Prediction models are popular in medical research and practice. By predicting
an outcome of interest for specific patients, these models may help inform
difficult treatment decisions, and are often hailed as the poster children for
personalized, data-driven healthcare.
We show however, that using prediction models for decision making can lead to
harmful decisions, even when the predictions exhibit good discrimination after
deployment. These models are harmful self-fulfilling prophecies: their
deployment harms a group of patients but the worse outcome of these patients
does not invalidate the predictive power of the model. Our main result is a
formal characterization of a set of such prediction models. Next we show that
models that are well calibrated before} and after deployment are useless for
decision making as they made no change in the data distribution. These results
point to the need to revise standard practices for validation, deployment and
evaluation of prediction models that are used in medical decisions.

使用预测模型做医学决策可能导致有害结果，即使部署后这些模型的预测能力良好，我们需要改变用于医学决策的预测模型的验证、部署和评估过程。

准确的预测模型导致有害的自我实现预言

When accurate prediction models yield harmful self-fulfilling prophecies

The rapid growth of Electronic Health Records (EHRs), as well as the
accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
widespread interests and attentions. Recent progress in the design and
applications of deep learning methods has shown promising results and is
forcing massive changes in healthcare academia and industry, but most of these
methods rely on massive labeled data. In this work, we propose a general deep
learning framework which is able to boost risk prediction performance with
limited EHR data. Our model takes a modified generative adversarial network
namely ehrGAN, which can provide plausible labeled EHR data by mimicking real
patient records, to augment the training dataset in a semi-supervised learning
manner. We use this generative model together with a convolutional neural
network (CNN) based prediction model to improve the onset prediction
performance. Experiments on two real healthcare datasets demonstrate that our
proposed framework produces realistic data samples and achieves significant
improvements on classification tasks with the generated data over several
stat-of-the-art baselines.

本研究提出了一种基于深度半监督学习框架的 ehrGAN 模型来增强由限制的 Electronic Health Records 数据引发的风险预测绩效，并在两个真实的卫生健康数据集上的实验证明该模型可以产生逼真的数据样本，并且在生成数据的分类任务中，该模型较其他基线方法取得了更显著的改进。