Pičulin Matej, Smole Tim, Žunkovič Bojan, Kokalj Enja, Robnik-Šikonja Marko, Kukar Matjaž, Fotiadis Dimitrios I, Pezoulas Vasileios C, Tachos Nikolaos S, Barlocco Fausto, Mazzarotto Francesco, Popović Dejana, Maier Lars S, Velicki Lazar, Olivotto Iacopo, MacGowan Guy A, Jakovljević Djordje G, Filipović Nenad, Bosnić Zoran
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.
JMIR Med Inform. 2022 Feb 2;10(2):e30483. doi: 10.2196/30483.
Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD).
Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period.
The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method.
The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R from 0.3 to 0.6.
By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.
心血管疾病总体上导致全球30%的死亡。其中，肥厚型心肌病（HCM）是一种遗传性心脏病，约每500名年轻人中就有1人患病，可导致心源性猝死（SCD）。
尽管目前的先进方法可以对患者的心源性猝死风险进行建模，但据我们所知，尚无方法可对患者未来10年的临床状况进行建模。在本文中，我们提出了一种基于机器学习（ML）的新型工具，用于预测被诊断为HCM的患者在10年期间心脏不良重塑方面的疾病进展。
该方法由6个预测回归模型组成，这些模型独立预测6个临床特征的未来值：左心房大小、左心房容积、左心室射血分数、纽约心脏协会功能分级、左心室内径舒张末期值和左心室内径收缩末期值。我们用使用Shapley加法解释方法生成的解释对每个预测进行补充。
最终实验表明，与专家（平均低0.34）或专家联盟（平均低0.22）相比，6个构建模型中的5个模型的预测误差更低。实验表明，半监督学习和虚拟患者的人工数据有助于提高预测准确性。表现最佳的随机森林模型将R值从0.3提高到了0.6。
通过让医学专家对结果进行解释和验证，我们确定了与专家对6个目标中的5个目标的表现相比，这些模型具有良好的性能。
