Tashkovska Matea, Krsteski Stefan, Kizhevska Emilija, Valič Jakob, Gjoreski Hristijan, Luštrek Mitja
Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia.
Faculty of Electrical Engineering and Information Technologies, Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia.
PLoS One. 2024 Dec 4;19(12):e0313815. doi: 10.1371/journal.pone.0313815. eCollection 2024.
Congestive heart failure (CHF) is an incurable disease where a key objective of the treatment is to maintain the patient's quality of life (QoL) as much as possible. A model that predicts health-related QoL (HRQoL) based on physiological and ambient parameters can be used to monitor these parameters for the patient's benefit. Since it is difficult to predict how CHF progresses, in this study we tried to predict HRQoL for a particular patient as an individual, using two different datasets, collected while telemonitoring CHF patients. We used different types of imputation, classification models, number of classes and evaluation techniques for both datasets, but the main focus is on unifying the datasets, which allowed us to build cross-dataset models. The results showed that using general predictive models intended for previously unseen patients do not work well. Personalization significantly improves the prediction, both personalized models and personalized imputation, which is important due to many missing data in the datasets. However, this implies that applications using such predictive models would also need to collect some self-reported labels of HRQoL to be able to help patients effectively.
充血性心力衰竭（CHF）是一种无法治愈的疾病，治疗的一个关键目标是尽可能维持患者的生活质量（QoL）。基于生理和环境参数预测健康相关生活质量（HRQoL）的模型可用于监测这些参数，以造福患者。由于很难预测CHF的进展情况，在本研究中，我们尝试使用在远程监测CHF患者时收集的两个不同数据集，针对特定患者个体预测HRQoL。我们对两个数据集使用了不同类型的插补、分类模型、类别数量和评估技术，但主要重点是统一数据集，这使我们能够构建跨数据集模型。结果表明，使用针对之前未见过的患者的通用预测模型效果不佳。个性化显著提高了预测效果，包括个性化模型和个性化插补，这一点很重要，因为数据集中存在许多缺失数据。然而，这意味着使用此类预测模型的应用程序也需要收集一些HRQoL的自我报告标签，以便能够有效地帮助患者。
