Unsupervised entity and relation extraction from clinical records in Italian

Alicante, Anita; Corazza, Anna; Isgrò, Francesco; Silvestri, Stefano

doi:10.1016/j.compbiomed.2016.01.014

Cited by 45 publications

(23 citation statements)

References 18 publications

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“…Supervised systems based on machine learning have proven to be more efficient than rule-based and terminology-based systems for NER [3] . Research efforts have then been made to unify these methods in hybrid systems, in a purely unsupervised [4,5] or semi-supervised fashion [6][7][8] . Such approaches are motivated by the necessity to reduce the need for manually annotated examples in the case of a supervised system, or the need for handwritten rules by experts in the field of rule-based and ontological systems.…”

Section: Resultsmentioning

confidence: 99%

Terminologies augmented recurrent neural network model for clinical named entity recognition

Lerner

Paris

Tannier

2020

Journal of Biomedical Informatics

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ObjectiveWe aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. MethodsWe used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and an hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents . In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (drug name, sign or symptom, disease or disorder, diagnostic procedure or lab test and therapeutic procedure). We evaluated each NER systems using exact and partial match definition of F-measure for NER. ResultsThe APcNER contains 4,837 entities which took 28 hours to annotate, the inter-annotator agreement was acceptable for Drug name in exact match (85%) and acceptable for other entity types in non-exact match (>70%). For drug name recognition on both i2b2-2009 and APcNER, the biGRU-CRF performed better that the terminology-based system, with an exact-match F-measure of 91.1% versus 73% and 81.9% versus 75% respectively. Moreover, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 92.2% versus 91.1% (i2b2-2009) and 88.4% versus 81.9% (APcNER). On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69.5% in exact match, and 84.1% in non-exact match. ConclusionAPcNER is a French corpus for clinical-NER of five type of entities which covers a large variety of document types. Extending supervised model with terminology allowed for an easy performance gain, especially in low regimes of entities, and established near state of the art results on the i2b2-2009 corpus.

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Section: Resultsmentioning

confidence: 99%

Terminologies augmented recurrent neural network model for clinical named entity recognition

Lerner

Paris

Tannier

2020

Journal of Biomedical Informatics

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“…Lexicons, terminologies and annotated corpora While the lack of language specific resources is sometimes addressed by investigating unsupervised methods [ 46 , 47 ], many clinical NLP methods rely on language-specific resources. As a result, the creation of resources such as synonym or abbreviation lexicons [ 27 , 36 , 48 ] receives a lot of effort, as it serves as the basis for more advanced NLP and text mining work.…”

Section: Main Textmentioning

confidence: 99%

Clinical Natural Language Processing in languages other than English: opportunities and challenges

et al. 2018

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BackgroundNatural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area.Main BodyWe envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application.ConclusionWe show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.

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“…C_NER (Alicante et al, 2016) uses several traditional NLP tools to extract entities. It is an unsupervised approach that uses clustering to group entity pairs.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Qiu

Xie

Tao

2019

Earth and Space Science

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A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage this wealth of data in the geoscience literature for data analysis and further information extraction. Existing NER models and techniques are mainly based on rule‐based and supervised approaches, and developing such systems requires a costly manual effort. In this paper, we first design a generic stepwise framework for domain‐specific NER. Following this framework, domain‐specific entities and domain‐general words are collected and selected as seed terms. Normalization and grouping processes are then applied to these seed terms for further analysis. A random extraction algorithm based on a unigram language model is used to generate a large‐scale training data set consisting of probabilistically labeled pseudosentences. Each generated sentence is then used as input to the self‐training and learning algorithm. Experimental results on two constructed data sets demonstrate that the proposed model effectively recognizes and identifies geological named entities.

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Unsupervised entity and relation extraction from clinical records in Italian

Cited by 45 publications

References 18 publications

Terminologies augmented recurrent neural network model for clinical named entity recognition

Terminologies augmented recurrent neural network model for clinical named entity recognition

Clinical Natural Language Processing in languages other than English: opportunities and challenges

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

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