Author name cluster

Yi Guan

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.

11 papers

1 author row

AAAI Conference 2026 Conference Paper

AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models

Lian Yan
Haotian Wang
Chen Tang
Haifeng Liu
Tianyang Sun
Liangliang Liu
Yi Guan
Jingchi Jiang

n the agricultural domain, the deployment of large language models (LLMs) is hindered by the lack of training data and evaluation benchmarks. To mitigate this issue, we propose AgriEval, the first comprehensive Chinese agricultural benchmark with three main characteristics: (1) Comprehensive Capability Evaluation. AgriEval covers six major agriculture categories and 29 subcategories within agriculture, addressing four core cognitive scenarios—memorization, understanding, inference, and generation. (2) High-Quality Data. The dataset is curated from university-level examinations and assignments, providing a natural and robust benchmark for assessing the capacity of LLMs to apply knowledge and make expert-like decisions. (3) Diverse Formats and Extensive Scale. AgriEval comprises 14,697 multiple-choice questions and 2,167 open-ended question-and-answer questions, establishing it as the most extensive agricultural benchmark available to date. We also present comprehensive experimental results over 51 open-source and commercial LLMs. The experimental results reveal that most existing LLMs struggle to achieve 60 percent accuracy, underscoring the developmental potential in agricultural LLMs. Additionally, we conduct extensive experiments to investigate factors influencing model performance and propose strategies for enhancement.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Invariant Feature Learning for Counterfactual Watch-time Prediction in Video Recommendation

Chenghou Jin
Yixin Ren
Hongxu Ma
Yewei Xia
Yi Guan
Hao Zhang
Jiandong Ding
Jihong Guan

Video recommendation systems heavily rely on user watch time feedback, making accurate watch time prediction a crucial task. However, this task inherently suffers from bias, as recommendation models tend to favor long-duration videos to maximize watch time. This issue, known as duration bias in the watch-time prediction context, can be explained from a causal perspective, where video duration acts as a confounder. Recent works address this bias using backdoor adjustment, isolating the direct effect of content on watch time from observational data. These methods typically discretize video duration into groups, estimate group-wise effects, and then aggregate them via a unified prediction model. However, this aggregation strategy is prone to model misspecification due to feature distribution shift across groups. In this paper, we reinterpret the problem through the lens of invariant learning and propose a novel framework: Duration-Invariant Feature Learning (DIFL). DIFL employs a kernel-based regularization that enforces representation invariance across duration groups, reducing sensitivity to group design and improving generalization. This enables more accurate modeling of the direct causal effect and making counterfactual inference. Extensive experiments on both public and real large-scale production datasets demonstrate the effectiveness of our approach, which achieves SOTA performance.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Dialogues Are Not Just Text: Modeling Cognition for Dialogue Coherence Evaluation

Xue Li
Jia Su
Yang Yang
Zipeng Gao
Xinyu Duan
Yi Guan

The generation of logically coherent dialogues by humans relies on underlying cognitive abilities. Based on this, we redefine the dialogue coherence evaluation process, combining cognitive judgment with the basic text to achieve a more human-like evaluation. We propose a novel dialogue evaluation framework based on Dialogue Cognition Graph (DCGEval) to implement the fusion by in-depth interaction between cognition modeling and text modeling. The proposed Abstract Meaning Representation (AMR) based graph structure called DCG aims to uniformly model four dialogue cognitive abilities. Specifically, core-semantic cognition is modeled by converting the utterance into an AMR graph, which can extract essential semantic information without redundancy. The temporal and role cognition are modeled by establishing logical relationships among the different AMR graphs. Finally, the commonsense knowledge from ConceptNet is fused to express commonsense cognition. Experiments demonstrate the necessity of modeling human cognition for dialogue evaluation, and our DCGEval presents stronger correlations with human judgments compared to other state-of-the-art evaluation metrics.

PDF Details DOI

JBHI Journal 2024 Journal Article

EIRAD: An Evidence-Based Dialogue System With Highly Interpretable Reasoning Path for Automatic Diagnosis

Lian Yan
Yi Guan
Haotian Wang
Yi Lin
Yang Yang
Boran Wang
Jingchi Jiang

Dialogue System for Medical Diagnosis (DSMD) based on reinforcement learning (RL) can simulate patient-doctor interactions, playing a crucial role in clinical diagnosis. However, due to the complexity of disease etiology, DSMD faces the challenges of low efficiency in diagnostic evidence search. Moreover, solely RL-based DSMS, without the constraints of professional medical knowledge, often generates irrational, meaningless, or even erroneous symptom inquiries, leading to poor interpretability of diagnostic path and high misdiagnosis rates. To address these issues, we propose an E vidence-based dialogue system with highly I nterpretable R easoning path for A utomatic D iagnosis (EIRAD) grounded in medical knowledge graph (MKG). Specifically, our automated diagnostic model captures key symptoms for suspected diseases by explicitly leveraging the topology of MKG, enhancing the interpretability and accuracy of diagnosis. To expedite the retrieval of factual evidence, we develop two mechanisms: 1) Mapping mechanism between the entity set of MKG and DSMD's diagnostic evidence and diseases. According to the patient's symptoms, EIRAD prunes irrelevant disease and symptom nodes from the MKG, which can truncate the invalid action of RL-based DSMD. 2) Reward Mechanism of integrating the effectiveness of symptom inquiry and the accuracy of disease diagnosis. The comprehensive reward system is suitable for intelligent consultation, which can effectively drive DSMD to accelerate evidence collection. Experimental results demonstrate that our model significantly outperforms competitive benchmark methods in symptom inquiry efficiency and diagnostic accuracy.

