arXiv subjects

Ying Ding

Publications and source records attributed to Ying Ding.

At least 55 records · Page 3Linked to original sources

arXiv2025

GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing

Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LLMs direct the discourse and steer the conversation's objectives-remains under-explored. In this study, we first characterize LLM-guided conversation into three fundamental components: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GuideLLM as an installation. We then implement an interviewing environment for the evaluation of LLM-guided conversation. Specifically, various topics are involved in this environment for comprehensive interviewing evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over 200 events mentioned during the interviewing for each chatbot evaluation. We compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and Llama-3-70b-Instruct, from the perspective of interviewing quality, and autobiography generation quality. For automatic evaluation, we derive user proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM behaviors. We further conduct a human-involved experiment by employing 45 human participants to chat with GuideLLM and baselines. We then collect human feedback, preferences, and ratings regarding the qualities of conversation and autobiography. Experimental results indicate that GuideLLM significantly outperforms baseline LLMs in automatic evaluation and achieves consistent leading performances in human ratings.

cs.CL↗

arXiv2025

LLM-TA: An LLM-Enhanced Thematic Analysis Pipeline for Transcripts from Parents of Children with Congenital Heart Disease

Thematic Analysis (TA) is a fundamental method in healthcare research for analyzing transcript data, but it is resource-intensive and difficult to scale for large, complex datasets. This study investigates the potential of large language models (LLMs) to augment the inductive TA process in high-stakes healthcare settings. Focusing on interview transcripts from parents of children with Anomalous Aortic Origin of a Coronary Artery (AAOCA), a rare congenital heart disease, we propose an LLM-Enhanced Thematic Analysis (LLM-TA) pipeline. Our pipeline integrates an affordable state-of-the-art LLM (GPT-4o mini), LangChain, and prompt engineering with chunking techniques to analyze nine detailed transcripts following the inductive TA framework. We evaluate the LLM-generated themes against human-generated results using thematic similarity metrics, LLM-assisted assessments, and expert reviews. Results demonstrate that our pipeline outperforms existing LLM-assisted TA methods significantly. While the pipeline alone has not yet reached human-level quality in inductive TA, it shows great potential to improve scalability, efficiency, and accuracy while reducing analyst workload when working collaboratively with domain experts. We provide practical recommendations for incorporating LLMs into high-stakes TA workflows and emphasize the importance of close collaboration with domain experts to address challenges related to real-world applicability and dataset complexity. https://github.com/jiaweixu98/LLM-TA

cs.CL↗

arXiv2025

Decoding Patterns of Data Generation Teams for Clinical and Scientific Success: Insights from the Bridge2AI Talent Knowledge Graph

High-quality biomedical datasets are essential for medical research and disease treatment innovation. The NIH-funded Bridge2AI project strives to facilitate such innovations by uniting top-tier, diverse teams to curate datasets designed for AI-driven biomedical research. We examined 1,699 dataset papers from the Nucleic Acids Research (NAR) database issues and the Bridge2AI Talent Knowledge Graph. By treating each paper's authors as a team, we explored the relationship between team attributes (team power and fairness) and dataset paper quality, measured by scientific impact (Relative Citation Ratio percentile) and clinical translation power (APT, likelihood of citation by clinical trials and guidelines). Utilizing the SHAP explainable AI framework, we identified correlations between team attributes and the success of dataset papers in both citation impact and clinical translation. Key findings reveal that (1) PI (Principal Investigator) leadership and team academic prowess are strong predictors of dataset success; (2) team size and career age are positively correlated with scientific impact but show inverse patterns for clinical translation; and (3) higher female representation correlates with greater dataset success. Although our results are correlational, they offer valuable insights into forming high-performing data generation teams. Future research should incorporate causal frameworks to deepen understanding of these relationships.

cs.DL↗

arXiv2025

Position: Open and Closed Large Language Models in Healthcare

This position paper analyzes the evolving roles of open-source and closed-source large language models (LLMs) in healthcare, emphasizing their distinct contributions and the scientific community's response to their development. Due to their advanced reasoning capabilities, closed LLMs, such as GPT-4, have dominated high-performance applications, particularly in medical imaging and multimodal diagnostics. Conversely, open LLMs, like Meta's LLaMA, have gained popularity for their adaptability and cost-effectiveness, enabling researchers to fine-tune models for specific domains, such as mental health and patient communication.

cs.CY↗

arXiv2025

Demo: Interactive Visualization of Semantic Relationships in a Biomedical Project's Talent Knowledge Graph

