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Post-COVID-19 Condition Prediction in Hospitalised Cancer Patients: A Machine Learning-Based Approach Mahvash Mohammadi, Sara; Rumyantsev, Mikhail; Abdeeva, Elina; Baimukhambetova, Dina; Bobkova, Polina; El-Taravi, Yasmin; Pikuza, Maria; Trefilova, Anastasia; Zolotarev, Aleksandr; Andreeva, Margarita; Iakovleva, Ekaterina; Bulanov, Nikolay; Avdeev, Sergey; Pazukhina, Ekaterina; Zaikin, Alexey; Kapustina, Valentina; Fomin, Victor; Svistunov, Andrey A.; Timashev, Peter; Avdeenko, Nina; Ivanova, Yulia; Fedorova, Lyudmila; Kondrikova, Elena; Turina, Irina; Glybochko, Petr; Butnaru, Denis; Blyuss, Oleg; Munblit, Daniel; Sechenov StopCOVID Research Team

Abstract

Background: The COVID-19 pandemic has led to widespread long-term complications, known as post-COVID conditions (PCC), particularly affecting vulnerable populations such as cancer patients. This study aims to predict the incidence of PCC in hospitalised cancer patients using the data from a longitudinal cohort study conducted in four major university hospitals in Moscow, Russia. Methods: Clinical data have been collected during the acute phase and follow-ups at 6 and 12 months post-discharge. A total of 49 clinical features were evaluated, and machine learning classifiers including logistic regression, random forest, support vector machine (SVM), k-nearest neighbours (KNN), and neural network were applied to predict PCC. Results: Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. KNN demonstrated the highest predictive performance, with an AUC of 0.80, sensitivity of 0.73, and specificity of 0.69. Severe COVID-19 and pre-existing comorbidities were significant predictors of PCC. Conclusions: Machine learning models, particularly KNN, showed some promise in predicting PCC in cancer patients, offering the potential for early intervention and personalised care. These findings emphasise the importance of long-term monitoring for cancer patients recovering from COVID-19 to mitigate PCC impact.

Item Metadata

Title
Post-COVID-19 Condition Prediction in Hospitalised Cancer Patients: A Machine Learning-Based Approach
Creator
Mahvash Mohammadi, Sara; Rumyantsev, Mikhail; Abdeeva, Elina; Baimukhambetova, Dina; Bobkova, Polina; El-Taravi, Yasmin; Pikuza, Maria; Trefilova, Anastasia; Zolotarev, Aleksandr; Andreeva, Margarita; Iakovleva, Ekaterina; Bulanov, Nikolay; Avdeev, Sergey; Pazukhina, Ekaterina; Zaikin, Alexey; Kapustina, Valentina; Fomin, Victor; Svistunov, Andrey A.; Timashev, Peter; Avdeenko, Nina; Ivanova, Yulia; Fedorova, Lyudmila; Kondrikova, Elena; Turina, Irina; Glybochko, Petr; Butnaru, Denis; Blyuss, Oleg; Munblit, Daniel; Sechenov StopCOVID Research Team
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2025-02-18
Description
Background: The COVID-19 pandemic has led to widespread long-term complications, known as post-COVID conditions (PCC), particularly affecting vulnerable populations such as cancer patients. This study aims to predict the incidence of PCC in hospitalised cancer patients using the data from a longitudinal cohort study conducted in four major university hospitals in Moscow, Russia. Methods: Clinical data have been collected during the acute phase and follow-ups at 6 and 12 months post-discharge. A total of 49 clinical features were evaluated, and machine learning classifiers including logistic regression, random forest, support vector machine (SVM), k-nearest neighbours (KNN), and neural network were applied to predict PCC. Results: Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. KNN demonstrated the highest predictive performance, with an AUC of 0.80, sensitivity of 0.73, and specificity of 0.69. Severe COVID-19 and pre-existing comorbidities were significant predictors of PCC. Conclusions: Machine learning models, particularly KNN, showed some promise in predicting PCC in cancer patients, offering the potential for early intervention and personalised care. These findings emphasise the importance of long-term monitoring for cancer patients recovering from COVID-19 to mitigate PCC impact.
Subject
post-COVID conditions; cancer patients; machine learning classifiers; predictive modelling
Genre
Article
Type
Text
Language
eng
Date Available
2025-04-14
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0448398
URI
http://hdl.handle.net/2429/90653
Affiliation
Other UBC
Citation
Cancers 17 (4): 687 (2025)
Publisher DOI
10.3390/cancers17040687
Peer Review Status
Reviewed
Scholarly Level
Researcher
Rights URI
https://creativecommons.org/licenses/by/4.0/
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CC BY 4.0