JMIR Publications

ABSTRACT

Background:

Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly utilized in healthcare. However, the extent to which NLP has been formally studied in neurological disorders remains unclear.

Objective:

We sought to characterize studies that applied NLP to the diagnosis, prediction, or treatment of common neurological disorders.

Methods:

This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standards and was registered with the Prospective Register of Systematic Reviews (PROSPERO; CRD42021228703). The search was conducted using MEDLINE and EMBASE on May 11, 2022. We included studies of NLP use in migraine, Parkinson disease, Alzheimer disease, stroke and transient ischemic attack (TIA), epilepsy, or multiple sclerosis (MS). We excluded conference abstracts, review articles, as well as studies involving heterogeneous clinical populations or indirect clinical uses of NLP. Study characteristics were extracted and analyzed using descriptive statistics. We did not aggregate measurements of performance in our review due to the high variability in study outcomes, which is the main limitation of the study.

Results:

We identified 916 studies, of which 41 (4.5%) met all eligibility criteria and were included in the final review. The most frequently represented disorders were stroke and TIA (N=20 studies, 48.8%), followed by epilepsy (N=10, 24.4%), Alzheimer disease (N=6, 14.6%), and MS (N=5, 12.2%). We found no studies of NLP use in migraine or Parkinson disease that met our eligibility criteria. The main objective of NLP was diagnosis (N=20, 48.8%) followed by disease phenotyping (N=17, 41.5%), prognostication (N=9, 22%) and treatment (N=4, 9.8%). Eighteen (43.9%) studies used only machine learning approaches, 6 (14.6%) used only rule-based methods, and 17 (41.5%) used both.

Conclusions:

We found that NLP was most commonly applied for diagnosis, implying a potential role for NLP in augmenting diagnostic accuracy in settings with limited access to neurological expertise. We also found several gaps in neurological NLP research, with few to no studies addressing certain disorders, which may suggest additional areas of inquiry.