Appl Clin Inform 2021; 12(04): 856-863
DOI: 10.1055/s-0041-1735178
Research Article

Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants

Manan Shah
1   Division of Neonatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
Derek Shu
2   Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
V. B. Surya Prasath
3   Division of Bioinformatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
Yizhao Ni
3   Division of Bioinformatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
4   Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
Andrew H. Schapiro
5   Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
6   Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
Kevin R. Dufendach
1   Division of Neonatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
2   Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
3   Division of Bioinformatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
› Author Affiliations


Background In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedside clinicians to noncentral PICCs.

Objectives This research seeks to use natural language processing (NLP) and supervised machine learning (ML) techniques to predict PICC tip position based primarily on text analysis of radiograph reports from infants with an upper extremity PICC.

Methods Radiographs, containing a PICC line in infants under 6 months of age, were manually classified into 12 anatomical locations based on the radiologist's textual report of the PICC line's tip. After categorization, we performed a 70/30 train/test split and benchmarked the performance of seven different (neural network, support vector machine, the naïve Bayes, decision tree, random forest, AdaBoost, and K-nearest neighbors) supervised ML algorithms. After optimization, we calculated accuracy, precision, and recall of each algorithm's ability to correctly categorize the stated location of the PICC tip.

Results A total of 17,337 radiographs met criteria for inclusion and were labeled manually. Interrater agreement was 99.1%. Support vector machines and neural networks yielded accuracies as high as 98% in identifying PICC tips in central versus noncentral position (binary outcome) and accuracies as high as 95% when attempting to categorize the individual anatomical location (12-category outcome).

Conclusion Our study shows that ML classifiers can automatically extract the anatomical location of PICC tips from radiology reports. Two ML classifiers, support vector machine (SVM) and a neural network, obtained top accuracies in both binary and multiple category predictions. Implementing these algorithms in a neonatal intensive care unit as a clinical decision support system may help clinicians address PICC line position.

Protection of Human and Animal Subjects

The study was reviewed by the Institutional Review Board of Cincinnati Children's Hospital Medical Center (IRB no.: 2019–1057) and deemed exempt.

Supplementary Material

Publication History

Received: 14 February 2021

Accepted: 25 July 2021

Article published online:
08 September 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

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