PerMediQ GmbH, Pelargusstr. 2, 70180 Stuttgart, Germany.
QUIBIQ GmbH, Heßbrühlstr. 11, D-70565 Stuttgart, Germany.
Artif Intell Med. 2022 Sep;131:102359. doi: 10.1016/j.artmed.2022.102359. Epub 2022 Jul 19.
Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic health records (EHRs) offer the potential to uncover relevant patterns relating to diseases and therapies, which in turn could help identify empirical medical guidelines to reflect best practices in a healthcare system. Based on this pattern of identification model, it is thus possible to implement recommender systems with the notion that a higher volume of procedures is often associated with better high-quality models.
Although there are several different applications that uses machine learning methods to identify such patterns, such identification is still a challenge, due in part because these methods often ignore the basic structure of the population, or even considering the similarity of diagnoses and patient typology. To this end, we have developed a method based on graph-data representation aimed to cluster 'similar' patients. Using such a model, patients will be linked when there is a same and/or similar patterns are being observed amongst them, a concept that will enable the construction of a network-like structure which is called a patient graph. This structure can be then analyzed by Graph Neural Networks (GNN) to identify relevant labels, and in this case the appropriate medical procedures that will be recommended.
We were able to construct a patient graph structure based on the patient's basic information like age and gender as well as the diagnosis and the trained GNNs models to identify the corresponding patient's therapies using a synthetic patient database. We have even compared our GNN models against different baseline models (using the SCIKIT-learn library of python) and also against the performance of these different model-methods. We have found that the GNNs models are superior, with an average improvement of the f1 score of 6.48 % in respect to the baseline models. In addition, the GNNs models are useful in performing additional clustering analysis which allow a distinctive identification of specific therapeutic/treatment clusters relating to a particular combination of diagnoses.
We found that the GNNs models offer a promising lead to model the distribution of diagnoses in patient population, and is thus a better model in identifying patients with similar phenotype based on the combination of morbidities and/or comorbidities. Nevertheless, network/graph building is still challenging and prone to biases as it is highly dependent on how the ICD distribution affects the patient network embedding space. This graph setup not only requires a high quality of the underlying diagnostic ecosystem, but it also requires a good understanding on how patients at hand are identified by disease respectively. For this reason, additional work is still needed to better improve patient embedding in graph structures for future investigations and the applications of this service-based technology. Therefore, there has not been any interventional study yet.
目前，医疗保健行业致力于通过医疗保健提供者提高患者护理管理的质量，并提高其经济绩效/效率（例如，成本效益）。电子健康记录（EHR）中存储的数据提供了发现与疾病和治疗相关的相关模式的潜力，这反过来又有助于确定反映医疗系统最佳实践的经验医学指南。基于这种识别模式的模型，因此可以实施推荐系统，其概念是更高的程序量通常与更好的高质量模型相关联。
尽管有许多不同的应用程序使用机器学习方法来识别这种模式，但这种识别仍然是一个挑战，部分原因是这些方法经常忽略人口的基本结构，甚至考虑诊断和患者类型的相似性。为此，我们开发了一种基于图形数据表示的方法，旨在对“相似”患者进行聚类。使用这种模型，当观察到它们之间存在相同和/或相似的模式时，患者将被链接，这一概念将能够构建一个称为患者图的类似网络的结构。然后可以通过图神经网络（GNN）分析此结构以识别相关标签，在这种情况下，推荐适当的医疗程序。
我们能够基于患者的基本信息（如年龄和性别）以及诊断构建患者图结构，并使用合成患者数据库识别训练有素的 GNN 模型所对应的患者疗法。我们甚至将我们的 GNN 模型与不同的基线模型（使用 Python 的 SCIKIT-learn 库）进行了比较，并且还与这些不同模型方法的性能进行了比较。我们发现 GNN 模型具有优势，与基线模型相比，平均 f1 分数提高了 6.48%。此外，GNN 模型可用于执行额外的聚类分析，从而可以对特定诊断组合相关的特定治疗/治疗群集进行独特识别。
我们发现 GNN 模型为模型化患者群体中的诊断分布提供了有希望的线索，因此是一种基于合并症和/或合并症识别具有相似表型的患者的更好模型。然而，网络/图的构建仍然具有挑战性且容易出现偏差，因为它高度依赖于 ICD 分布如何影响患者网络嵌入空间。这种图形设置不仅需要底层诊断生态系统的高质量，还需要很好地了解如何分别根据疾病识别手头的患者。出于这个原因，还需要做更多的工作来更好地改进患者在图结构中的嵌入，以便进行未来的研究和这项基于服务的技术的应用。因此，目前还没有任何干预性研究。
