| Title: |
Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing |
| Authors: |
Marcus, Hani J.; Grover, Patrick; Williams, Simon C.; Noor, Kawsar; Sinha, Siddharth; Dobson, Richard J.B.; Searle, Thomas; Funnell, Jonathan P.; Hanrahan, John G.; Muirhead, William R.; Kitchen, Neil; Kanona, Hala; Khalil, Sherif; Saeed, Shakeel R. |
| Contributors: |
Wellcome; EPSRC; Centre for Interventional and Surgical Sciences, University College London; Margaret Spittle Research Fellowship Grant; NIHR Biomedical Research Centre at University College London; NIHR Academic Clinical Fellowship; Wellcome Trust |
| Source: |
Journal of Neurological Surgery Part B: Skull Base ; volume 86, issue 03, page 332-341 ; ISSN 2193-6331 2193-634X |
| Publisher Information: |
Georg Thieme Verlag KG |
| Publication Year: |
2024 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1055/s-0044-1786738 |
| DOI: |
10.1055/s-0044-1786738.pdf |
| Availability: |
https://doi.org/10.1055/s-0044-1786738; http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0044-1786738.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.5513D6F6 |
| Database: |
BASE |