论文标题
利用自然学习处理以发现心力衰竭的患者的临床笔记中的主题
Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure
论文作者
论文摘要
当心脏无法像应有的那样支撑体内的其他器官时，心力衰竭就会发生。治疗包括药物，有时还包括住院治疗。心力衰竭的患者可以具有心血管疾病和非心血管合并症。可以分析心力衰竭患者的临床笔记，以深入了解这些注释中讨论的主题以及这些患者的主要合并症。在这方面，我们应用机器学习技术，例如主题建模，以确定针对伊利诺伊大学医院和健康科学系统（UI Health）心力衰竭的1,200名患者进行的临床注释中发现的主要主题。主题建模揭示了这些临床笔记中的五个隐藏主题，其中包括与心脏病合并症有关。
Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling revealed five hidden themes in these clinical notes, including one related to heart disease comorbidities.
