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3 results for “SNOMED CT”
Mapping of OrphaCodes to SNOMED CT with the help of Usagi
<p>The CSV file contains the mapping of OrphaCodes to SNOMED CT carried out with Usagi.<br>This file has not yet been quality assured, but will be made available for validation.</p>
Medical Concept Embeddings for SNOMED-CT (Jan 2019 version)
<p>This dataset contains the SNOMED-CT medical concept embeddings trained using the following text and graph embedding methods.</p> <ul> <li>Averaged Word Embedding (300)</li> <li>ELMo (1024)</li> <li>Universal Sentence Encoder (512)</li> <li>BERT (768)</li> <li>Deepwalk (128)</li> <li>Node2Vec (128)</li> <li>HARP (128)</li> <li>LINE (128)</li> </ul> <p>The tar file contains eight JSON files corresponding to the aforementioned embedding techniques. The number (in parenthesis) besides each embedding method represents the dimensionality of the embedding. Each JSON file contains a python dictionary of the form</p> <p>SNOMED concept ID (String): Embedding (List).</p> <p>If you find this resource useful in your research, please consider citing our paper:</p> <p>"Pattisapu, N., Patil, S., Palshikar, G. and Varma, V., Medical Concept Normalization by Encoding Target Knowledge, Proceedings of Machine Learning Research 116:246–259, 2020 Machine Learning for Health (ML4H) at NeurIPS 2019"</p> <p>Warning: The dataset size is large (~12 GB). Please ensure that you have sufficient network bandwidth and disk space before requesting a download.</p>
FastText embeddings obtained from SNOMED CT - Spanish and International
<p>FastText model trained in a corpus that was obtained from SNOMED CT by performing random walks. There are three models: one trained using all relations available in the international version of SNOMED CT, another one trained using only is_a relations from the international version of SNOMED and the last one is trained using all relations from the Spanish version of SNOMED CT. This was developed for the end project of the Master Degree in Artificial Intelligence in the Universidad Politécnica de Madrid. More information and the document of the project can be found in <a href="https://github.com/JavierCastellD/SemanticFormalizationSNOMED">https://github.com/JavierCastellD/SemanticFormalizationSNOMED</a>.</p>
ScienceDex guides
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