论文标题
精神分裂症患者的精神病复发预测使用基于移动传感的监督深度学习模型
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model
论文作者
论文摘要
基于移动传感的行为变化的建模可以预测精神分裂症患者临时精神病复发的及时干预措施。深度学习模型可以通过建模与预测相关的潜在行为特征来补充现有的非深度学习模型，以进行复发预测。但是，鉴于个体间的行为差异，预测模型可能需要模型个性化。在这项工作中，我们提出了复发性新闻，这是一个长期的短期记忆（LSTM）神经网络基于复发预测的模型。该模型是通过使用与给定患者最相似的患者的数据进行培训的特定患者个性化的。几个人口统计学和基线心理健康评分被认为是定义患者相似性的个性化指标。我们研究了个性化对培训数据集特征，学习的嵌入以及复发预测性能的影响。我们将复发性新网络与基于深度学习的异常检测模型进行了比较，以进行复发预测。此外，我们研究了融合模型中的RelapsePredNet是否可以补充融合模型中提出的聚类和模板特征的clusterRfModel（一个随机的森林模型和模板特征），通过识别与复发预测相关的潜在行为特征。通过从63名精神分裂症患者获得的连续移动感应数据组成的交叉检查数据集，每个人都经过了长达一年的监测，用于我们的评估。拟议的复发性网络的表现优于基于深度学习的异常检测模型，用于复发预测。在完整测试集中，预测的F2得分为0.21和0.52，复发测试集（分别由仅复发的患者组成）。与现有的基于深度学习的模型相比，这些对应于29.4％和38.8％的改善。
Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement existing non-deep learning models for relapse prediction by modeling latent behavioral features relevant to the prediction. However, given the inter-individual behavioral differences, model personalization might be required for a predictive model. In this work, we propose RelapsePredNet, a Long Short-Term Memory (LSTM) neural network-based model for relapse prediction. The model is personalized for a particular patient by training using data from patients most similar to the given patient. Several demographics and baseline mental health scores were considered as personalization metrics to define patient similarity. We investigated the effect of personalization on training dataset characteristics, learned embeddings, and relapse prediction performance. We compared RelapsePredNet with a deep learning-based anomaly detection model for relapse prediction. Further, we investigated if RelapsePredNet could complement ClusterRFModel (a random forest model leveraging clustering and template features proposed in prior work) in a fusion model, by identifying latent behavioral features relevant for relapse prediction. The CrossCheck dataset consisting of continuous mobile sensing data obtained from 63 schizophrenia patients, each monitored for up to a year, was used for our evaluations. The proposed RelapsePredNet outperformed the deep learning-based anomaly detection model for relapse prediction. The F2 score for prediction were 0.21 and 0.52 in the full test set and the Relapse Test Set (consisting of data from patients who have had relapse only), respectively. These corresponded to a 29.4% and 38.8% improvement compared to the existing deep learning-based model for relapse prediction.
