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Identifying epilepsy surgery candidates with natural language processing: A systematic review.

Author information

Affiliations

  • 1. University of Adelaide, Adelaide, SA 5005, Australia.
      Authors
    • Tan S 1
    • Tang C 1
    • Ng JS 1
    • Ng C 1
    (4 authors)
  • 2. University of Adelaide, Adelaide, SA 5005, Australia; Royal Adelaide Hospital, Adelaide, SA 5000, Australia.
      Authors
    • Kovoor JG 2
    • Ovenden C 2
    • Goh R 2
    • Abou-Hamden A 2
    (4 authors)
  • 3. University of Adelaide, Adelaide, SA 5005, Australia; Gold Coast University Hospital, Southport, QLD 4215, Australia.
      Authors
    • Gupta AK 3
    (1 author)
  • 4. Central Clinical School, Monash University, Melbourne, VIC 3004, Australia; Alfred Health, Melbourne, VIC 3004, Australia; Royal Melbourne Hospital, Parkville, VIC 3050, Australia.
      Authors
    • Courtney MR 4
    • Neal A 4
    (2 authors)
  • 5. Flinders University and Medical Centre, Bedford Park, SA 5042, Australia.
      Authors
    • Whitham E 5
    • Frasca J 5
    (2 authors)
    • Show all (6)

ORCIDs linked to this article

Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia, 22 Jun 2023, 114:104-109
https://doi.org/10.1016/j.jocn.2023.06.010 PMID: 37354663 

Review

Abstract 

Introduction

Epilepsy surgery is an underutilised, efficacious management strategy for selected individuals with drug-resistant epilepsy. Natural language processing (NLP) may aid in the identification of patients who are suitable to undergo evaluation for epilepsy surgery. The feasibility of this approach is yet to be determined.

Method

In accordance with the PRISMA guidelines, a systematic review of the databases PubMed, EMBASE and Cochrane library was performed. This systematic review was prospectively registered on PROSPERO.

Results

6 studies fulfilled inclusion criteria. The majority of included studies reported on datasets from only a single centre, with one study utilising data from two centres and one study six centres. The most commonly employed algorithms were support vector machines (5/6), with only one study utilising NLP strategies such as random forest models and gradient boosted machines. However, the results are promising, with all studies demonstrating moderate to high levels of performance in the identification of patients who may be suitable to undergo epilepsy surgery evaluation. Furthermore, multiple studies demonstrated that NLP could identify such patients 1-2 years prior to the treating clinicians instigating referral. However, no studies were identified that have evaluated the influence of implementing such algorithms on healthcare systems or patient outcomes.

Conclusions

NLP is a promising approach to aid in the identification of patients that may be suitable to undergo epilepsy surgery evaluation. Further studies are required examining diverse datasets with additional analytical methodologies. Studies evaluating the impact of implementation of such algorithms would be beneficial.

Full text links 

Read article at publisher's site: https://doi.org/10.1016/j.jocn.2023.06.010

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