ArticleNPJ digital medicine2018
Natural language generation for electronic health records.
Article in NPJ digital medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study.JMIR medical informatics · 2026Article
- AI-powered Chatbot integration for enhanced accessibility of electronic health records in a pediatric hospital.Scientific reports · 2026Article
- SimSUM - simulated benchmark with structured and unstructured medical records.Journal of biomedical semantics · 2025Article
- Impact of Detailed Versus Generic Instructions on Fine-Tuned Language Models for Patient Discharge Instructions Generation: Comparative Statistical Analysis.JMIR formative research · 2025Article
- A review on generative AI models for synthetic medical text, time series, and longitudinal data.NPJ digital medicine · 2025Article
- Evaluation and Bias Analysis of Large Language Models in Generating Synthetic Electronic Health Records: Comparative Study.Journal of medical Internet research · 2025Article
- Open LLM-based actionable incidental finding extraction from [Frontiers in digital health · 2025Article
- Synthetic data generation: a privacy-preserving approach to accelerate rare disease research.Frontiers in digital health · 2025Article
- Synthetic4Health: generating annotated synthetic clinical letters.Frontiers in digital health · 2025Article
- Synthetic data generation methods in healthcare: A review on open-source tools and methods.Computational and structural biotechnology journal · 2024Review
- Neural Models for Generating Natural Language Summaries from Temporal Personal Health Data.Journal of healthcare informatics research · 2024Article
- AI-assisted literature exploration of innovative Chinese medicine formulas.Frontiers in pharmacology · 2024Article
- Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model.Nature communications · 2023Article
- ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model.International journal of oral science · 2023Review
- Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?PLOS digital health · 2022Article
- Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan.PLOS digital health · 2022Article
- ChartSweep: A HIPAA-compliant Tool to Automate Chart Review for Plastic Surgery Research.Plastic and reconstructive surgery. Global open · 2021Article
- Artificial intelligence in child abuse imaging.Pediatric radiology · 2021Review
- The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructured Data in Medical Records Through Natural Language Processing.Frontiers in digital health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
One broad goal of biomedical informatics is to generate fully-synthetic, faithfully representative electronic health records (EHRs) to facilitate data sharing between healthcare providers and researchers and promote methodological research. A variety of methods existing for generating synthetic EHRs, but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness, or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that features in many contemporary machine translation systems, to generate synthetic chief complaints from discrete variables in EHRs, like age group, gender, and discharge diagnosis. After being trained end-to-end on authentic records, the model can generate realistic chief complaint text that appears to preserve the epidemiological information encoded in the original record-sentence pairs. As a side effect of the model's optimization goal, these synthetic chief complaints are also free of relatively uncommon abbreviation and misspellings, and they include none of the personally identifiable information (PII) that was in the training data, suggesting that this model may be used to support the de-identification of text in EHRs. When combined with algorithms like generative adversarial networks (GANs), our model could be used to generate fully-synthetic EHRs, allowing healthcare providers to share faithful representations of multimodal medical data without compromising patient privacy. This is an important advance that we hope will facilitate the development of machine-learning methods for clinical decision support, disease surveillance, and other data-hungry applications in biomedical informatics.
Identifiers
30687797PMC6345174What 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.