当前位置: X-MOL 学术Artif. Intell. Med. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Data reduction and data visualization for automatic diagnosis using gene expression and clinical data.
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2020-05-28 , DOI: 10.1016/j.artmed.2020.101884
Pierangela Bruno 1 , Francesco Calimeri 1 , Alexandre Sébastien Kitanidis 2 , Elena De Momi 2
Affiliation  

Accurate diagnoses of specific diseases require, in general, the review of the whole medical history of a patient. Currently, even though many advances have been made for disease monitoring, domain experts are still requested to perform direct analyses in order to get a precise classification, thus implying significant efforts and costs.

In this work we present a framework for automated diagnosis based on high-dimensional gene expression and clinical data. Given that high-dimensional data can be difficult to analyze and computationally expensive to process, we first perform data reduction to transform high-dimensional representations of data into a lower dimensional space, yet keeping them meaningful for our purposes. We used then different data visualization techniques to embed complex pieces of information in 2-D images, that are in turn used to perform diagnosis relying on deep learning approaches. Experimental analyses show that the proposed method achieves good performance, featuring a prediction Recall value between 91% and 99%.



中文翻译:

使用基因表达和临床数据进行自动诊断的数据简化和数据可视化。

对特定疾病的准确诊断通常需要回顾患者的整个病史。目前,尽管在疾病监测方面取得了许多进展,但仍需要领域专家进行直接分析以获得精确的分类,因此需要付出巨大的努力和成本。

在这项工作中,我们提出了一个基于高维基因表达和临床数据的自动诊断框架。鉴于高维数据难以分析且处理起来计算成本高昂,我们首先执行数据约简以将数据的高维表示转换为低维空间,但仍保持它们对我们的目的有意义。然后,我们使用不同的数据可视化技术将复杂的信息嵌入到二维图像中,这些信息又用于依靠深度学习方法进行诊断。实验分析表明,所提出的方法取得了良好的性能,预测召回值在 91% 到 99% 之间。

更新日期:2020-05-28
down
wechat
bug