Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning prediction of patient response time course from early data
  via neural-pharmacokinetic/pharmacodynamic modeling   The longitudinal analysis of patient response time course following doses of
therapeutics is currently performed using Pharmacokinetic/Pharmacodynamic
(PK/PD) methodologies, which requires significant human experience and
expertise in the modeling of dynamical systems. By utilizing recent
advancements in deep learning, we show that the governing differential
equations can be learnt directly from longitudinal patient data. In particular,
we propose a novel neural-PK/PD framework that combines key pharmacological
principles with neural ordinary differential equations. We applied it to an
analysis of drug concentration and platelet response from a clinical dataset
consisting of over 600 patients. We show that the neural-PK/PD model improves
upon a state-of-the-art model with respect to metrics for temporal prediction.
Furthermore, by incorporating key PK/PD concepts into its architecture, the
model can generalize and enable the simulations of patient responses to
untested dosing regimens. These results demonstrate the potential of
neural-PK/PD for automated predictive analytics of patient response time
course.

34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex