Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance
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IN SHORT: This paper addresses the critical challenge of quantitatively evaluating antibiotic prescribing policies under realistic uncertainty and partial observability, where traditional observational studies are limited by incomplete data and unmeasured confounding factors.
核心创新
- Methodology Introduces a novel 'leaky-balloon' abstraction for modeling antibiotic resistance dynamics, providing a computationally efficient yet biologically plausible representation of resistance accumulation and decay.
- Methodology Implements a modular MDP/POMDP framework with explicit control over observability parameters (noise, bias, delay), enabling systematic study of how information degradation affects optimal prescribing strategies.
- Methodology Provides the first Gymnasium-compatible simulation environment specifically designed for antibiotic stewardship research, bridging computational epidemiology and reinforcement learning communities.
主要结论
- The abx_amr_simulator provides a quantitative framework for evaluating antibiotic prescribing policies, addressing the limitation that observational studies alone cannot directly quantify long-term effects of prescribing interventions.
- The simulator's modular design enables researchers to systematically investigate how specific data deficiencies (noise, bias, delay) impede antibiotic stewardship efforts and assess potential gains from targeted interventions.
- By balancing individual clinical outcomes (λ=0) and community resistance management (λ=1) through configurable reward functions, the framework allows exploration of trade-offs between short-term patient care and long-term public health objectives.
摘要: Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this challenge, we introduce abx_amr_simulator, a Python-based simulation package designed to model antibiotic prescribing and AMR dynamics within a controlled, reinforcement learning (RL)-compatible environment. The simulator allows users to specify patient populations, antibiotic-specific AMR response curves, and reward functions that balance immediate clinical benefit against long-term resistance management. Key features include a modular design for configuring patient attributes, antibiotic resistance dynamics modeled via a leaky-balloon abstraction, and tools to explore partial observability through noise, bias, and delay in observations. The package is compatible with the Gymnasium RL API, enabling users to train and test RL agents under diverse clinical scenarios. From an ML perspective, the package provides a configurable benchmark environment for sequential decision-making under uncertainty, including partial observability induced by noisy, biased, and delayed observations. By providing a customizable and extensible framework, abx_amr_simulator offers a valuable tool for studying AMR dynamics and optimizing antibiotic stewardship strategies under realistic uncertainty.