Asset Details
Do you wish to reserve the book?
Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program
Do you wish to request the book?
Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program
2025
Overview
COPD exacerbations have a profound clinical impact on patients. Accurately predicting these events could help healthcare professionals take proactive measures to mitigate their impact. For over a decade, telEPOC, a telehealthcare program, has collected data that can be utilized to train machine learning models to anticipate COPD exacerbations. The objective of this study is to develop a machine learning model that, based on a patient’s history, predicts the probability of an exacerbation event within the next 3 days. After cleaning and harmonizing the different subsets of data, we split the data along the temporal axis: one subset for model training, another for model selection, and another for model evaluation. We then trained a gradient tree boosting approach as well as neural network-based approaches. After conducting our analysis, we found that the CatBoost algorithm yielded the best results, with an area under the precision-recall curve of 0.53 and an area under the ROC curve of 0.91. Additionally, we assessed the significance of the input variables and discovered that breathing rate, heart rate, and SpO2 were the most informative. The resulting model can operate in a 50% recall and 50% precision regime, which we consider has the potential to be useful in daily practice.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
