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Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants
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.
Keywordssupervised machine learning - natural language processing - medical error reduction - radiology information systems - patient safety
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.
Received: 14 February 2021
Accepted: 25 July 2021
08 September 2021 (online)
© 2021. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
- 1 Sharpe E, Pettit J, Ellsbury DL. A national survey of neonatal peripherally inserted central catheter (PICC) practices. Adv Neonatal Care 2013; 13 (01) 55-74
- 2 Gnannt R, Connolly BL, Parra DA, Amaral J, Moineddin R, Thakor AS. Variables decreasing tip movement of peripherally inserted central catheters in pediatric patients. Pediatr Radiol 2016; 46 (11) 1532-1538
- 3 Loskutav A, Wible BC. Peripherally inserted central catheters. In: Wible BC. ed. Diagnostic Imaging: Interventional Procedures. 2nd ed.. Philadelphia, PA: Elsevier; 2018: 102-107
- 4 Dhillon SS, Connolly B, Shearkhani O, Brown M, Hamilton R. Arrhythmias in children with peripherally inserted central catheters (PICCs). Pediatr Cardiol 2020; 41 (02) 407-413
- 5 Goldwasser B, Baia C, Kim M, Taragin BH, Angert RM. Non-central peripherally inserted central catheters in neonatal intensive care: complication rates and longevity of catheters relative to tip position. Pediatr Radiol 2017; 47 (12) 1676-1681
- 6 Jain A, Deshpande P, Shah P. Peripherally inserted central catheter tip position and risk of associated complications in neonates. J Perinatol 2013; 33 (04) 307-312
- 7 Trivedi G, Dadashzadeh ER, Handzel RM, Chapman WW, Visweswaran S, Hochheiser H. Interactive NLP in clinical care: identifying incidental findings in radiology reports. Appl Clin Inform 2019; 10 (04) 655-669
- 8 Jones BE, South BR, Shao Y. et al. Development and validation of a natural language processing tool to identify patients treated for pneumonia across VA emergency departments. Appl Clin Inform 2018; 9 (01) 122-128
- 9 Pons E, Braun LM, Hunink MG, Kors JA. Natural language processing in radiology: a systematic review. Radiology 2016; 279 (02) 329-343
- 10 Kreimeyer K, Foster M, Pandey A. et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform 2017; 73: 14-29
- 11 Goldberg Y, Hirst G. Neural network methods in natural language processing. Synthesis Lectures on Human Language Technologies 2017; 10 (01) 1-309
- 12 Zunic A, Corcoran P, Spasic I. Sentiment analysis in health and well-being: systematic review. JMIR Med Inform 2020; 8 (01) e16023
- 13 Nawab K, Ramsey G, Schreiber R. Natural language processing to extract meaningful information from patient experience feedback. Appl Clin Inform 2020; 11 (02) 242-252
- 14 Zhu VJ, Walker TD, Warren RW, Jenny PB, Meystre S, Lenert LA. Identifying falls risk screenings not documented with administrative codes using natural language processing. AMIA Annu Symp Proc 2018; 2017: 1923-1930
- 15 Nguyen AN, Truran D, Kemp M. et al. Computer-assisted diagnostic coding: effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings. AMIA Annu Symp Proc 2018; 2018: 807-816
- 16 Grundmeier RW, Masino AJ, Casper TC. et al; Pediatric Emergency Care Applied Research Network. Identification of long bone fractures in radiology reports using natural language processing to support healthcare quality improvement. Appl Clin Inform 2016; 7 (04) 1051-1068
- 17 Sevenster M, Buurman J, Liu P, Peters JF, Chang PJ. Natural language processing techniques for extracting and categorizing finding measurements in narrative radiology reports. Appl Clin Inform 2015; 6 (03) 600-110
- 18 Malmasi S, Sandor NL, Hosomura N, Goldberg M, Skentzos S, Turchin A. Canary: an NLP platform for clinicians and researchers. Appl Clin Inform 2017; 8 (02) 447-453
- 19 Deo RC. Machine learning in medicine. Circulation 2015; 132 (20) 1920-1930
- 20 Lopez C, Tucker S, Salameh T, Tucker C. An unsupervised machine learning method for discovering patient clusters based on genetic signatures. J Biomed Inform 2018; 85: 30-39
- 21 Choy G, Khalilzadeh O, Michalski M. et al. Current applications and future impact of machine learning in radiology. Radiology 2018; 288 (02) 318-328
- 22 McBee MP, Awan OA, Colucci AT. et al. Deep learning in radiology. Acad Radiol 2018; 25 (11) 1472-1480
- 23 Esteva A, Kuprel B, Novoa RA. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542 (7639): 115-118
- 24 Liu Y, Chen PC, Krause J, Peng L. How to read articles that use machine learning: users' guides to the medical literature. JAMA 2019; 322 (18) 1806-1816
- 25 Dominiczak J, Khansa L. Principles of automation for patient safety in intensive care: learning from aviation. Jt Comm J Qual Patient Saf 2018; 44 (06) 366-371
- 26 Lee H, Mansouri M, Tajmir S, Lev MH, Do S. A deep-learning system for fully-automated peripherally inserted central catheter (PICC) tip detection. J Digit Imaging 2018; 31 (04) 393-402
- 27 Kim C, Zhu V, Obeid J, Lenert L. Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke. PLoS One 2019; 14 (02) e0212778
- 28 Rossum GV, Drake FL. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace; 2009
- 29 Pedregosa F, Varoquaux G, Gramfort A. et al. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011; 12: 2825-2830
- 30 Blagev DP, Lloyd JF, Conner K. et al. Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol 2016; 13 (2, suppl): R18-R24
- 31 Bell SK, Delbanco T, Elmore JG. et al. Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Netw Open 2020; 3 (06) e205867
- 32 Lam BD, Bourgeois F, Dong ZJ, Bell SK. Speaking up about patient-perceived serious visit note errors: patient and family experiences and recommendations. J Am Med Inform Assoc 2021; 28 (04) 685-694
- 33 Yadav S, Kazanji N. KC N. et al. Comparison of accuracy of physical examination findings in initial progress notes between paper charts and a newly implemented electronic health record. J Am Med Inform Assoc 2017; 24 (01) 140-144
- 34 Howe JL, Adams KT, Hettinger AZ, Ratwani RM. Electronic health record usability issues and potential contribution to patient harm. JAMA 2018; 319 (12) 1276-1278
- 35 Robertson SL, Robinson MD, Reid A. Electronic health record effects on work-life balance and burnout within the I3 population collaborative. J Grad Med Educ 2017; 9 (04) 479-484
- 36 Carayon P, Wetterneck TB, Alyousef B. et al. Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. Int J Med Inform 2015; 84 (08) 578-594
- 37 Zahabi M, Kaber DB, Swangnetr M. Usability and safety in electronic medical records interface design: a review of recent literature and guideline formulation. Hum Factors 2015; 57 (05) 805-834
- 38 Bishop CM. Pattern Recognition and Machine Learning. New York, NY: Springer; 2016
- 39 Aggarwal CC. Neural Networks and Deep Learning: A Textbook. Switzerland: Springer; 2018
- 40 Shawe-Taylor JCN. Kernel Methods for Pattern Analysis. Davis, CA: Cambridge University Press; 2004
- 41 Breiman L. Random forests. Mach Learn 2001; 45 (01) 5-32