Performance and Accuracy of Natural Language Processing to Identify Disease Aetiology from Non-Structured Cardiac MRI Electronic Medical Record Reports

Abstract

The utility of cardiac MRI (CMR) in patients with heart failure has been well demonstrated and continues to expand as MRI techniques evolve. Its main superiorities in this patient population include: accurate and reproducible quantification of ventricular systolic functions; enhanced discrimination of abnormal myocardial tissue characteristics (i.e., oedema, interstitial fibrosis, and replacement fibrosis); and assessment of valvular function/morphology, endocardium and pericardium in a single scan.1,2 CMR is now an essential part of the diagnosis of various types of heart failure, including cardiac amyloidosis, cardiac sarcoidosis, myocarditis, arrhythmogenic right ventricular cardiomyopathy, and iron overload cardiomyopathy. CMR findings also have prognostic implications, such as in hypertrophic cardiomyopathy.1,2These have resulted in an increasing demand and utility of CMR in routine clinical practice. However, the synthesis of imaging findings into a final or differential diagnosis is typically written in free-text, resulting in difficulties with accurately categorising cardiomyopathy types by generic query algorithms. Natural language processing (NLP) is an analytical method that has been used to develop computer-based algorithms that handle and transform natural linguistics so that the information can be used for computation.3 It enables gathering and combining of information extracted from various online databases, and helps create solid outputs that could serve as research endpoints, including sample identification and variable collection. In the field of imaging, NLP may also have several clinical applications, such as highlighting and classifying imaging findings, generating follow-up recommendations, imaging protocols, and survival prediction models.4