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Integrating speculation detection and deep learning to extract lung cancer diagnosis from clinical notes
- Source :
- Biblos-e Archivo. Repositorio Institucional de la UAM, Universitat Autònoma de Barcelona, Applied Sciences, Vol 11, Iss 865, p 865 (2021), Applied Sciences, Volume 11, Issue 2
- Publication Year :
- 2021
- Publisher :
- MDPI, 2021.
-
Abstract
- Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient’s natural history. In this paper, we concentrate on extracting from clinical narratives the cancer diagnosis and relating it to the diagnosis date. To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed. The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date. In particular, we apply the proposed approach to extract the lung cancer diagnosis and its diagnosis date from clinical narratives written in Spanish. Results obtained show an F-score of 90% in the named entity recognition task, and a 89% F-score in the task of relating the cancer diagnosis to the diagnosis date. Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes<br />This work is supported by the EU Horizon 2020 innovation program under grant agreement No. 780495, project BigMedilytics (Big Data for Medical Analytics). It has been also supported by Fundación AECC and Instituto de Salud Carlos III (grant AC19/00034), under the frame of ERA-NET PerMed
- Subjects :
- Computer science
computer.software_genre
lcsh:Technology
Task (project management)
lcsh:Chemistry
0302 clinical medicine
Negation
Negation detection
General Materials Science
030212 general & internal medicine
information extraction
Instrumentation
lcsh:QH301-705.5
media_common
Fluid Flow and Transfer Processes
Speculation detection
Diagnosis extraction
General Engineering
Ambiguity
lcsh:QC1-999
3. Good health
Computer Science Applications
Information extraction
negation detection
030220 oncology & carcinogenesis
Lung cancer
Natural language processing
Medicina
media_common.quotation_subject
Natural Language Processing (NLP)
diagnosis extraction
03 medical and health sciences
Named-entity recognition
medicine
Speculation
business.industry
lcsh:T
Process Chemistry and Technology
Deep learning
speculation detection
Cancer
deep learning
medicine.disease
lung cancer
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
computer
lcsh:Physics
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Biblos-e Archivo. Repositorio Institucional de la UAM, Universitat Autònoma de Barcelona, Applied Sciences, Vol 11, Iss 865, p 865 (2021), Applied Sciences, Volume 11, Issue 2
- Accession number :
- edsair.doi.dedup.....85eb654f76841518bef8d1b792e116ba