Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing

• Published in: PloS one Vol. 15; no. 7; p. e0236827
• Main Authors: Pandey, Mohit, Xu, Zhuoran, Sholle, Evan, Maliakal, Gabriel, Singh, Gurpreet, Fatima, Zahra, Larine, Daria, Lee, Benjamin C., Wang, Jing, van Rosendael, Alexander R., Baskaran, Lohendran, Shaw, Leslee J., Min, James K., Al’Aref, Subhi J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.07.2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Age; Aged; Artificial neural networks; Biology and Life Sciences; Cardiology; Cohort Studies; Computed tomography; Computer and Information Sciences; Confidence intervals; Congestive heart failure; Data collection; Electronic Health Records; Feature extraction; Female; Gender; Heart diseases; Heart failure; Heart Failure - diagnostic imaging; Heart Failure - mortality; Heart Failure - pathology; High frequencies; Humans; Image Processing, Computer-Assisted - methods; Language; Learning algorithms; Machine Learning; Male; Medicine; Medicine and Health Sciences; Morbidity; Mortality; Natural Language Processing; Neural networks; Neural Networks, Computer; Observational studies; Patients; Pleural effusion; Prognosis; Radiography, Abdominal - methods; Radiography, Thoracic - methods; Radiology; Regression analysis; Research and Analysis Methods; Social Sciences; Statistical analysis; Survival Rate; Time dependence; Tomography, X-Ray Computed - methods; Unstructured data; New York; Singapore; United States > US; Pennsylvania
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0236827

Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming. We utilized a machine learning (ML)-based natural language processing (NLP) approach to extract clinical terms from unstructured radiology reports. Additionally, we investigate the prognostic value of the extracted data in predicting all-cause mortality (ACM) in HF patients. This observational cohort study utilized 122,025 thoracoabdominal computed tomography (CT) reports from 11,808 HF patients obtained between 2008 and 2018. 1,560 CT reports were manually annotated for the presence or absence of 14 radiographic findings, in addition to age and gender. Thereafter, a Convolutional Neural Network (CNN) was trained, validated and tested to determine the presence or absence of these features. Further, the ability of CNN to predict ACM was evaluated using Cox regression analysis on the extracted features. 11,808 CT reports were analyzed from 11,808 patients (mean age 72.8 ± 14.8 years; 52.7% (6,217/11,808) male) from whom 3,107 died during the 10.6-year follow-up. The CNN demonstrated excellent accuracy for retrieval of the 14 radiographic findings with area-under-the-curve (AUC) ranging between 0.83-1.00 (F1 score 0.84-0.97). Cox model showed the time-dependent AUC for predicting ACM was 0.747 (95% confidence interval [CI] of 0.704-0.790) at 30 days. An ML-based NLP approach to unstructured CT reports demonstrates excellent accuracy for the extraction of predetermined radiographic findings, and provides prognostic value in HF patients.