Identification of seizure clusters using free text notes in an electronic seizure diary

Online seizure diaries offer a wealth of information regarding real world experience of patients living with epilepsy. Free text notes (FTN) written by patients reflect concerns and priorities of patients and provide supplemental information to structured diary data.

This project evaluated feasibility using an automated lexical analysis to identify FTN relevant to seizure clusters (SCs).

Data were extracted from EpiDiary™, a free electronic epilepsy diary with 42,799 unique users, generating 1,096,168 entries and 247,232 FTN. Both structured data as well as FTN were analyzed for presence of SC. A pilot study was conducted to validate an automated lexical analysis algorithm to identify SC in FTN in a sample of 98 diaries. The lexical analysis was then applied to the entire dataset. Outcomes included cluster prevalence and frequency, as well as the types of triggers commonly reported.

At least one FTN was found among 13,987 (32.68%) individual diaries. An automated lexical analysis algorithm identified 5797 of FTN as SC. There were 2423 unique patients with SC that were not identified by structured data alone and were identified using lexical analysis of FTN only. Seizure clusters were identified in n = 10,331 (24.1%) of diary users through both structured data and FTN. The median number of SCs days per year was 13.7, (interquartile rank (IQR): 3.2-54.7). The median number of seizures in a cluster day was 3 (IQR 2-4). The most common missed medication linked to patients with SC was levetiracetam (n = 576, 29%) followed by lamotrigine (n = 495, 24%), topiramate (n = 208, 10.5%), carbamazepine (n = 190, 9.6%), and lacosamide (n = 170, 8.6%). These percentages generally reflected prevalence of medication use in this population. The use of rescue medications was documented in 3306 of structured entries and 4305 in FTN.

This exploratory study demonstrates a novel approach applying lexical analysis to previously untapped FTN in a large electronic seizure diary database. Free text notes captured information about SC not available from the structured diary data. Diary FTN contain information of high importance to people with epilepsy, written in their own words.