Sundaram Divaakar Siva Baala, Arunachalam Shivaram P, Damani Devanshi N, Farahani Nasibeh Zanjirani, Enayati Moein, Pasupathy Kalyan S, Arruda-Olson Adelaide M
Mayo Clinic Rochester, MN.
Proc Des Med Devices Conf. 2021 Apr;2021. doi: 10.1115/dmd2021-1076. Epub 2021 May 11.
Hypertrophic Cardiomyopathy (HCM) is the most common genetic heart disease in the US and is known to cause sudden death (SCD) in young adults. While significant advancements have been made in HCM diagnosis and management, there is a need to identify HCM cases from electronic health record (EHR) data to develop automated tools based on natural language processing guided machine learning (ML) models for accurate HCM case identification to improve management and reduce adverse outcomes of HCM patients. Cardiac Magnetic Resonance (CMR) Imaging, plays a significant role in HCM diagnosis and risk stratification. CMR reports, generated by clinician annotation, offer rich data in the form of cardiac measurements as well as narratives describing interpretation and phenotypic description. The purpose of this study is to develop an NLP-based interpretable model utilizing impressions extracted from CMR reports to automatically identify HCM patients. CMR reports of patients with suspected HCM diagnosis between the years 1995 to 2019 were used in this study. Patients were classified into three categories of yes HCM, no HCM and, possible HCM. A random forest (RF) model was developed to predict the performance of both CMR measurements and impression features to identify HCM patients. The RF model yielded an accuracy of 86% (608 features) and 85% (30 features). These results offer promise for accurate identification of HCM patients using CMR reports from EHR for efficient clinical management transforming health care delivery for these patients.
肥厚型心肌病（HCM）是美国最常见的遗传性心脏病，已知会导致年轻人猝死（SCD）。虽然在HCM的诊断和管理方面已经取得了重大进展，但仍需要从电子健康记录（EHR）数据中识别HCM病例，以开发基于自然语言处理引导的机器学习（ML）模型的自动化工具，用于准确识别HCM病例，从而改善管理并减少HCM患者的不良后果。心脏磁共振（CMR）成像在HCM诊断和风险分层中起着重要作用。由临床医生注释生成的CMR报告以心脏测量数据以及描述解释和表型描述的叙述形式提供了丰富的数据。本研究的目的是开发一种基于自然语言处理的可解释模型，利用从CMR报告中提取的印象来自动识别HCM患者。本研究使用了1995年至2019年间疑似HCM诊断患者的CMR报告。患者被分为三类：确诊HCM、排除HCM和可能患有HCM。开发了一种随机森林（RF）模型来预测CMR测量和印象特征识别HCM患者的性能。RF模型的准确率分别为86%（608个特征）和85%（30个特征）。这些结果为利用EHR中的CMR报告准确识别HCM患者以进行高效临床管理、改善这些患者的医疗保健服务提供了希望。
