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| title: SNOMED-Entity-Linking | |
| emoji: ⚕️ | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 4.42.0 | |
| app_file: app.py | |
| pinned: false | |
| # SNOMED-Entity-Linking | |
| A [Gradio](https://www.gradio.app/) app for Entity linking on the [SNOMED CT](https://www.snomed.org/five-step-briefing), a knowledge graph of clinical healthcare terminology. | |
|  | |
| ## Motivation | |
| Much of the world's healthcare data is stored in free-text documents, usually clinical notes taken by doctors. This unstructured data can be challenging to analyze and extract meaningful insights from. | |
| However, by applying a standardized terminology like SNOMED CT, we can the interpretability of these notes for patients and individuals outside the organization of origin. | |
| Moreover, healthcare organizations can convert this free-text data into a structured format that can be readily analyzed by computers, in turn stimulating the development of new medicines, treatment pathways, and better patient outcomes. | |
| Here, we use entity linking to analyze clinical notes identifing and labeling the portions of each note that correspond to specific medical concepts. | |
| # Methodology | |
| The pipline involves two models, one for segmentation and the other for disambiguation (classification of the segmentations). | |
| The segmentation model is a [CANINE-s](https://huggingface.co/google/canine-s) character-level transformer model finetuned to optimise the BCE, Dice, and Focal loss each weighted 1, 1, .1 respectively. The objective function is then optimised using Adam with a learning rate of 1e-5. | |
| The classification model uses the [BioBERT](https://huggingface.co/dmis-lab/biosyn-biobert-bc5cdr-disease) model. Here, the model is trained similarly using Adan and a learning rate of 2e-5. We train using the [MultipleNegativesRankingLoss](https://arxiv.org/pdf/1705.00652) using the [SentenceTransformers](https://sbert.net/) library. | |
| ## Dataset | |
| The dataset used to train the models is the dataset used for the [SNOMED CT Entity Linking Challenge](https://physionet.org/content/snomed-ct-entity-challenge/1.0.0/), which is a subset of [MIMIC-IV-Note](https://physionet.org/content/mimic-iv-note/2.2/) of 75,000 entity annotations across about 300 discharge notes. | |
| For the sake of simplicity we only include entities with more than 10 mentions. | |
| ## References | |
| - Hardman, W., Banks, M., Davidson, R., Truran, D., Ayuningtyas, N. W., Ngo, H., Johnson, A., & Pollard, T. (2023). SNOMED CT Entity Linking Challenge (version 1.0.0). PhysioNet. https://doi.org/10.13026/s48e-sp45. | |
| - Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. | |
| - Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, Jaewoo Kang, BioBERT: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, Volume 36, Issue 4, February 2020, Pages 1234–1240, https://doi.org/10.1093/bioinformatics/btz682 | |
| - Henderson, M., Al-Rfou, R., Strope, B., Sung, Y., Lukács, L., Guo, R., Kumar, S., Miklos, B., & Kurzweil, R. (2017). Efficient Natural Language Response Suggestion for Smart Reply. ArXiv, abs/1705.00652. | |
| - Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Conference on Empirical Methods in Natural Language Processing. | |