We present an interactive visualization of the Cell Map for AI Talent Knowledge Graph (CM4AI TKG), a detailed semantic space comprising approximately 28,000 experts and 1,000 datasets focused on the biomedical field. Our tool leverages transformer-based embeddings, WebGL visualization techniques, and generative AI, specifically Large Language Models (LLMs), to provide a responsive and user-friendly interface. This visualization supports the exploration of around 29,000 nodes, assisting users in identifying potential collaborators and dataset users within the health and biomedical research fields. Our solution transcends the limitations of conventional graph visualization tools like Gephi, particularly in handling large-scale interactive graphs. We utilize GPT-4o to furnish detailed justifications for recommended collaborators and dataset users, promoting informed decision-making. Key functionalities include responsive search and exploration, as well as GenAI-driven recommendations, all contributing to a nuanced representation of the convergence between biomedical and AI research landscapes. In addition to benefiting the Bridge2AI and CM4AI communities, this adaptable visualization framework can be extended to other biomedical knowledge graphs, fostering advancements in medical AI and healthcare innovation through improved user interaction and data exploration. The demonstration is available at: https://jiawei-alpha.vercel.app/.

cs.SI↗

arXiv2024

Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue

The degraded performance and group unfairness caused by confounding sensitive attributes in rumor detection remains relatively unexplored. To address this, we propose a two-step framework. Initially, it identifies confounding sensitive attributes that limit rumor detection performance and cause unfairness across groups. Subsequently, we aim to learn equally informative representations through invariant learning. Our method considers diverse sets of groups without sensitive attribute annotations. Experiments show our method easily integrates with existing rumor detectors, significantly improving both their detection performance and fairness.

cs.SI↗

arXiv2024

Know2Vec: A Black-Box Proxy for Neural Network Retrieval

For general users, training a neural network from scratch is usually challenging and labor-intensive. Fortunately, neural network zoos enable them to find a well-performing model for directly use or fine-tuning it in their local environments. Although current model retrieval solutions attempt to convert neural network models into vectors to avoid complex multiple inference processes required for model selection, it is still difficult to choose a suitable model due to inaccurate vectorization and biased correlation alignment between the query dataset and models. From the perspective of knowledge consistency, i.e., whether the knowledge possessed by the model can meet the needs of query tasks, we propose a model retrieval scheme, named Know2Vec, that acts as a black-box retrieval proxy for model zoo. Know2Vec first accesses to models via a black-box interface in advance, capturing vital decision knowledge from models while ensuring their privacy. Next, it employs an effective encoding technique to transform the knowledge into precise model vectors. Secondly, it maps the user's query task to a knowledge vector by probing the semantic relationships within query samples. Furthermore, the proxy ensures the knowledge-consistency between query vector and model vectors within their alignment space, which is optimized through the supervised learning with diverse loss functions, and finally it can identify the most suitable model for a given task during the inference stage. Extensive experiments show that our Know2Vec achieves superior retrieval accuracy against the state-of-the-art methods in diverse neural network retrieval tasks.

cs.LG↗

arXiv2024

Are Large Language Models Useful for Time Series Data Analysis?

Time series data plays a critical role across diverse domains such as healthcare, energy, and finance, where tasks like classification, anomaly detection, and forecasting are essential for informed decision-making. Recently, large language models (LLMs) have gained prominence for their ability to handle complex data and extract meaningful insights. This study investigates whether LLMs are effective for time series data analysis by comparing their performance with non-LLM-based approaches across three tasks: classification, anomaly detection, and forecasting. Through a series of experiments using GPT4TS and autoregressive models, we evaluate their performance on benchmark datasets and assess their accuracy, precision, and ability to generalize. Our findings indicate that while LLM-based methods excel in specific tasks like anomaly detection, their benefits are less pronounced in others, such as forecasting, where simpler models sometimes perform comparably or better. This research highlights the role of LLMs in time series analysis and lays the groundwork for future studies to systematically explore their applications and limitations in handling temporal data.

cs.LG↗

arXiv2024

Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare

Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formulation for graph embeddings, focusing on invariance with respect to sensitive SDoH information. Via employing a heterogeneous-GCN model for drug-disease link prediction, we detect biases related to various SDoH factors. To mitigate these biases, we propose a post-processing method that strategically reweights edges connected to SDoHs, balancing their influence on graph representations. This approach represents one of the first comprehensive investigations into fairness issues within biomedical knowledge graphs incorporating SDoH. Our work not only highlights the importance of considering SDoH in medical informatics but also provides a concrete method for reducing SDoH-related biases in link prediction tasks, paving the way for more equitable healthcare recommendations. Our code is available at \url{https://github.com/hwq0726/SDoH-KG}.

