TALP Research Center, Computer Science Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
IDEAI-UPC Research Center, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
BMC Med Inform Decis Mak. 2023 Sep 19;23(1):189. doi: 10.1186/s12911-023-02287-0.
The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish.
We propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances.
As we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients.
Our algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.
数字医疗数据的指数级增长推动了数据库知识发现（KDD）的发展。提取医疗事件之间的时间关系对于揭示隐藏模式至关重要，这些模式可以帮助医生找到最佳治疗方法、诊断疾病、检测药物不良反应等。本文提出了一种从加泰罗尼亚语和/或西班牙语书写的电子健康记录中提取患者演变模式的方法。
我们提出了一种稳健的提取时间关联规则（TAR）的方法，该方法通过考虑多次就诊的序列，超越了简单的规则提取。我们高度可配置的算法利用这种公式从医疗实例的序列中提取时间关联规则。我们可以生成所需格式、内容和时间因素的规则，同时考虑医疗实例的不同抽象级别。为了证明我们的方法的有效性，我们将其应用于从加泰罗尼亚初级保健中心（CAP）就诊的患有心脏病和中风的多种疾病患者的临床病史中提取患者演变模式。我们的主要目标是发现具有多个时间步骤的复杂规则，这些规则包含一组医疗实例。
由于我们正在处理真实世界中易错的数据，因此我们提出了一个由初级保健专家验证结果的过程。尽管我们的数据集有限，但专家认为正确和相关的模式百分比很高，这是很有希望的。从这些模式中获得的见解可以为预防措施提供信息，并帮助检测风险因素，最终为患者提供更好的治疗和结果。
我们的算法成功提取了一组有意义和相关的时间模式，特别是对于考虑的特定类型的多种疾病患者。这些模式由专家进行了评估，并证明了预测与某些疾病相关的风险因素的能力。此外，医疗事件发生之间的平均时间间隔提供了这些风险因素期限的关键见解。这些信息在初级保健和预防医学背景下具有重要价值，突出了我们的方法作为有价值的医疗工具的潜力。
