# Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

arXiv: https://arxiv.org/abs/1908.05780 · HTML: https://mlsanity.com/paper/1908.05780/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 30 of 36)

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- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
- [Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling](https://mlsanity.com/paper/2401.12994/) (2024)
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- [DSG-KD: Knowledge Distillation from Domain-Specific to General Language Models](https://mlsanity.com/paper/2409.14904/) (2024)
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- [Assessing mortality prediction through different representation models based on concepts extracted from clinical notes](https://mlsanity.com/paper/2207.10872/) (2022)
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- [FlashDP: Private Training Large Language Models with Efficient DP-SGD](https://mlsanity.com/paper/2507.01154/) (2025)
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- [Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature](https://mlsanity.com/paper/2111.13781/) (2021)
- [Automated Drug-Related Information Extraction from French Clinical Documents: ReLyfe Approach](https://mlsanity.com/paper/2112.11439/) (2021)
- [Applying unsupervised keyphrase methods on concepts extracted from discharge sheets](https://mlsanity.com/paper/2303.08928/) (2023)
- [Natural language processing on customer note data](https://mlsanity.com/paper/2305.02029/) (2023)
- [Validation of a Zero-Shot Learning Natural Language Processing Tool for Data Abstraction from Unstructured Healthcare Data](https://mlsanity.com/paper/2308.00107/) (2023)
- [Discovering influential text using convolutional neural networks](https://mlsanity.com/paper/2406.10086/) (2024)
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## Abstract (arXiv)

Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using ICD-10. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Further efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.