ArticleBMC medical informatics and decision making2023
RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 12 citations in OpenAlex.
- Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges.Journal of assisted reproduction and genetics · 2025Review
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
- Construction and Application of a Traditional Chinese Medicine Syndrome Differentiation Model for Dysmenorrhea Based on Machine Learning.Combinatorial chemistry & high throughput screening · 2025Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
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9 authors at 2 institutions in 1 country.
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Abstract
backgroundThe ovarian reserve is a reservoir for reproductive potential. In clinical practice, early detection and treatment of premature ovarian decline characterized by abnormal ovarian reserve tests is regarded as a critical measure to prevent infertility. However, the relevant data are typically stored in an unstructured format in a hospital's electronic medical record (EMR) system, and their retrieval requires tedious manual abstraction by domain experts. Computational tools are therefore needed to reduce the workload.
methodsWe presented RegEMR, an artificial intelligence tool composed of a rule-based natural language processing (NLP) extractor and a knowledge-based disease scoring model, to automatize the screening procedure of premature ovarian decline using Chinese reproductive EMRs. We used regular expressions (REs) as a text mining method and explored whether REs automatically synthesized by the genetic programming-based online platform RegexGenerator + + could be as effective as manually formulated REs. We also investigated how the representativeness of the learning corpus affected the performance of machine-generated REs. Additionally, we translated the clinical diagnostic criteria into a programmable disease diagnostic model for disease scoring and risk stratification. Four hundred outpatient medical records were collected from a Chinese fertility center. Manual review served as the gold standard, and fivefold cross-validation was used for evaluation.
resultsThe overall F-score of manually built REs was 0.9444 (95% CI 0.9373 to 0.9515), with no significant difference (paired t test p > 0.05) compared with machine-generated REs that could be affected by training set sizes and annotation portions. The extractor performed effectively in automatically tracing the dynamic changes in hormone levels (F-score 0.9518-0.9884) and ultrasonographic measures (F-score 0.9472-0.9822). Applying the extracted information to the proposed diagnostic model, the program obtained an accuracy of 0.98 and a sensitivity of 0.93 in risk screening. For each specific disease, the automatic diagnosis in 76% of patients was consistent with that of the clinical diagnosis, and the kappa coefficient was 0.63.
conclusionA Chinese NLP system named RegEMR was developed to automatically identify high risk of early ovarian aging and diagnose related diseases from Chinese reproductive EMRs. We hope that this system can aid EMR-based data collection and clinical decision support in fertility centers.
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