深心:心血管风险预测半超序列学习 (DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction)

Brandon Ballinger,Johnson Hsieh,Avesh Singh,Nimit Sohoni,Jack Wang,Geoffrey H. Tison,Gregory M. Marcus,Jose M. Sanchez,Carol Maguire,Jeffrey E. Olgin,Mark J. Pletcher

from arxiv, Presented at AAAI 2018

We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised train- ing methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.

翻译：我们在57,675人周内对来自现成可磨损性心率传感器的半监督、多任务LSTM数据进行培训和验证,这些数据显示在检测糖尿病(0.8451),高胆固醇(0.7441),高血压(0.8086)和睡眠apnea(0.8298)等多种医疗条件时具有很高的准确性。我们比较了两种半监督的火车方法,即半监督序列学习和超速训练,并显示它们比医学文献中的手工工程生物标志好。我们相信我们的工作提出了一种基于流行的磨损设备如Fitbit、苹果观察或机器人wear所产生的心血管风险分数的病人风险分数的新型分数。