cs.AI↗

arXiv2024

Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering

Accurate diagnosis and prognosis assisted by pathology images are essential for cancer treatment selection and planning. Despite the recent trend of adopting deep-learning approaches for analyzing complex pathology images, they fall short as they often overlook the domain-expert understanding of tissue structure and cell composition. In this work, we focus on a challenging Open-ended Pathology VQA (PathVQA-Open) task and propose a novel framework named Path-RAG, which leverages HistoCartography to retrieve relevant domain knowledge from pathology images and significantly improves performance on PathVQA-Open. Admitting the complexity of pathology image analysis, Path-RAG adopts a human-centered AI approach by retrieving domain knowledge using HistoCartography to select the relevant patches from pathology images. Our experiments suggest that domain guidance can significantly boost the accuracy of LLaVA-Med from 38% to 47%, with a notable gain of 28% for H&E-stained pathology images in the PathVQA-Open dataset. For longer-form question and answer pairs, our model consistently achieves significant improvements of 32.5% in ARCH-Open PubMed and 30.6% in ARCH-Open Books on H\&E images. Our code and dataset is available here (https://github.com/embedded-robotics/path-rag).

cs.CV↗

arXiv2024

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature

Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. In this work, we introduce DALK, a.k.a. Dynamic Co-Augmentation of LLMs and KG, to address this limitation and demonstrate its ability on studying Alzheimer's Disease (AD), a specialized sub-field in biomedicine and a global health priority. With a synergized framework of LLM and KG mutually enhancing each other, we first leverage LLM to construct an evolving AD-specific knowledge graph (KG) sourced from AD-related scientific literature, and then we utilize a coarse-to-fine sampling method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the KG to augment LLM inference capabilities. The experimental results, conducted on our constructed AD question answering (ADQA) benchmark, underscore the efficacy of DALK. Additionally, we perform a series of detailed analyses that can offer valuable insights and guidelines for the emerging topic of mutually enhancing KG and LLM. We will release the code and data at https://github.com/David-Li0406/DALK.

cs.CL↗

arXiv2024

SDoH-GPT: Using Large Language Models to Extract Social Determinants of Health (SDoH)

Extracting social determinants of health (SDoH) from unstructured medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing. In this study we introduced SDoH-GPT, a simple and effective few-shot Large Language Model (LLM) method leveraging contrastive examples and concise instructions to extract SDoH without relying on extensive medical annotations or costly human intervention. It achieved tenfold and twentyfold reductions in time and cost respectively, and superior consistency with human annotators measured by Cohen's kappa of up to 0.92. The innovative combination of SDoH-GPT and XGBoost leverages the strengths of both, ensuring high accuracy and computational efficiency while consistently maintaining 0.90+ AUROC scores. Testing across three distinct datasets has confirmed its robustness and accuracy. This study highlights the potential of leveraging LLMs to revolutionize medical note classification, demonstrating their capability to achieve highly accurate classifications with significantly reduced time and cost.

cs.CL↗

arXiv2024

All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks

Graph Neural Networks (GNNs) have attracted immense attention in the past decade due to their numerous real-world applications built around graph-structured data. On the other hand, Large Language Models (LLMs) with extensive pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. In this paper, we investigate how LLMs can be leveraged in a computationally efficient fashion to benefit rich graph-structured data, a modality relatively unexplored in LLM literature. Prior works in this area exploit LLMs to augment every node features in an ad-hoc fashion (not scalable for large graphs), use natural language to describe the complex structural information of graphs, or perform computationally expensive finetuning of LLMs in conjunction with GNNs. We propose E-LLaGNN (Efficient LLMs augmented GNNs), a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph. More specifically, E-LLaGNN relies on sampling high-quality neighborhoods using LLMs, followed by on-demand neighborhood feature enhancement using diverse prompts from our prompt catalog, and finally information aggregation using message passing from conventional GNN architectures. We explore several heuristics-based active node selection strategies to limit the computational and memory footprint of LLMs when handling millions of nodes. Through extensive experiments & ablation on popular graph benchmarks of varying scales (Cora, PubMed, ArXiv, & Products), we illustrate the effectiveness of our E-LLaGNN framework and reveal many interesting capabilities such as improved gradient flow in deep GNNs, LLM-free inference ability etc.

