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%。