PDF(Pubmed)
Abstract：
Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.
摘要：
人体内的进行性器官水平疾病通常与其他身体部位的疾病相关。例如,肝脏疾病可能与心脏问题有关,而癌症可能与脑部疾病（或心理状况）有关。定义这种相关性是一项复杂的任务，而执行此任务的现有深度学习模型在应用于实时场景时，要么表现出较低的准确性，要么不全面。为了克服这些问题,本文提出了通过异构相关身体器官分析的基于增强生物启发深度学习的多域身体参数分析的设计。所提出的模型最初收集不同身体部位的时间和空间数据扫描，并使用多域特征提取引擎将这些扫描转换为向量集。这些载体由细菌觅食优化器（BFO）处理，这有助于识别高度变异的特征集，它们分别分为不同的疾病类别。盗梦网络的融合，XCceptionNet,和GoogLeNet模型用于执行这些分类。分类的类别通过血液报告的时间分析与其他疾病类型相关联。时间分析引擎使用改进的分析层次处理(MAHP)模型来计算器官间疾病依赖概率。基于这些概率,该模型能够生成患者水平的相关图，临床专家可以用它来建议补救治疗，因此，该模型能够识别脑部疾病和肾脏之间的相关性，心脏病和肺部疾病,心脏病和肝脏疾病,在临床情景下评估时，脑部疾病和不同类型的癌症具有很高的效率。在MITBIH上验证时，DEAP,肾脏CT,RIDER,和PLCO数据样本，观察到所提出的模型能够将相关性的准确性提高8.5%，与类似临床情景下的现有相关模型相比，准确率和召回率提高了3.2%。
