Department of Medicine, Surgery and Dentistry, University of Salerno, Via S. Allende, Baronissi (SA), 84081, Italy.
Department of Computer Science, University of Salerno, Via Giovanni Paolo II, 132, Fisciano (SA), 84084, Italy.
Comput Biol Med. 2024 Nov;182:109110. doi: 10.1016/j.compbiomed.2024.109110. Epub 2024 Sep 6.
Heart Failure (HF) poses a challenge for our health systems, and early detection of Worsening HF (WHF), defined as a deterioration in symptoms and clinical and instrumental signs of HF, is vital to improving prognosis. Predicting WHF in a phase that is currently undiagnosable by physicians would enable prompt treatment of such events in patients at a higher risk of WHF. Although the role of Artificial Intelligence in cardiovascular diseases is becoming part of clinical practice, especially for diagnostic and prognostic purposes, its usage is often considered not completely reliable due to the incapacity of these models to provide a valid explanation about their output results. Physicians are often reluctant to make decisions based on unjustified results and see these models as black boxes. This study aims to develop a novel diagnostic model capable of predicting WHF while also providing an easy interpretation of the outcomes. We propose a threshold-based binary classifier built on a mathematical model derived from the Genetic Programming approach. This model clearly indicates that WHF is closely linked to creatinine, sPAP, and CAD, even though the relationship of these variables and WHF is almost complex. However, the proposed mathematical model allows for providing a 3D graphical representation, which medical staff can use to better understand the clinical situation of patients. Experiments conducted using retrospectively collected data from 519 patients treated at the HF Clinic of the University Hospital of Salerno have demonstrated the effectiveness of our model, surpassing the most commonly used machine learning algorithms. Indeed, the proposed GP-based classifier achieved a 96% average score for all considered evaluation metrics and fully supported the controls of medical staff. Our solution has the potential to impact clinical practice for HF by identifying patients at high risk of WHF and facilitating more rapid diagnosis, targeted treatment, and a reduction in hospitalizations.
心力衰竭（HF）对我们的医疗体系构成了挑战，早期检测恶化的心力衰竭（WHF）至关重要，WHF 定义为症状以及心力衰竭的临床和仪器征象恶化。预测目前医师无法诊断的 WHF 阶段，将使处于更高 WHF 风险的患者能够及时治疗此类事件，从而改善预后。尽管人工智能在心血管疾病中的作用正成为临床实践的一部分，尤其是在诊断和预后方面，但由于这些模型无法对其输出结果提供有效的解释，其使用通常被认为不完全可靠。医师通常不愿意根据没有充分依据的结果做出决策，并将这些模型视为黑箱。本研究旨在开发一种新的诊断模型，能够预测 WHF，同时还可以对结果进行易于解释的预测。我们提出了一种基于基于遗传编程方法的数学模型构建的基于阈值的二进制分类器。该模型清楚地表明，WHF 与肌酸酐、sPAP 和 CAD 密切相关，尽管这些变量与 WHF 的关系几乎很复杂。但是，所提出的数学模型允许提供 3D 图形表示，医务人员可以使用该表示来更好地了解患者的临床情况。使用从萨勒诺大学医院 HF 诊所回顾性收集的 519 名患者的数据进行的实验表明了我们模型的有效性，超过了最常用的机器学习算法。实际上，所提出的基于 GP 的分类器在所有考虑的评估指标上的平均得分为 96%，并且完全支持医务人员的控制。我们的解决方案有可能通过识别处于高 WHF 风险的患者来影响 HF 的临床实践，从而实现更快速的诊断、有针对性的治疗以及减少住院治疗。
