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CC BY 4.0 · J Neurol Surg B Skull Base 2025; 86(03): 332-341
DOI: 10.1055/s-0044-1786738
Original Article

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing

Simon C. Williams 
1   Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
,
Kawsar Noor
3   Department of Computer Science, Institute for Health Informatics, University College London, London, United Kingdom
4   Department of Biostatistics and Health Informatics, NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, United Kingdom
,
Siddharth Sinha
1   Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
,
Richard J.B. Dobson
3   Department of Computer Science, Institute for Health Informatics, University College London, London, United Kingdom
4   Department of Biostatistics and Health Informatics, NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, United Kingdom
5   Department of Informatics, NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
6   Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
,
Thomas Searle
5   Department of Informatics, NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
,
Jonathan P. Funnell
1   Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
,
John G. Hanrahan
1   Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
,
William R. Muirhead
1   Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
,
Neil Kitchen
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
,
Hala Kanona
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
7   Ear Nose and Throat Department, The Royal National ENT and Eastman Dental Hospital, University College London Hospitals, London, United Kingdom
,
Sherif Khalil
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
7   Ear Nose and Throat Department, The Royal National ENT and Eastman Dental Hospital, University College London Hospitals, London, United Kingdom
,
Shakeel R. Saeed
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
7   Ear Nose and Throat Department, The Royal National ENT and Eastman Dental Hospital, University College London Hospitals, London, United Kingdom
8   University College London Ear Institute, London, United Kingdom
,
Hani J. Marcus‡   *
1   Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
,
Patrick Grover‡   *
2   Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
› Institutsangaben
Funding S.C.W., J.P.F., J.G.H., W.R.M., and H.J.M. are supported by the Wellcome (203145Z/16/Z) EPSRC (NS/A000050/1) Centre for Interventional and Surgical Sciences, University College London. S.C.W. was supported by the Margaret Spittle Research Fellowship Grant. H.J.M. is also funded by the NIHR Biomedical Research Centre at University College London. J.G.H. is funded by an NIHR Academic Clinical Fellowship. This research was funded in whole, or in part, by the Wellcome Trust [203145Z/16/Z].
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Abstract

Background Natural language processing (NLP), a subset of artificial intelligence (AI), aims to decipher unstructured human language. This study showcases NLP's application in surgical health care, focusing on vestibular schwannoma (VS). By employing an NLP platform, we identify prevalent text concepts in VS patients' electronic health care records (EHRs), creating concept panels covering symptomatology, comorbidities, and management. Through a case study, we illustrate NLP's potential in predicting postoperative cerebrospinal fluid (CSF) leaks.

Methods An NLP model analyzed EHRs of surgically managed VS patients from 2008 to 2018 in a single center. The model underwent unsupervised (trained on one million documents from EHR) and supervised (300 documents annotated in duplicate) learning phases, extracting text concepts and generating concept panels related to symptoms, comorbidities, and management. Statistical analysis correlated concept occurrences with postoperative complications, notably CSF leaks.

Results Analysis included 292 patients' records, yielding 6,901 unique concepts and 360,929 occurrences. Concept panels highlighted key associations with postoperative CSF leaks, including “antibiotics,” “sepsis,” and “intensive care unit admission.” The NLP model demonstrated high accuracy (precision 0.92, recall 0.96, macro F1 0.93).

Conclusion Our NLP model effectively extracted concepts from VS patients' EHRs, facilitating personalized concept panels with diverse applications. NLP shows promise in surgical settings, aiding in early diagnosis, complication prediction, and patient care. Further validation of NLP's predictive capabilities is warranted.

Keywords

artificial intelligence - vestibular schwannoma - machine learning - Natural Language Processing - retrosigmoid - translabyrinthine

Data Availability

Due to GDPR (General Data Protection Regulation) restrictions, any patient-sensitive data are not available for dissemination. Raw concept occurrence data are available on reasonable request.


Code Availability

CogStack and related models are Open Source and available online.


Authors Contributions

S.C.W.: conceptualization, data curation, formal analysis, methodology, project administration, data analysis, writing—original draft preparation; K.N.: data curation, formal analysis, methodology, writing—reviewing and editing; S.S.: data curation, formal analysis, writing—reviewing and editing; R.J.B.D.: data curation, formal analysis, writing—reviewing and editing; J.P.F.: conceptualization, methodology, data curation, writing—reviewing and editing; J.G.H.: methodology, data curation, writing—reviewing and editing; W.R.M.: methodology, data curation, project administration, writing—reviewing and editing; N.K.: data curation, project administration, writing—reviewing and editing; H.K.: data curation, project administration, writing—reviewing and editing; S.K.: data curation, project administration, writing—reviewing and editing; S.R.S.: data curation, project administration, writing—reviewing and editing; H.J.M.: conceptualization, methodology, data curation, project administration, writing—reviewing and editing; P.G.: conceptualization, methodology, data curation, project administration, writing—reviewing and editing.


* Equal contribution and joint senior authorship.




Publikationsverlauf

Eingereicht: 04. November 2023

Angenommen: 31. März 2024

Artikel online veröffentlicht:
11. Mai 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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