Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants Article Swipe
Manan Shah
,
Derek Shu
,
V. B. Surya Prasath
,
Yizhao Ni
,
Andrew H. Schapiro
,
Kevin R. Dufendach
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1055/s-0041-1735178
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1055/s-0041-1735178
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1055/s-0041-1735178
- http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0041-1735178.pdf
- OA Status
- bronze
- Cited By
- 9
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198547783
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3198547783Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1055/s-0041-1735178Digital Object Identifier
- Title
-
Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in InfantsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-01Full publication date if available
- Authors
-
Manan Shah, Derek Shu, V. B. Surya Prasath, Yizhao Ni, Andrew H. Schapiro, Kevin R. DufendachList of authors in order
- Landing page
-
https://doi.org/10.1055/s-0041-1735178Publisher landing page
- PDF URL
-
https://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0041-1735178.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0041-1735178.pdfDirect OA link when available
- Concepts
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Peripherally inserted central catheter, Artificial intelligence, Support vector machine, Naive Bayes classifier, Medicine, Random forest, Categorization, Computer science, Radiography, Deep learning, Machine learning, Radiology, CatheterTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
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2025: 3, 2024: 1, 2023: 2, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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