Author name cluster

Huijie Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

JBHI Journal 2026 Journal Article

Value Decomposition-Based Multi-Agent Learning for Anesthetics Collaborative Control

Huijie Li
Yide Yu
Si Shi
Anmin Hu
Jian Huo
Wei Lin
Chaoran Wu
Wuman Luo

Automated control of personalized multiple anesthetics in clinical Total Intravenous Anesthesia (TIVA) is crucial yet challenging. Current systems, including target-controlled infusion (TCI) and closed-loop systems, either rely on relatively static pharmacokinetic/pharmacodynamic (PK/PD) models or focus on single anesthetic control. So they limit both personalization and collaborative control. To address these issues, we propose a novel V alue D ecomposition M ulti- A gent D eep R einforcement L earning (VD-MADRL) framework based on Markov Game (MG) for P ersonalized M ultiple A nesthetics C ontrol in a C losed- L oop system (PMAC-CL). VD-MADRL optimizes the collaboration between two anesthetics propofol (Agent I) and remifentanil (Agent II) by leveraging a MG to identify optimal actions among heterogeneous agents. We employ various value function decomposition methods to resolve the credit allocation problem and enhance collaborative control. We also introduce a multivariate environment model based on random forest (RF) for anesthesia state simulation. To ensure data validity, we design a data resampling and alignment technique to synchronize trajectory data from different devices, avoiding gradient explosion and maintaining conformity to Markov property. Extensive experiments on general and thoracic surgery datasets demonstrate that VD-MADRL provides more refined dose adjustments and maintains multiple anesthesia state indicators more stably at target levels compared to human experience. Especially, the best-performing algorithm, VDN in general surgery with online training, achieved a 16. 4% increase in cumulative reward (CR) and a 58. 0% reduction in mean MDPE compared to human experience. This demonstrates its great clinical value.

Details DOI

JBHI Journal 2025 Journal Article

Multi-Agent Learning for Precise and Collaborative Control of Anesthetics in TIVA

Huijie Li
Kunpeng Liu
Yide Yu
Yuejing Zhai
Anmin Hu
Jian Huo
Wuman Luo

Precise and collaborative control of multiple anesthetics in Total Intravenous Anesthesia (TIVA) is essential for ensuring patient safety and maintaining the target depth of anesthesia (DoA). However, existing automated anesthesia control methods often fail to effectively capture the complex synergistic interactions between anesthetics and lack adaptability to patient-specific physiological variability, thereby limiting their clinical applicability. To address these issues, we propose AnesMADRL, a novel Multi-Agent Deep Reinforcement Learning (MADRL)-based framework that leverages the Counterfactual Multi-Agent algorithm for effective credit assignment between agents controlling propofol and remifentanil, and adopts a continuous action space to enable fine-grained dose adjustments. Furthermore, AnesMADRL integrates comprehensive patient-specific physiological indicators and employs a random forest-based simulator to generate dynamic and diverse training environments. Experimental results show that AnesMADRL significantly outperforms baseline methods and human expertise in terms of anesthetic efficiency and total drug consumption. Relative to human expertise, AnesMADRL achieves roughly twofold efficiency while reducing total dose to about one-half, highlighting its potential to enhance patient safety and optimize clinical outcomes.

Details DOI

YNIMG Journal 2019 Journal Article

Uncovering cortical activations of discourse comprehension and their overlaps with common large-scale neural networks

XiaoHong Yang
Huijie Li
Nan Lin
XiuPing Zhang
YinShan Wang
Ying Zhang
Qian Zhang
XiNian Zuo

We conducted a meta-analysis of 78 task-based functional magnetic resonance imaging (fMRI) studies (1976 total participants) to reveal underlying brain activations and their overlap with large-scale neural networks in the brain during general discourse comprehension and its sub-processes. We found that discourse comprehension involved a neural system consisting of widely distributed brain regions that comprised not only the bilateral perisylvian language zones, but also regions in the superior and medial frontal cortex and the medial temporal lobe. Moreover, this neural system can be categorized into several sub-systems representing various sub-processes of discourse comprehension, with the left inferior frontal gyrus and middle temporal gyrus serving as core regions across all sub-processes. At a large-scale network level, we found that discourse comprehension relied most heavily on the default network, particularly on its dorsal medial subsystem. The pattern associated with large-scale network cooperation varied according to the respective sub-processes required. Our results reveal the functional dissociation within the discourse comprehension neural system and highlight the flexible involvements of large-scale networks.

Details DOI