Next Article in Journal
Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage
Previous Article in Journal
Towards Improved Classification Accuracy on Highly Imbalanced Text Dataset Using Deep Neural Language Models
Previous Article in Special Issue
Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques
Article

Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

1
Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
2
Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain
3
Hospital Universitario Puerta de Hierro, 28222 Majadahonda, Madrid, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(2), 865; https://doi.org/10.3390/app11020865
Received: 18 December 2020 / Revised: 11 January 2021 / Accepted: 12 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Integration and Mining of Data from Mobile Devices)
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. View Full-Text
Keywords: Natural Language Processing (NLP); information extraction; diagnosis extraction; lung cancer; deep learning; speculation detection; negation detection Natural Language Processing (NLP); information extraction; diagnosis extraction; lung cancer; deep learning; speculation detection; negation detection
Show Figures

Figure 1

MDPI and ACS Style

Solarte Pabón, O.; Torrente, M.; Provencio, M.; Rodríguez-Gonzalez, A.; Menasalvas, E. Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes. Appl. Sci. 2021, 11, 865. https://doi.org/10.3390/app11020865

AMA Style

Solarte Pabón O, Torrente M, Provencio M, Rodríguez-Gonzalez A, Menasalvas E. Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes. Applied Sciences. 2021; 11(2):865. https://doi.org/10.3390/app11020865

Chicago/Turabian Style

Solarte Pabón, Oswaldo, Maria Torrente, Mariano Provencio, Alejandro Rodríguez-Gonzalez, and Ernestina Menasalvas. 2021. "Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes" Applied Sciences 11, no. 2: 865. https://doi.org/10.3390/app11020865

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop