Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts
Abstract Background and objective Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to iden...
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04810-y |
| _version_ | 1811224087818665984 |
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| author | Isabel Segura-Bedmar David Camino-Perdones Sara Guerrero-Aspizua |
| author_facet | Isabel Segura-Bedmar David Camino-Perdones Sara Guerrero-Aspizua |
| author_sort | Isabel Segura-Bedmar |
| collection | DOAJ |
| description | Abstract Background and objective Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to identify them. In addition to this, rare diseases usually show a wide variety of manifestations, which might make the diagnosis even more difficult. A delayed diagnosis can negatively affect the patient’s life. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) and Deep Learning can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments. Methods The paper explores several deep learning techniques such as Bidirectional Long Short Term Memory (BiLSTM) networks or deep contextualized word representations based on Bidirectional Encoder Representations from Transformers (BERT) to recognize rare diseases and their clinical manifestations (signs and symptoms). Results BioBERT, a domain-specific language representation based on BERT and trained on biomedical corpora, obtains the best results with an F1 of 85.2% for rare diseases. Since many signs are usually described by complex noun phrases that involve the use of use of overlapped, nested and discontinuous entities, the model provides lower results with an F1 of 57.2%. Conclusions While our results are promising, there is still much room for improvement, especially with respect to the identification of clinical manifestations (signs and symptoms). |
| first_indexed | 2024-04-12T08:43:00Z |
| format | Article |
| id | doaj.art-a7b7b5146fc24c1ea630b534ee63ea2f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T08:43:00Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a7b7b5146fc24c1ea630b534ee63ea2f2022-12-22T03:39:47ZengBMCBMC Bioinformatics1471-21052022-07-0123112310.1186/s12859-022-04810-yExploring deep learning methods for recognizing rare diseases and their clinical manifestations from textsIsabel Segura-Bedmar0David Camino-Perdones1Sara Guerrero-Aspizua2Human Language and Accesibility Technologies, Computer Science Department, Universidad Carlos III de MadridHuman Language and Accesibility Technologies, Computer Science Department, Universidad Carlos III de MadridTissue Engineering and Regenerative Medicine group, Department of Bioengineering, Universidad Carlos III de MadridAbstract Background and objective Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to identify them. In addition to this, rare diseases usually show a wide variety of manifestations, which might make the diagnosis even more difficult. A delayed diagnosis can negatively affect the patient’s life. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) and Deep Learning can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments. Methods The paper explores several deep learning techniques such as Bidirectional Long Short Term Memory (BiLSTM) networks or deep contextualized word representations based on Bidirectional Encoder Representations from Transformers (BERT) to recognize rare diseases and their clinical manifestations (signs and symptoms). Results BioBERT, a domain-specific language representation based on BERT and trained on biomedical corpora, obtains the best results with an F1 of 85.2% for rare diseases. Since many signs are usually described by complex noun phrases that involve the use of use of overlapped, nested and discontinuous entities, the model provides lower results with an F1 of 57.2%. Conclusions While our results are promising, there is still much room for improvement, especially with respect to the identification of clinical manifestations (signs and symptoms).https://doi.org/10.1186/s12859-022-04810-yRare diseasesNamed entity recognitionDeep learning |
| spellingShingle | Isabel Segura-Bedmar David Camino-Perdones Sara Guerrero-Aspizua Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts BMC Bioinformatics Rare diseases Named entity recognition Deep learning |
| title | Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts |
| title_full | Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts |
| title_fullStr | Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts |
| title_full_unstemmed | Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts |
| title_short | Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts |
| title_sort | exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts |
| topic | Rare diseases Named entity recognition Deep learning |
| url | https://doi.org/10.1186/s12859-022-04810-y |
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