Kontigo Care AB, Uppsala, Sweden.
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
PLoS One. 2022 Jul 14;17(7):e0271465. doi: 10.1371/journal.pone.0271465. eCollection 2022.
eHealth systems allow efficient daily smartphone-based collection of self-reported data on mood, wellbeing, routines, and motivation; however, missing data is frequent. Within addictive disorders, missing data may reflect lack of motivation to stay sober. We hypothesize that qualitative questionnaire data contains valuable information, which after proper handling of missing data becomes more useful for practitioners.
Anonymized data from daily questionnaires containing 11 questions was collected with an eHealth system for 751 patients with alcohol use disorder (AUD). Two digital continuous biomarkers were composed from 9 wellbeing questions (WeBe-i) and from two questions representing motivation/self-confidence to remain sober (MotSC-i). To investigate possible loss of information in the process of composing the digital biomarkers, performance of neural networks to predict exacerbation events (relapse) in alcohol use disorder was compared.
Long short-term memory (LSTM) neural networks predicted a coming exacerbation event 1-3 days (AUC 0.68-0.70) and 5-7 days (AUC 0.65-0.68) in advance on unseen patients. The predictive capability of digital biomarkers and raw questionnaire data was equal, indicating no loss of information. The transformation into digital biomarkers enable a continuous graphical display of each patient's clinical course and a combined interpretation of qualitative and quantitative aspects of recovery on a time scale.
By transforming questionnaire data with large proportion of missing data into continuous digital biomarkers, the information captured by questionnaires can be more easily used in clinical practice. Information, assessed by the capability to predict exacerbation events of AUD, is preserved when processing raw questionnaire data into digital biomarkers.
电子健康系统允许高效地通过智能手机每天收集关于情绪、幸福感、日常生活和动机的自我报告数据；然而，数据缺失很常见。在成瘾障碍中，数据缺失可能反映了缺乏保持清醒的动机。我们假设定性问卷数据包含有价值的信息，在适当处理缺失数据后，这些信息对从业者来说将更加有用。
使用电子健康系统收集了 751 名酒精使用障碍（AUD）患者的匿名日常问卷数据，共包含 11 个问题。从 9 个幸福感问题组成了两个数字连续生物标志物（WeBe-i），从代表保持清醒的动机/自信的两个问题组成了两个数字连续生物标志物（MotSC-i）。为了研究在组成数字生物标志物的过程中可能丢失的信息，比较了神经网络预测酒精使用障碍恶化事件（复发）的性能。
长短期记忆（LSTM）神经网络预测了即将到来的恶化事件，在未见过的患者中提前 1-3 天（AUC 0.68-0.70）和 5-7 天（AUC 0.65-0.68）。数字生物标志物和原始问卷数据的预测能力相当，表明没有信息丢失。将问卷数据转化为数字生物标志物，可以更方便地在临床实践中使用捕获的信息。通过将包含大量缺失数据的问卷数据转化为连续的数字生物标志物，可以更方便地在临床实践中使用捕获的信息。
通过将包含大量缺失数据的问卷数据转化为连续的数字生物标志物，可以更方便地在临床实践中使用捕获的信息。通过将原始问卷数据处理为数字生物标志物，可以保留评估 AUD 恶化事件能力的信息。
