Department of Mathematics and Computer Science, University of Ferrara, Via Nicolò Machiavelli 30, Ferrara, 44121, Italy.
DE - Department of Engineering, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy.
Med Biol Eng Comput. 2022 Dec;60(12):3461-3474. doi: 10.1007/s11517-022-02674-1. Epub 2022 Oct 6.
Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program.
最近，人工智能（AI）和机器学习（ML）已成功应用于许多领域，包括医学诊断。由于大量数据的可用性，可以构建可靠的 AI 系统来协助人类做出决策。最近的 COVID-19 大流行迅速在全球范围内传播，造成严重的健康问题和严重的经济和社会破坏。计算机科学家正在与医生合作，开发不同的 ML 模型，使用计算机断层扫描（CT）和临床数据诊断 COVID-19 患者。在这项工作中，我们提出了一个神经符号系统，用于预测到达医院的 COVID-19 患者是否会出现危急情况。所提出的系统依赖于深度 3D 卷积神经网络（3D-CNN）来分析 COVID-19 患者的肺部 CT 扫描，决策树（DT）来分析 COVID-19 患者的临床数据，预测患者最终是否会死亡，以及一个使用分层概率逻辑程序（HPLP）集成前两者的神经系统。预测 COVID-19 患者是否会出现危急情况有助于管理医院有限的重症监护资源。此外，及早了解 COVID-19 患者可能会出现严重情况，使医生能够尽早了解患者的情况，并为那些预计情况危急的患者提供特殊治疗。所提出的系统名为神经 HPLP，在接收者操作特征和精度曲线下面积方面取得了良好的性能，两个指标的值都约为 0.96。因此，使用神经 HPLP，不仅可以有效地预测 COVID-19 患者是否会出现严重情况，还可以提供预测的解释。这使得神经 HPLP 具有可解释性、可解释性和可靠性。
