The proliferation of early diagnostic technologies, including self-monitoring
systems and wearables, coupled with the application of these technologies on
large segments of healthy populations may significantly aggravate the problem
of overdiagnosis. This can lead to unwanted consequences such as overloading
health care systems and overtreatment, with potential harms to healthy
individuals. The advent of machine-learning tools to assist diagnosis -- while
promising rapid and more personalised patient management and screening -- might
contribute to this issue. The identification of overdiagnosis is usually post
hoc and demonstrated after long periods (from years to decades) and costly
randomised control trials. In this paper, we present an innovative approach
that allows us to preemptively detect potential cases of overdiagnosis during
predictive model development. This approach is based on the combination of
labels obtained from a prediction model and clustered medical trajectories,
using sepsis in adults as a test case. This is one of the first attempts to
quantify machine-learning induced overdiagnosis and we believe will serves as a
platform for further development, leading to guidelines for safe deployment of
computational diagnostic tools.

通过集成预测模型标签与簇医学轨迹的方法，本研究开发了一种创新的方法，可以在模型开发期间预测性的检测到潜在的过度诊断病例，以提供安全的计算机诊断工具使用指南。

机器学习诱发的脓毒症过度诊断的量化研究

Quantifying machine learning-induced overdiagnosis in sepsis

We introduce the first deep reinforcement learning agent that learns to beat
Atari games with the aid of natural language instructions. The agent uses a
multimodal embedding between environment observations and natural language to
self-monitor progress through a list of English instructions, granting itself
reward for completing instructions in addition to increasing the game score.
Our agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous
Advantage Actor-Critic (A3C) agents, and the best agents posted to OpenAI Gym
on what is often considered the hardest Atari 2600 environment: Montezuma's
Revenge.

该研究介绍了第一个深度强化学习代理，利用自然语言指令学习打败 Ataria 游戏。代理人利用环境观察和自然语言之间的多模态嵌入来自我监控进度，并通过完成自然语言指令来获得奖励。该代理人在 Montezuma's Revenge 环境上显著优于其他代理人。