Worldwide, many millions of people die suddenly and unexpectedly each year,
either with or without a prior history of cardiovascular disease. Such events
are sparse (once in a lifetime), many victims will not have had prior
investigations for cardiac disease and many different definitions of sudden
death exist. Accordingly, sudden death is hard to predict.
This analysis used NHS Electronic Health Records (EHRs) for people aged
$\geq$50 years living in the Greater Glasgow and Clyde (GG\&C) region in 2010
(n = 380,000) to try to overcome these challenges. We investigated whether
medical history, blood tests, prescription of medicines, and hospitalisations
might, in combination, predict a heightened risk of sudden death.
We compared the performance of models trained to predict either sudden death
or all-cause mortality. We built six models for each outcome of interest: three
taken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and three
of our own creation. We trained these using two different data representations:
a language-based representation, and a sparse temporal matrix.
We used global interpretability to understand the most important features of
each model, and compare how much agreement there was amongst models using Rank
Biased Overlap. It is challenging to account for correlated variables without
increasing the complexity of the interpretability technique. We overcame this
by clustering features into groups and comparing the most important groups for
each model. We found the agreement between models to be much higher when
accounting for correlated variables.
Our analysis emphasises the challenge of predicting sudden death and
emphasises the need for better understanding and interpretation of machine
learning models applied to healthcare applications.

通过使用英国国家医疗服务 (NHS) 电子健康记录 (EHR)，该研究通过构建预测模型，结合病史、血液检测、药物处方和住院情况，尝试预测突然死亡的风险，并强调了预测突然死亡的挑战和对机器学习模型在医疗应用中的理解和解释的需求。

现有死亡率预测模型的共识：从全因死亡到突发死亡预测

Consensus of state of the art mortality prediction models: From  all-cause mortality to sudden death prediction

Predicting highrisk vascular diseases is a significant issue in the medical
domain. Most predicting methods predict the prognosis of patients from
pathological and radiological measurements, which are expensive and require
much time to be analyzed. Here we propose deep attention models that predict
the onset of the high risky vascular disease from symbolic medical histories
sequence of hypertension patients such as ICD-10 and pharmacy codes only,
Medical History-based Prediction using Attention Network (MeHPAN). We
demonstrate two types of attention models based on 1) bidirectional gated
recurrent unit (R-MeHPAN) and 2) 1D convolutional multilayer model (C-MeHPAN).
Two MeHPAN models are evaluated on approximately 50,000 hypertension patients
with respect to precision, recall, f1-measure and area under the curve (AUC).
Experimental results show that our MeHPAN methods outperform standard
classification models. Comparing two MeHPANs, R-MeHPAN provides more better
discriminative capability with respect to all metrics while C-MeHPAN presents
much shorter training time with competitive accuracy.

本研究提出了基于深度注意模型的高风险心血管疾病预测模型，对单纯的高血压患者病史序列进行预测，通过比较双向门控循环单元模型和一维卷积多层模型，实验结果表明该模型的分类精度优于传统模型，并且具有更好的性能表现。