Dosing models often use differential equations to model biological dynamics.
Neural differential equations in particular can learn to predict the derivative
of a process, which permits predictions at irregular points of time. However,
this temporal flexibility often comes with a high sensitivity to noise, whereas
medical problems often present high noise and limited data. Moreover, medical
dosing models must generalize reliably over individual patients and changing
treatment policies. To address these challenges, we introduce the Neural Eigen
Stochastic Differential Equation algorithm (NESDE). NESDE provides
individualized modeling (using a hypernetwork over patient-level parameters);
generalization to new treatment policies (using decoupled control); tunable
expressiveness according to the noise level (using piecewise linearity); and
fast, continuous, closed-form prediction (using spectral representation). We
demonstrate the robustness of NESDE in both synthetic and real medical
problems, and use the learned dynamics to publish simulated medical gym
environments.

该论文提出了一种名为 Neural Eigen 随机微分方程的算法，通过在患者设定的超参数上运行超网络来提供个性化建模、扩展到新的治疗政策、根据噪声水平进行可调表现，以及快速、连续和闭合的预测，以模拟医学健身房环境中的真实医疗问题。

神经特征值分解实现个体化剂量动力学

Individualized Dosing Dynamics via Neural Eigen Decomposition

We outline how to create a mechanism that provides an optimal way to elicit,
from an arbitrary group of experts, the probability of the truth of an
arbitrary logical proposition together with collective information that has an
explicit form and interprets this probability. Namely, we provide strong
arguments for the possibility of the development of a self-resolving prediction
market with play money that incentivizes direct information exchange between
experts. Such a system could, in particular, motivate experts from all over the
world to collectively solve scientific or medical problems in a very efficient
manner. In our main considerations about real experts, they are not assumed to
be Bayesian and their behavior is described by utilities that satisfy the von
Neumann--Morgenstern axioms only locally.

提出一种使用预测市场机制，通过激励专家之间直接信息交流的方式来解决科学或医学领域问题的方法。