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.