Today, the existence of a vast amount of electronic health data has created
potential capacities for conducting studies aiming to improve the medical
services provided to patients and reduce the costs of the healthcare system.
One of the topics that has been receiving attention in the field of medicine in
recent years is the identification of patients who are likely to be
re-hospitalized shortly after being discharged from the hospital. This
identification can help doctors choose appropriate treatment methods, thereby
reducing the rate of patient re-hospitalization and resulting in effective
treatment cost reduction. In this study, the prediction of patient
re-hospitalization using text mining approaches and the processing of discharge
report texts in the patient's electronic file has been discussed. To this end,
the performance of various machine learning models has been evaluated using two
approaches: bag of word and bag of concept, in the process of predicting
patient readmission. Comparing the efficiency of these approaches has shown the
superiority of the random forest model and the bag of concept approach over
other machine learning models and approaches. This research has achieved the
highest score in predicting the probability of patient re-hospitalization, with
a recall score of 68.9%, compared to similar works that have utilized machine
learning models in this field.

利用文本挖掘方法和患者电子档案中的出院报告文本进行患者再住院预测，本研究评估了各种机器学习模型在预测患者再住院过程中的效果，并比较了词袋法和概念词袋法两种方法，结果显示随机森林模型和概念词袋法在其他机器学习模型和方法中具有更高的效率，相比于在该领域使用机器学习模型的类似研究，本研究在预测患者再住院概率方面取得了最高分数，回忆率为 68.9%。

通过从临床文本中提取生物医学概念预测患者的再入院

Prediction of readmission of patients by extracting biomedical concepts  from clinical texts

The sequence of visits and procedures performed by the patient in the health
system, also known as the patient's pathway or trajectory, can reveal important
information about the clinical treatment adopted and the health service
provided. The rise of electronic health data availability made it possible to
assess the pathways of a large number of patients. Nevertheless, some
challenges also arose concerning how to synthesize these pathways and how to
mine them from the data, fostering a new field of research. The objective of
this review is to survey this new field of research, highlighting
representation models, mining techniques, methods of analysis, and examples of
case studies.

本综述旨在调查基于患者路径或轨迹的新型研究领域，重点关注路径的表示模型、挖掘技术、分析方法和案例研究，并讨论了采集大量患者的电子医疗数据的挑战。

患者路径建模与挖掘：范围审视

Modelling and Mining of Patient Pathways: A Scoping Review

Leveraging real-world health data for machine learning tasks requires
addressing many practical challenges, such as distributed data silos, privacy
concerns with creating a centralized database from person-specific sensitive
data, resource constraints for transferring and integrating data from multiple
sites, and risk of a single point of failure. In this paper, we introduce a
federated learning framework that can learn a global model from distributed
health data held locally at different sites. The framework offers two levels of
privacy protection. First, it does not move or share raw data across sites or
with a centralized server during the model training process. Second, it uses a
differential privacy mechanism to further protect the model from potential
privacy attacks. We perform a comprehensive evaluation of our approach on two
healthcare applications, using real-world electronic health data of 1 million
patients. We demonstrate the feasibility and effectiveness of the federated
learning framework in offering an elevated level of privacy and maintaining
utility of the global model.

本文介绍了一种联邦学习框架，可以从不同站点本地持有的分布式健康数据中学习全局模型，提供了两个级别的隐私保护，第一级别的隐私保护不在模型训练过程中移动或共享原始数据，第二级别的隐私保护使用差分隐私机制防止对模型的潜在隐私攻击，并在两个医疗应用程序上对我们的方法进行了全面评估，使用 100 万患者的真实世界电子健康数据，证明了联邦学习框架在提供更高级别的隐私保护和维护全局模型效用方面的可行性和有效性。