Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure.
Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure.
复制标题
利用自然学习处理来揭示因心力衰竭入院的患者临床记录中的主题。
DOI：
10.1109/embc48229.2022.9871400
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Banerjee,Tanvi
中科院分区：
文献类型：
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作者：
Agarwal,Ankita;Thirunarayan,Krishnaprasad;Romine,WilliamL;Alambo,Amanuel;Cajita,Mia;Banerjee,Tanvi
当心脏不能泵送血液和氧气来支持身体中的其他器官时，就会发生心力衰竭。治疗包括药物治疗，有时住院治疗。心力衰竭患者可能患有心血管和非心血管合并症。可以分析心力衰竭患者的临床记录，以深入了解这些记录中讨论的主题和这些患者的主要合并症。在这方面，我们应用机器学习技术，如主题建模，来识别在伊利诺伊大学医院和健康科学系统（UI Health）的1，200名心力衰竭患者的临床记录中发现的主要主题。主题建模揭示了这些临床记录中的五个隐藏主题，其中一个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.