Department of Psychiatry/Epilepsy, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA.
Department of Neuroscience, Johnson and Johnson, Philadelphia, PA, USA.
Epilepsia. 2020 May;61(5):951-958. doi: 10.1111/epi.16507. Epub 2020 May 8.
Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal clinical settings and help generate useful information for treatment planning. Our study aimed to explore the big data from open-source digital conversations among PWE with regard to suicidality, specifically comparing teenagers and adults, using machine learning technology.
Advanced machine-learning empowered methodology was used to mine and structure open-source digital conversations of self-identifying teenagers and adults who endorsed suffering from epilepsy and engaged in conversation about suicide. The search was limited to 12 months and included only conversations originating from US internet protocol (IP) addresses. Natural language processing and text analytics were employed to develop a thematic analysis.
A total of 222 000 unique conversations about epilepsy, including 9000 (4%) related to suicide, were posted during the study period. The suicide-related conversations were posted by 7.8% of teenagers and 3.2% of adults in the study. Several critical differences were noted between teenagers and adults. A higher percentage of teenagers are: fearful of "the unknown" due to seizures (63% vs 12% adults), concerned about social consequences of seizures (30% vs 21%), and seek emotional support (29% vs 19%). In contrast, a significantly higher percentage of adults show a defeatist ("given up") attitude compared to teenagers (42% vs 4%). There were important differences in the author's determined sentiments behind the conversations among teenagers and adults.
In this first of its kind big data analysis of nearly a quarter-million digital conversations about epilepsy using machine learning, we found that teenagers engage in an online conversation about suicide more often than adults. There are some key differences in the attitudes and concerns, which may have implications for the treatment of younger patients with epilepsy.
数字媒体对话可以提供重要的信息,了解癫痫患者(PWE)在正式临床环境之外的关注和挣扎,并为治疗计划生成有用的信息。我们的研究旨在利用机器学习技术,探索 PWE 之间开源数字对话中的大数据,特别是比较青少年和成年人的自杀倾向。
使用先进的机器学习赋能方法,挖掘和构建自我认同的青少年和成年人的开源数字对话,他们承认患有癫痫并就自杀进行了对话。搜索范围限于 12 个月,仅包括源自美国互联网协议(IP)地址的对话。采用自然语言处理和文本分析来进行主题分析。
在研究期间,共发布了 222000 个关于癫痫的独特对话,其中 9000 个(4%)与自杀有关。在研究中,青少年中有 7.8%,成年人中有 3.2%发布了与自杀有关的对话。青少年和成年人之间有几个明显的差异。更高比例的青少年:对癫痫发作的“未知”感到恐惧(63%对 12%成年人),担心癫痫发作的社会后果(30%对 21%),并寻求情感支持(29%对 19%)。相比之下,与青少年相比,成年人表现出明显更高的失败主义(“放弃”)态度(42%对 4%)。青少年和成年人之间的对话背后的作者情绪有很大差异。
这是首次使用机器学习对近 25 万条关于癫痫的数字对话进行的大数据分析,我们发现青少年比成年人更频繁地在网上讨论自杀。在态度和关注点上存在一些关键差异,这可能对治疗年轻的癫痫患者有影响。