TY - JOUR
T1 - Automated interpretation of stress echocardiography reports using natural language processing
AU - Zheng, Chengyi
AU - Sun, Benjamin C.
AU - Wu, Yi Lin
AU - Ferencik, Maros
AU - Lee, Ming Sum
AU - Redberg, Rita F.
AU - Kawatkar, Aniket A.
AU - Musigdilok, Visanee V.
AU - Sharp, Adam L.
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Aims: Stress echocardiography (SE) findings and interpretations are commonly documented in free-text reports. Reusing SE results requires laborious manual reviews. This study aimed to develop and validate an automated method for abstracting SE reports in a large cohort. Methods and results: This study included adult patients who had SE within 30 days of their emergency department visit for suspected acute coronary syndrome in a large integrated healthcare system. An automated natural language processing (NLP) algorithm was developed to abstract SE reports and classify overall SE results into normal, non-diagnostic, infarction, and ischaemia categories. Randomly selected reports (n = 140) were double-blindly reviewed by cardiologists to perform criterion validity of the NLP algorithm. Construct validity was tested on the entire cohort using abstracted SE data and additional clinical variables. The NLP algorithm abstracted 6346 consecutive SE reports. Cardiologists had good agreements on the overall SE results on the 140 reports: Kappa (0.83) and intraclass correlation coefficient (0.89). The NLP algorithm achieved 98.6% specificity and negative predictive value, 95.7% sensitivity, positive predictive value, and F-score on the overall SE results and near-perfect scores on ischaemia findings. The 30-day acute myocardial infarction or death outcomes were highest among patients with ischaemia (5.0%), followed by infarction (1.4%), non-diagnostic (0.8%), and normal (0.3%) results. We found substantial variations in the format and quality of SE reports, even within the same institution. Conclusions: Natural language processing is an accurate and efficient method for abstracting unstructured SE reports. This approach creates new opportunities for research, public health measures, and care improvement.
AB - Aims: Stress echocardiography (SE) findings and interpretations are commonly documented in free-text reports. Reusing SE results requires laborious manual reviews. This study aimed to develop and validate an automated method for abstracting SE reports in a large cohort. Methods and results: This study included adult patients who had SE within 30 days of their emergency department visit for suspected acute coronary syndrome in a large integrated healthcare system. An automated natural language processing (NLP) algorithm was developed to abstract SE reports and classify overall SE results into normal, non-diagnostic, infarction, and ischaemia categories. Randomly selected reports (n = 140) were double-blindly reviewed by cardiologists to perform criterion validity of the NLP algorithm. Construct validity was tested on the entire cohort using abstracted SE data and additional clinical variables. The NLP algorithm abstracted 6346 consecutive SE reports. Cardiologists had good agreements on the overall SE results on the 140 reports: Kappa (0.83) and intraclass correlation coefficient (0.89). The NLP algorithm achieved 98.6% specificity and negative predictive value, 95.7% sensitivity, positive predictive value, and F-score on the overall SE results and near-perfect scores on ischaemia findings. The 30-day acute myocardial infarction or death outcomes were highest among patients with ischaemia (5.0%), followed by infarction (1.4%), non-diagnostic (0.8%), and normal (0.3%) results. We found substantial variations in the format and quality of SE reports, even within the same institution. Conclusions: Natural language processing is an accurate and efficient method for abstracting unstructured SE reports. This approach creates new opportunities for research, public health measures, and care improvement.
KW - Acute coronary syndrome
KW - Artificial intelligence
KW - Emergency department
KW - Natural language processing
KW - Noninvasive stress test
KW - Stress echocardiography
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U2 - 10.1093/ehjdh/ztac047
DO - 10.1093/ehjdh/ztac047
M3 - Article
AN - SCOPUS:85153879884
SN - 2634-3916
VL - 3
SP - 626
EP - 637
JO - European Heart Journal - Digital Health
JF - European Heart Journal - Digital Health
IS - 4
ER -