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Chang Lu

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6 papers

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JBHI Journal 2026 Journal Article

Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning

Pengfei Hu
Chang Lu
Fei Wang
Yue Ning

Despite the growing use of Electronic Health Records (EHR) for AI-assisted diagnosis prediction, most data-driven models struggle to incorporate clinically meaningful medical knowledge. They often rely on limited ontologies, lacking structured reasoning capabilities and comprehensive coverage. This raises an important research question: Will medical knowledge improve predictive models to support stepwise clinical reasoning as performed by human doctors? To address this problem, we propose DuaLK, a dual-expertise framework that combines two complementary sources of information. For external knowledge, we construct a Diagnosis Knowledge Graph (KG) that encodes both hierarchical and semantic relations enriched by large language models (LLM). To align with patient data, we further introduce a lab-informed proxy task that guides the model to follow a clinically consistent, stepwise reasoning process based on lab test signals. Experimental results on two public EHR datasets demonstrate that DuaLK consistently outperforms existing baselines across four clinical prediction tasks. These findings highlight the potential of combining structured medical knowledge with individual-level clinical signals to achieve more accurate and interpretable diagnostic predictions. The source code is publicly available on https://github.com/humphreyhuu/DuaLK.

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JBHI Journal 2026 Journal Article

EAP-LSTM: A Bi-LSTM-Based Deep Learning Framework for Quantitatively Predicting Enhancer Activity in Drosophila and Human Cell Lines

Yao Zhang
Lichang Dai
Yu Dou
Xin Li
Chang Lu
Hao Wu

Enhancer activity plays a critical role in gene regulation, influencing various biological processes such as development and disease progression. Accurate prediction of enhancer activity is essential for understanding the mechanisms underlying gene regulation and enhancer function. This study introduces a novel deep learning framework, EAP-LSTM (Enhancer Activity Prediction based on Bi-LSTM), to quantitatively predict enhancer activity across different species and cell lines. The model integrates multiple feature modules, including Word2Vec-based representations of DNA sequences, reverse complement k-mer, mismatch k-mer features, and epigenomic data. Evaluated on six cell lines, including five human cell lines (A549, HCT116, HepG2, K562, and MCF-7) and one Drosophila cell line (S2), EAP-LSTM consistently outperforms state-of-the-art models, such as DeepSTARR and HEAP, in all datasets. For example, on the K562 dataset, EAP-LSTM achieves a Pearson correlation coefficient (PCC) of 0. 7944, outperforming DeepSTARR and HEAP by 13. 65% and 2. 73%, respectively. In addition, EAP-LSTM demonstrates strong performance in small-sample learning scenarios, showing clear improvements compared with baseline models. Furthermore, the study investigates the role of transcription factor binding sites (TFBSs) within enhancer regions, identifying critical motifs associated with enhancer activity. These findings not only improve enhancer prediction accuracy but also provide valuable insights into the molecular mechanisms underlying enhancer function.

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NeurIPS Conference 2025 Conference Paper

LLM-PySC2: Starcraft II learning environment for Large Language Models

Zongyuan Li
Yanan Ni
Runnan Qi
Chang Lu
Lumin Jiang
Xu Xiaojie
Xiangbei Liu
Pengfei Li

The tremendous potential has been demonstrated by large language models (LLMs) in intelligent decision-making problems, with unprecedented capabilities shown across diverse applications ranging from gaming AI systems to complex strategic planning frameworks. However, the StarCraft II platform, which has been widely adopted for validating decision-making algorithms in the past decade, has not yet provided substantial support for this emerging domain. To address issues that LLMs cannot interface with the hundreds of actions of the pysc2 backend and the lack of native support for multi-agent (MA) collaboration, we propose the LLM-PySC2 environment. This is the first environment that offers LLMs the complete pysc2 action space with sufficient multi-modal information and game Wiki knowledge. With an asynchronous query architecture, the environment efficiently interacts with LLMs that maintain a constant latency regardless of the scale of the agents' population. In the experiments, we evaluated LLMs' decision-making performance in both the macro-decision and micro-operation scenarios, with traditional StarCraft II Multi-Agent Challenge (SMAC) tasks and a series of new proposed. Results indicate that LLMs possess the potential to achieve victories in complex scenarios but cannot constantly generate correct decisions, especially in the recovered pysc2 action space and MA settings. Without task-relevant instructions, the pre-trained models suffer from issues such as hallucinations and inefficient collaboration. Our findings suggest that StarCraft II still challenges in the era of large models, revealing that there is a lot to do to develop an advanced LLM decision-making system, and the proposed LLM-PySC2 environment will support future development of LLM-based decision-making solutions.

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NeurIPS Conference 2023 Conference Paper

Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning

Chang Lu
Chandan Reddy
Ping Wang
Yue Ning

Automatic coding of International Classification of Diseases (ICD) is a multi-label text categorization task that involves extracting disease or procedure codes from clinical notes. Despite the application of state-of-the-art natural language processing (NLP) techniques, there are still challenges including limited availability of data due to privacy constraints and the high variability of clinical notes caused by different writing habits of medical professionals and various pathological features of patients. In this work, we investigate the semi-structured nature of clinical notes and propose an automatic algorithm to segment them into sections. To address the variability issues in existing ICD coding models with limited data, we introduce a contrastive pre-training approach on sections using a soft multi-label similarity metric based on tree edit distance. Additionally, we design a masked section training strategy to enable ICD coding models to locate sections related to ICD codes. Extensive experimental results demonstrate that our proposed training strategies effectively enhance the performance of existing ICD coding methods.

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AAAI Conference 2022 Conference Paper

Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

Chang Lu
Tian Han
Yue Ning

With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time reflect hidden patterns that are conducive to prognosis. Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. Specifically, we construct a global disease co-occurrence graph with multiple node properties for disease combinations. We design dynamic subgraphs for each patient’s visit to leverage global and local contexts. We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes. Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events.

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IJCAI Conference 2021 Conference Paper

Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

Chang Lu
Chandan K Reddy
Prithwish Chakraborty
Samantha Kleinberg
Yue Ning

Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep-learning-based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured features. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention manipulating strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.

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