Cargando…
Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning
Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036927/ https://www.ncbi.nlm.nih.gov/pubmed/32050435 http://dx.doi.org/10.3390/ijerph17031093 |
_version_ | 1783500308022820864 |
---|---|
author | López Seguí, Francesc Ander Egg Aguilar, Ricardo de Maeztu, Gabriel García-Altés, Anna García Cuyàs, Francesc Walsh, Sandra Sagarra Castro, Marta Vidal-Alaball, Josep |
author_facet | López Seguí, Francesc Ander Egg Aguilar, Ricardo de Maeztu, Gabriel García-Altés, Anna García Cuyàs, Francesc Walsh, Sandra Sagarra Castro, Marta Vidal-Alaball, Josep |
author_sort | López Seguí, Francesc |
collection | PubMed |
description | Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables “avoiding the need of a face-to-face visit” and “increased demand” (precision = 0.98 and 0.97, respectively) rather than the variable “type of query” (precision = 0.48). Conclusion: To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals. |
format | Online Article Text |
id | pubmed-7036927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70369272020-03-11 Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning López Seguí, Francesc Ander Egg Aguilar, Ricardo de Maeztu, Gabriel García-Altés, Anna García Cuyàs, Francesc Walsh, Sandra Sagarra Castro, Marta Vidal-Alaball, Josep Int J Environ Res Public Health Article Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables “avoiding the need of a face-to-face visit” and “increased demand” (precision = 0.98 and 0.97, respectively) rather than the variable “type of query” (precision = 0.48). Conclusion: To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals. MDPI 2020-02-09 2020-02 /pmc/articles/PMC7036927/ /pubmed/32050435 http://dx.doi.org/10.3390/ijerph17031093 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article López Seguí, Francesc Ander Egg Aguilar, Ricardo de Maeztu, Gabriel García-Altés, Anna García Cuyàs, Francesc Walsh, Sandra Sagarra Castro, Marta Vidal-Alaball, Josep Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning |
title | Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning |
title_full | Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning |
title_fullStr | Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning |
title_full_unstemmed | Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning |
title_short | Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning |
title_sort | teleconsultations between patients and healthcare professionals in primary care in catalonia: the evaluation of text classification algorithms using supervised machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036927/ https://www.ncbi.nlm.nih.gov/pubmed/32050435 http://dx.doi.org/10.3390/ijerph17031093 |
work_keys_str_mv | AT lopezseguifrancesc teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning AT andereggaguilarricardo teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning AT demaeztugabriel teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning AT garciaaltesanna teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning AT garciacuyasfrancesc teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning AT walshsandra teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning AT sagarracastromarta teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning AT vidalalaballjosep teleconsultationsbetweenpatientsandhealthcareprofessionalsinprimarycareincataloniatheevaluationoftextclassificationalgorithmsusingsupervisedmachinelearning |