DETI/IEETA, University of Aveiro, Aveiro, Portugal.
J Biomed Inform. 2022 Oct;134:104195. doi: 10.1016/j.jbi.2022.104195. Epub 2022 Sep 21.
Electronic Health Records (EHRs) aggregate diverse information at the patient level, holding a trajectory representative of the evolution of the patient health status throughout time. Although this information provides context and can be leveraged by physicians to monitor patient health and make more accurate prognoses/diagnoses, patient records can contain information from very long time spans, which combined with the rapid generation rate of medical data makes clinical decision making more complex. Patient trajectory modelling can assist by exploring existing information in a scalable manner, and can contribute in augmenting health care quality by fostering preventive medicine practices (e.g. earlier disease diagnosis).
We propose a solution to model patient trajectories that combines different types of information (e.g. clinical text, standard codes) and considers the temporal aspect of clinical data. This solution leverages two different architectures: one supporting flexible sets of input features, to convert patient admissions into dense representations; and a second exploring extracted admission representations in a recurrent-based architecture, where patient trajectories are processed in sub-sequences using a sliding window mechanism.
The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression, using the publicly available Medical Information Mart for Intensive Care (MIMIC)-III clinical database. The results obtained demonstrate the potential of the first architecture to model readmission and diagnoses prediction using single patient admissions. While information from clinical text did not show the discriminative power observed in other existing works, this may be explained by the need to fine-tune the clinicalBERT model. Finally, we demonstrate the potential of the sequence-based architecture using a sliding window mechanism to represent the input data, attaining comparable performances to other existing solutions.
Herein, we explored DL-based techniques to model patient trajectories and propose two flexible architectures that explore patient admissions on an individual and sequence basis. The combination of clinical text with other types of information led to positive results, which can be further improved by including a fine-tuned version of clinicalBERT in the architectures. The proposed solution can be publicly accessed at https://github.com/bioinformatics-ua/PatientTM.
电子健康记录 (EHR) 在患者层面聚合了各种信息，记录了患者健康状况随时间演变的轨迹。虽然这些信息提供了背景信息，可以被医生利用来监测患者的健康状况并做出更准确的预测/诊断，但患者记录可能包含来自很长时间跨度的信息，再加上医疗数据的快速生成速度，使得临床决策更加复杂。患者轨迹建模可以通过以可扩展的方式探索现有信息来提供帮助，并通过促进预防医学实践（例如更早地诊断疾病）来提高医疗质量。
我们提出了一种解决方案，用于对患者轨迹建模，该解决方案结合了不同类型的信息（例如临床文本、标准代码）并考虑了临床数据的时间方面。该解决方案利用了两种不同的架构：一种支持灵活的输入特征集，将患者入院转换为密集表示；另一种架构则在基于递归的架构中探索提取的入院表示，其中使用滑动窗口机制在子序列中处理患者轨迹。
在所使用的公开的医疗信息监护 (MIMIC)-III 临床数据库上，我们针对两个不同的临床结果（意外患者再入院和疾病进展）评估了所开发的解决方案。结果表明，第一种架构在使用单个患者入院记录预测再入院和诊断方面具有潜力。虽然临床文本信息并没有表现出其他现有工作中观察到的区分能力，但这可能是由于需要微调临床 BERT 模型所致。最后，我们通过使用滑动窗口机制来表示输入数据展示了基于序列的架构的潜力，该架构的性能可与其他现有解决方案相媲美。
在这里，我们探索了基于深度学习的技术来对患者轨迹建模，并提出了两种灵活的架构，分别基于个体和序列探索患者入院情况。将临床文本与其他类型的信息结合使用取得了积极的结果，通过在架构中包含经过微调的临床 BERT 版本，这些结果还可以进一步改进。该解决方案可以在 https://github.com/bioinformatics-ua/PatientTM 上公开访问。
