Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance
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30秒速读
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.