cs.LG↗

arXiv2024

How high-status women promote repeated collaboration among women in male-dominated contexts

Male-dominated contexts pose a dilemma: they increase the benefits of repeated collaboration among women, yet at the same time, make such collaborations less likely. This paper seeks to understand the conditions that foster repeated collaboration among women versus men in male-dominated settings by examining the critical role of status hierarchies. Using collaboration data on 8,232,769 computer science research teams, we found that when a woman holds the top-ranking position in a steep status hierarchy, other women on that team are more likely than men to collaborate again, as compared to when the hierarchy is flat, and compared to when men occupy the top-ranking position. In steep hierarchies, top-ranking women but not top-ranking men foster conditions in which junior women are more likely to collaborate again than junior men of similar status levels. Our research suggests that whereas status hierarchies are especially detrimental to repeated collaboration among underrepresented individuals, top-ranking women in steep status hierarchies mitigate these negative impacts between women in male-dominated settings.

cs.CY↗

arXiv2024

A pseudo-outcome-based framework to analyze treatment heterogeneity in survival data using electronic health records

An important aspect of precision medicine focuses on characterizing diverse responses to treatment due to unique patient characteristics, also known as heterogeneous treatment effects (HTE), and identifying beneficial subgroups with enhanced treatment effects. Estimating HTE with right-censored data in observational studies remains challenging. In this paper, we propose a pseudo-outcome-based framework for analyzing HTE in survival data, which includes a list of meta-learners for estimating HTE, a variable importance metric for identifying predictive variables to HTE, and a data-adaptive procedure to select subgroups with enhanced treatment effects. We evaluate the finite sample performance of the framework under various settings of observational studies. Furthermore, we applied the proposed methods to analyze the treatment heterogeneity of a Written Asthma Action Plan (WAAP) on time-to-ED (Emergency Department) return due to asthma exacerbation using a large asthma electronic health records dataset with visit records expanded from pre- to post-COVID-19 pandemic. We identified vulnerable subgroups of patients with poorer asthma outcomes but enhanced benefits from WAAP and characterized patient profiles. Our research provides valuable insights for healthcare providers on the strategic distribution of WAAP, particularly during disruptive public health crises, ultimately improving the management and control of pediatric asthma.

stat.ME↗

arXiv2024

Effects of Same-Race Mentorship Preferences on Academic Performance and Survival

Same-race mentorship preference refers to mentors or mentees forming connections significantly influenced by a shared race. Although racial diversity in science has been well-studied and linked to favorable outcomes, the extent and effects of same-race mentorship preferences remain largely underexplored. Here, we analyze 465,355 mentor-mentee pairs from more than 60 research areas over the last 70 years to investigate the effect of same-race mentorship preferences on mentees' academic performance and survival. We use causal inference and statistical matching to measure same-race mentorship preferences while accounting for racial demographic variations across institutions, time periods, and research fields. Our findings reveal a pervasive same-race mentorship propensity across races, fields, and universities of varying research intensity. We observe an increase in same-race mentorship propensity over the years, further reinforced inter-generationally within a mentorship lineage. This propensity is more pronounced for minorities (Asians, Blacks, and Hispanics). Our results reveal that mentees under the supervision of mentors with high same-race propensity experience significantly lower productivity, impact, and collaboration reach during and after training, ultimately leading to a 27.6% reduced likelihood of remaining in academia. In contrast, a mentorship approach devoid of racial propensity appears to offer the best prospects for academic performance and persistence. These findings underscore the importance of mentorship diversity for academic success and shed light on factors contributing to minority underrepresentation in science.

cs.SI↗

arXiv2024

Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our results showed that incorporating the target state's data into training the suicide-crisis classifier brought an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. To conclude, we demonstrated the annotation inconsistencies in NVDRS's death investigation notes, identified problematic instances, evaluated the effectiveness of correcting problematic instances, and eventually proposed an NLP improvement solution.

cs.CL↗

arXiv2024

tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction

The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the non-linear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network (CNN) with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is a large AMD study, the Age-Related Eye Disease Study (AREDS), in which more than 50,000 fundus images were captured over a period of 12 years for more than 4,000 participants. Another is a public dataset of the primary biliary cirrhosis (PBC) disease, where multiple lab tests were longitudinally collected to predict the time-to-liver transplant. Our approach demonstrates commendable predictive performance in both simulation studies and the analysis of the two real datasets.

stat.ML↗