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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

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EPEE: towards efficient and effective foundation models in biomedicine.

Zaifu Zhan1,2, Shuang Zhou2, Huixue Zhou3

  • 1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN USA.

Npj Health Systems
|May 15, 2026
PubMed
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We developed EPEE (Entropy- and Patience-based Early Exiting), a new method to speed up foundation models in healthcare. EPEE significantly cuts down inference time while keeping accuracy high for real-time clinical applications.

Keywords:
Computational biology and bioinformaticsHealth careMathematics and computing

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Area of Science:

  • Biomedical informatics
  • Artificial intelligence in healthcare
  • Machine learning for clinical decision support

Background:

  • Foundation models (e.g., GPT, CLIP) show promise in biomedical tasks.
  • High inference latency and "overthinking" hinder real-time clinical use of these models.

Purpose of the Study:

  • To introduce EPEE (Entropy- and Patience-based Early Exiting), a hybrid strategy to enhance foundation model inference efficiency.
  • To address the trade-off between efficiency and effectiveness in biomedical foundation models.

Main Methods:

  • Developed EPEE, combining entropy-based and patience-based early exiting strategies.
  • Evaluated EPEE on classification, relation extraction, and event extraction tasks.
  • Tested EPEE across eight foundation models (BERT, ALBERT, GPT-2, ViT, Qwen, GPT-oss, BioMistral, Meditron3) and twelve diverse datasets (clinical notes, medical images).

Main Results:

  • EPEE significantly reduced inference time across all tested models and tasks.
  • Accuracy was maintained or improved with EPEE compared to standard inference.
  • Demonstrated adaptability of EPEE to various biomedical datasets and tasks.

Conclusions:

  • EPEE effectively balances efficiency and effectiveness for biomedical foundation models.
  • EPEE offers a practical solution for real-time clinical decision-making.
  • This approach supports reliable and efficient clinical workflows using advanced AI models.