SIGNIFICANCE 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. OBJECTIVE This project evaluated feasibility using an automated lexical analysis to identify FTN relevant to seizure clusters (SCs). METHODS 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. RESULTS 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. CONCLUSION 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.

中文翻译：

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使用电子癫痫日记中的自由文本注释识别癫痫发作簇

意义 在线癫痫日记提供了大量关于癫痫患者真实世界经历的信息。患者撰写的自由文本注释 (FTN) 反映了患者的关注点和优先事项，并为结构化日记数据提供了补充信息。目标该项目使用自动词法分析来评估与癫痫发作簇 (SC) 相关的 FTN 的可行性。方法 从 EpiDiary™ 中提取数据，这是一个免费的电子癫痫日记，拥有 42,799 个独立用户，生成 1,096,168 个条目和 247,232 FTN。分析结构化数据和 FTN 中 SC 的存在。进行了一项试点研究，以验证自动词法分析算法，以在 98 份日记样本中识别 FTN 中的 SC。然后将词法分析应用于整个数据集。结果包括集群流行率和频率，以及常见的触发因素类型。结果 在 13,987 (32.68%) 份个人日记中发现至少一个 FTN。自动词法分析算法将 FTN 的 5797 个识别为 SC。有 2423 名独特的 SC 患者不能仅通过结构化数据进行识别，而是仅使用 FTN 的词汇分析进行识别。通过结构化数据和 FTN，在 n = 10,331 (24.1%) 的日记用户中确定了癫痫发作簇。每年 SC 天数的中位数为 13.7，（四分位间距 (IQR)：3.2-54.7）。集群日癫痫发作的中位数为 3 (IQR 2-4)。与 SC 患者相关的最常见漏服药物是左乙拉西坦 (n = 576, 29%)，其次是拉莫三嗪 (n = 495, 24%)、托吡酯 (n = 208, 10.5%)、卡马西平 (n = 190, 9.6%) ), 和拉考沙胺（n = 170, 8.6%）。这些百分比通常反映了该人群中药物使用的普遍性。3306 条结构化条目和 4305 条 FTN 记录了救援药物的使用。结论 这项探索性研究展示了一种将词汇分析应用于大型电子癫痫日记数据库中以前未开发的 FTN 的新方法。自由文本注释捕获的有关 SC 的信息无法从结构化日记数据中获得。日记 FTN 包含对癫痫患者非常重要的信息，用他们自己的话写成。结论 这项探索性研究展示了一种将词汇分析应用于大型电子癫痫日记数据库中以前未开发的 FTN 的新方法。自由文本注释捕获的有关 SC 的信息无法从结构化日记数据中获得。日记 FTN 包含对癫痫患者非常重要的信息，用他们自己的话写成。结论 这项探索性研究展示了一种将词汇分析应用于大型电子癫痫日记数据库中以前未开发的 FTN 的新方法。自由文本注释捕获的有关 SC 的信息无法从结构化日记数据中获得。日记 FTN 包含对癫痫患者非常重要的信息，用他们自己的话写成。