Details DOI

AIIM Journal 2023 Journal Article

DECAF: An interpretable deep cascading framework for ICU mortality prediction

Jingchi Jiang
Xuehui Yu
Boran Wang
Linjiang Ma
Yi Guan

Medical risk detection is an important topic and a challenging task to improve the performance of clinical practices in Intensive Care Units (ICU). Although many bio-statistical learning and deep learning approaches have provided patient-specific mortality predictions, these existing methods lack interpretability that is crucial to gain adequate insight on why such predictions would work. In this paper, we introduce cascading theory to model the physiological domino effect and provide a novel approach to dynamically simulate the deterioration of patients' conditions. We propose a general DEep CAscading Framework (DECAF) to predict the potential risks of all physiological functions at each clinical stage. Compared with other feature-based and/or score-based models, our approach has a range of desirable properties, such as being interpretable, applicable with multi prediction tasks, and learnable from medical common sense and/or clinical experience knowledge. Experiments on a medical dataset (MIMIC-III) of 21, 828 ICU patients show that DECAF reaches up to 89. 30 % on AUROC, which surpasses the best competing methods for mortality prediction.

Details DOI

AIIM Journal 2022 Journal Article

Gated Tree-based Graph Attention Network (GTGAT) for medical knowledge graph reasoning

Jingchi Jiang
Tao Wang
Boran Wang
Linjiang Ma
Yi Guan

Knowledge graph (KG) is a multi-relational data that has proven valuable for many tasks including decision making and semantic search. In this paper, we present GTGAT (Gated Tree-based Graph Attention), a method for tackling the problems of transductive and inductive reasoning in generalized KGs. Based on recent advancement of graph attention network (GAT), we develop a gated tree-based method to distill valuable information in neighborhood via hierarchical-aware and semantic-aware attention mechanism. Our approach not only addresses several key challenges of GAT but is also capable of undertaking multiple downstream tasks. Experimental results have revealed that our proposed GTGAT has matched state-of-the-art approaches across transductive benchmarks on the Cora, Citeseer, and electronic medical record networks (EMRNet). Meanwhile, the inductive experiments on medical knowledge graphs show that GTGAT surpasses the best competing methods for personalized disease diagnosis.

Details DOI

AIIM Journal 2020 Journal Article

Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis

Jing Xie
Jingchi Jiang
Yehan Wang
Yi Guan
Xitong Guo

Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made by MKN-based clinical decision support system depends on the accuracy of medical knowledge and the completeness of the network. However, collecting knowledge is a long-lasting and cumulative process, which means it’s hard to construct a complete MKN with limited data. This study was conducted with the objective of developing an expandable EMR-based MKN to enhance capabilities in making an initial clinical diagnosis. A network of symptom-indicate-disease knowledge in 992 Chinese EMRs (CEMRs) was manually constructed as Original-MKN, and an incremental expansion framework was applied to it to obtain an expandable MKN based on new CEMRs. The framework was composed by: (1) integrating external knowledge extracted from the medical information websites and (2) mining potential knowledge with new EMRs. The framework also adopts a diagnosis-driven learning method to estimate the effectiveness of each knowledge in clinical practice. Experimental results indicate that our expanded MKN achieves a precision of 0. 837 for a recall of 0. 719 in clinical diagnosis, which outperforms Original-MKN and four classical machine learning methods. Furthermore, both external medical knowledge and potential medical knowledge benefit MKN expansion and disease diagnosis. The proposed incremental expansion framework sustains the MKN learning new knowledge.

Details DOI

AIIM Journal 2020 Journal Article

Medical knowledge embedding based on recursive neural network for multi-disease diagnosis

Jingchi Jiang
Huanzheng Wang
Jing Xie
Xitong Guo
Yi Guan
Qiubin Yu

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i. e. , high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the better the performance. The RNKN gradually reveals the interpretation of knowledge embeddings as the number of training epochs increases.

Details DOI

AIIM Journal 2019 Journal Article

Classifying medical relations in clinical text via convolutional neural networks

Bin He
Yi Guan
Rui Dai

Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method.

Details DOI

AIIM Journal 2018 Journal Article

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

Chao Zhao
Jingchi Jiang
Yi Guan
Xitong Guo
Bin He

Objective Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks—test recommendation, initial diagnosis, and treatment plan recommendation—given the condition of a patient. Methods We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e. g. , symptoms), and the other was the condition to be inferred (e. g. , diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. Results As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Conclusion Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions.

Details DOI

AAAI Conference 2014 Conference Paper

Representing Words as Lymphocytes

Jinfeng Yang
Yi Guan
Xishuang Dong
Bin He

Similarity between words is becoming a generic problem for many applications of computational linguistics, and computing word similarities is determined by word representations. Inspired by the analogies between words and lymphocytes, a lymphocyte-style word representation is proposed. The word representation is built on the basis of dependency syntax of sentences and represent word context as head properties and dependent properties of the word. Lymphocyte-style word representations are evaluated by computing the similarities between words, and experiments are conducted on the Penn Chinese Treebank 5. 1. Experimental results indicate that the proposed word representations are effective.

PDF Details