Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies
Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502863/ https://www.ncbi.nlm.nih.gov/pubmed/36143132 http://dx.doi.org/10.3390/jcm11185484 |
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author | Cascella, Marco Coluccia, Sergio Monaco, Federica Schiavo, Daniela Nocerino, Davide Grizzuti, Mariacinzia Romano, Maria Cristina Cuomo, Arturo |
author_facet | Cascella, Marco Coluccia, Sergio Monaco, Federica Schiavo, Daniela Nocerino, Davide Grizzuti, Mariacinzia Romano, Maria Cristina Cuomo, Arturo |
author_sort | Cascella, Marco |
collection | PubMed |
description | Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO–RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation. |
format | Online Article Text |
id | pubmed-9502863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95028632022-09-24 Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies Cascella, Marco Coluccia, Sergio Monaco, Federica Schiavo, Daniela Nocerino, Davide Grizzuti, Mariacinzia Romano, Maria Cristina Cuomo, Arturo J Clin Med Article Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO–RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation. MDPI 2022-09-19 /pmc/articles/PMC9502863/ /pubmed/36143132 http://dx.doi.org/10.3390/jcm11185484 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cascella, Marco Coluccia, Sergio Monaco, Federica Schiavo, Daniela Nocerino, Davide Grizzuti, Mariacinzia Romano, Maria Cristina Cuomo, Arturo Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies |
title | Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies |
title_full | Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies |
title_fullStr | Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies |
title_full_unstemmed | Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies |
title_short | Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies |
title_sort | different machine learning approaches for implementing telehealth-based cancer pain management strategies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502863/ https://www.ncbi.nlm.nih.gov/pubmed/36143132 http://dx.doi.org/10.3390/jcm11185484 |
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