Natural Language Processing in Electronic Health Records

Natural Language Processing (NLP) has emerged as a critical discipline for unlocking the rich, unstructured narratives contained within electronic health records (EHRs). By applying computational linguistics, machine learning and increasingly deep learning architectures to clinical notes, discharge summaries and pathology reports, researchers can extract diagnostic entities, temporal events and semantic relationships that elude structured coding systems. These techniques address key challenges in secondary use of healthcare data, including case identification, phenotyping, cohort discovery and outcome prediction. Rule-based approaches, once dominant, have given way to hybrid pipelines that combine lexicons and heuristics with statistical classifiers. More recently, transformer-based models pretrained on large corpora have demonstrated significant gains in disease mention recognition and relation extraction, even under few-shot conditions. Across global healthcare settings, NLP in EHRs underpins precision medicine initiatives, supports clinical decision-support tools and enables large-scale observational studies. As methods become more robust and de-identification protocols more rigorous, NLP is poised to transform routine care by facilitating real-time surveillance, automated coding and richer clinical analytics at population scale.

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Natural Language Processing in Electronic Health Records publication trend

The graph below shows the total number of articles in natural language processing in electronic health records across all publications each year (not limited to Nature Index journals).

Technical terms

Electronic Health Record (EHR): A digital repository of patient health information that includes structured fields and unstructured narrative text generated during routine care.

Natural Language Processing (NLP): The interdisciplinary field combining linguistics, computer science and machine learning to analyse and generate human language content.

Information Extraction (IE): The automated process of identifying and structuring specific data elements—such as diagnoses or medications—from unstructured text.

Named Entity Recognition (NER): An NLP task that locates and classifies entities (for example, diseases, treatments or patient attributes) within a text corpus.

Relation Extraction: The identification of semantic relationships between entities, such as drug–disease associations or temporal sequences of clinical events.

Transformer-based models: Deep learning architectures that employ self-attention mechanisms to capture long-range dependencies in text, facilitating state-of-the-art performance on various NLP tasks.

References

1. Multi-task learning for few-shot biomedical relation extraction. Artificial Intelligence Review (2023).
2. Extracting information from the text of electronic medical records to improve case detection: a systematic review. Journal of the American Medical Informatics Association (2016).
3. Supporting information retrieval from electronic health records: A report of University of Michigan’s nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE). Journal of Biomedical Informatics (2015).
4. Clinical information extraction applications: A literature review. Journal of Biomedical Informatics (2017).