The RareDis corpus: A corpus annotated with rare diseases, their signs and symptoms

Miguel, Claudia Martínez-de; Segura-Bedmar, Isabel; Chacón-Solano, Esteban; Guerrero-Aspizúa, Sara

doi:10.1016/j.jbi.2021.103961

Cited by 15 publications

(20 citation statements)

References 34 publications

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“…Similar phenotypic features may be recorded in a heterogeneous manner by biomedical annotators using the same source documents. This inter- and intra-observer variability is a well-known phenomenon ( 17 , 18 ). However, the issue of clinical phenotype heterogeneity across disease models is less well studied.…”

Section: Resultsmentioning

confidence: 99%

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders

et al. 2022

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There are >2500 different genetically determined developmental disorders (DD), which, as a group, show very high levels of both locus and allelic heterogeneity. This has led to the wide-spread use of evidence-based filtering of genome-wide sequence data as a diagnostic tool in DD. Determining whether the association of a filtered variant at a specific locus is a plausible explanation of the phenotype in the proband is crucial and commonly requires extensive manual literature review by both clinical scientists and clinicians. Access to a database of weighted clinical features extracted from rigorously curated literature would increase the efficiency of this process and facilitate the development of robust phenotypic similarity metrics. However, given the large and rapidly increasing volume of published information, conventional biocuration approaches are becoming impractical. Here, we present a scalable, automated method for the extraction of categorical phenotypic descriptors from the full-text literature. Papers identified through literature review were downloaded and parsed using the Cadmus custom retrieval package. Human Phenotype Ontology terms were extracted using MetaMap, with 76–84% precision and 65–73% recall. Mean terms per paper increased from 9 in title + abstract, to 68 using full text. We demonstrate that these literature-derived disease models plausibly reflect true disease expressivity more accurately than widely used manually curated models, through comparison with prospectively gathered data from the Deciphering Developmental Disorders study. The area under the curve for receiver operating characteristic (ROC) curves increased by 5–10% through the use of literature-derived models. This work shows that scalable automated literature curation increases performance and adds weight to the need for this strategy to be integrated into informatic variant analysis pipelines. Database URL: https://doi.org/10.1093/database/baac038

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Section: Resultsmentioning

confidence: 99%

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders

et al. 2022

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“…Additionally, since the corpus might be unbalanced, as the number of tokens tagged "O" is much higher than other labels, Kappa would be high and overestimates IAA. On the other hand, ignoring the "O" label yields low Kappa scores (Hripcsak and Rothschild, 2005;Brandsen et al, 2020;Campillos-Llanos et al, 2021;Martínez-deMiguel et al, 2022). Instead, they recommended to use the F1score, as it might be more appropriate in reflecting the IAA in NER tasks.…”

Section: Inter-annotator Agreementmentioning

confidence: 99%

Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT

Jarrar¹,

Khalilia²,

Ghanem³

2022

Preprint

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This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person, organization, location, event and date. More importantly, the corpus is annotated with nested entities instead of the more common flat annotations. The data contains about 75K entities and 22.5% of which are nested. The inter-annotator evaluation of the corpus demonstrated a strong agreement with Cohen's Kappa of 0.979 and an F1-score of 0.976. To validate our data, we used the corpus to train a nested NER model based on multi-task learning using the pre-trained AraBERT (Arabic BERT). The model achieved an overall micro F1-score of 0.884. Our corpus, the annotation guidelines, the source code and the pre-trained model are publicly available.

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“…We use the RareDis corpus [41], which is a collection of texts from the Rare Disease database (NORD) [1] . These texts were manually annotated with four entity types (diseases, rare diseases, signs, and symptoms).…”

Section: Datasetmentioning

confidence: 99%

“…Table 1 shows the number of the entity types annotated, as well as the number of documents, sentences, and tokens in each split. A more detailed description of the RareDis corpus can be found in [41]. The corpus contains a total of 9,318 entities.…”

Section: Datasetmentioning

confidence: 99%

Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts

Segura-Bedmar

Camino-Perdones

Guerrero-Aspizúa

2022

Preprint

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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).

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The RareDis corpus: A corpus annotated with rare diseases, their signs and symptoms

Cited by 15 publications

References 34 publications

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders

Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT

Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts

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