Chushig-Muzo David, Soguero-Ruiz Cristina, Miguel Bohoyo Pablo de, Mora-Jiménez Inmaculada
Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain.
University Hospital of Fuenlabrada, Madrid, Spain.
BioData Min. 2022 Sep 5;15(1):18. doi: 10.1186/s13040-022-00303-z.
Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches.
We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting.
To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients.
Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient's health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.
如今，糖尿病和高血压等慢性病患者在全球范围内已达到惊人数量。这些疾病增加了发生急性并发症的风险，带来了巨大的经济负担，并对卫生资源产生了需求。电子健康记录（EHR）的广泛采用为支持决策提供了巨大机遇。然而，从EHR中提取的数据复杂（异构、高维和通常有噪声），阻碍了用传统方法进行知识提取。
我们提议使用去噪自编码器（DAE），这是一种机器学习（ML）技术，可将高维数据转换为潜在表示（LR），从而应对临床数据的主要挑战。在这项工作中，我们探索如何将LR与一种可视化方法相结合，用于在二维空间中映射患者数据，从而了解不同慢性病患者的分布情况。此外，这种表示还可用于表征患者健康状况的演变，这在临床环境中至关重要。
为了获得临床LR，我们考虑了从与西班牙富恩拉夫拉达大学医院相关联的EHR中提取的真实世界数据。实验结果表明，DAE在识别与高血压、糖尿病和多种疾病相关临床模式的患者方面具有巨大潜力。该过程使我们能够找到患有相同主要慢性病但临床特征不同的患者。因此，我们识别出了两类药物治疗不同（胰岛素依赖型和非胰岛素依赖型）的糖尿病患者，以及一组受高血压和妊娠糖尿病影响的女性。我们还展示了一个概念验证，即在考虑与慢性病患者相关的最重要诊断和药物时，如何映射合成患者的健康状况演变。
我们的结果凸显了ML技术在提取临床知识方面的价值，支持识别患有特定慢性病的患者。此外，二维空间中患者健康状况的进展可能被用作临床医生表征健康状况和识别其更相关临床代码的工具。
