Author:
Segura-Bedmar Isabel,Camino-Perdones David,Guerrero-Aspizua Sara
Abstract
AbstractBackground and objectiveAlthough 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.MethodsThe 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).ResultsBioBERT, 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%.ConclusionsWhile our results are promising, there is still much room for improvement, especially with respect to the identification of clinical manifestations (signs and symptoms).
Funder
Ministerio de Ciencia e Innovación
Comunidad de Madrid
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference67 articles.
1. Paz MP, Villaverde-Hueso A, Alonso V, János S, Zurriaga Ó, Pollán M, Abaitua-Borda I. Rare diseases epidemiology research. Rare Dis Epidemiol. 2010;17–39.
2. Klimova B, Storek M, Valis M, Kuca K. Global view on rare diseases: a mini review. Curr Med Chem. 2017;24(29):3153–8.
3. Ferreira CR. The burden of rare diseases. Am J Med Genet A. 2019;179(6):885–92.
4. Zurynski Y, Deverell M, Dalkeith T, Johnson S, Christodoulou J, Leonard H, Elliott EJ. Australian children living with rare diseases: experiences of diagnosis and perceived consequences of diagnostic delays. Orphanet J Rare Dis. 2017;12(1):1–9.
5. Ts M, Jordanova R, Iskrov G, Stefanov R. General knowledge and awareness on rare diseases among general practitioners in Bulgaria. Georgian Med News. 2011;193:16–9.
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献