Patient Trajectory Modelling in Longitudinal Data: A Review on Existing Solutions
Patient Trajectory Modelling in Longitudinal Data: A Review on Existing Solutions
复制标题
纵向数据中的患者轨迹建模：现有解决方案的回顾
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Matos
中科院分区：
文献类型：
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作者：
J. F. Silva;S. Matos
医生使用电子健康记录来监测患者的健康状况，并做出更准确的检查、诊断和临床决策。然而，随着为每个患者存储的信息量的不断增加，手动处理和消化所有这些信息变得越来越具有挑战性，这为开发临床决策支持系统（例如患者轨迹建模解决方案）提供了机会。患者轨迹建模是一个日益增长的研究兴趣的主题，由于其潜在的，以帮助提高医疗保健质量，通过促进预防医学的做法，因为早期的疾病诊断可以使更好的疾病管理和早期干预，沿着改善资源分配。在本文中，我们回顾了最近的患者轨迹预测方法，基于三个关键方面进行了比较：1）开发方法的核心是什么，2）工作中使用的数据类型，以及3）在所提出的解决方案中如何处理时间信息。本文所介绍的作品的选择说明了目前的范例，在病人的轨迹建模，并提供了一个概述在这一领域的一些现有的挑战。
Physicians use electronic health records to monitor patient health and make more accurate prognoses, diagnoses and clinical decisions. However, with the ever increasing amounts of information stored for each patient, manually processing and digesting all this information becomes increasingly challenging, which opens an opportunity for developing clinical decision support systems such as patient trajectory modelling solutions. Patient trajectory modelling is a topic of growing research interest due to its potential to help improving health care quality by fostering preventive medicine practices, since an earlier disease diagnosis can enable better disease management and earlier intervention, along with an improved resource allocation. In this paper, we review recent approaches for patient trajectory prediction, performing a comparison based on three key aspects: 1) what is the core of the developed approach, 2) what type of data is used in the work, and 3) how is temporal information handled in the proposed solution. The resulting selection of works herein presented illustrates the current paradigm in patient trajectory modelling, and provides an overview on some of the existing challenges in this field.