Characterizing Treatment Non-responders and Responders in Completed Alzheimer's Disease Clinical Trials

Presentation Time: 08:00 AM - 08:15 AM

Abstract Keywords: Causal Inference, Drug Discoveries, Repurposing, and Side-effect, Machine Learning, Data Mining, Precision Medicine
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Characterizing differential responses to Alzheimer’s disease (AD) drugs will provide better insights into personalized treatment strategies. Our study aims to identify heterogeneous treatment effects and pre-treatment features that moderate the treatment effect of Galantamine, Bapineuzumab, and Semagacestat from completed trial data. The causal forest method has the ability to capture heterogeneity in treatment responses. We applied causal forest modeling to estimate the treatment effect and identify efficacy moderators in each trial. We found several patient’s pre-treatment conditions that determined treatment efficacy. For example, in Galantamine trials, whole brain volume (1092.54 vs. 1060.67 ml, P < .001) and right hippocampal volume (2.43e-3 vs. 2.79e-3, P < .001) are significantly different between responsive and non-responsive subgroups. Overall, our implementation of causal forests in AD clinical trials reveals the heterogeneous treatment effects and different moderators for AD drug responses, highlighting promising personalized treatment based on patient-specific characteristics in AD research and drug development.

Speaker(s):
Dulin Wang, MS
The University of Texas Health Science Center at Houston

Author(s):
Dulin Wang, MS - The University of Texas Health Science Center at Houston; Yaobin Ling, M.S. - University of Texas Health Center; Kristofer Harris, MPH, RN - University of Texas Health Center; Paul Schulz, M.D. - University of Texas Health Center; Xiaoqian Jiang, PhD - University of Texas Health Science Center at Houston; Yejin Kim, PhD - The University of Texas Health Science Center at Houston;

An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer’s Disease Using UK Biobank

Presentation Time: 08:15 AM - 08:30 AM

Abstract Keywords: Bioinformatics, Deep Learning, Biomarkers, Data Mining
Primary Track: Foundations
Programmatic Theme: Clinical Informatics

Alzheimer's disease (AD) manifests with varying progression rates across individuals, necessitating the understanding of their intricate patterns of cognition decline that could contribute to effective strategies for risk monitoring. In this study, we propose an innovative interpretable population graph network framework for identifying rapid progressors of AD by utilizing patient information from electronic health-related records in the UK Biobank. To achieve this, we first created a patient similarity graph, in which each AD patient is represented as a node; and an edge is established by patient clinical characteristics distance. We used graph neural networks (GNNs) to predict rapid progressors of AD and created a GNN Explainer with SHAP analysis for interpretability. The proposed model demonstrates superior predictive performance over the existing benchmark approaches. We also revealed several clinical features significantly associated with the prediction, which can be used to aid in effective interventions for the progression of AD patients.

Speaker(s):
Weimin Meng, Master of Science
University of Florida

Author(s):
Weimin Meng, Master of Science - University of Florida; Rohit Inampudi, Undergraduate Student - Department of Computer Science and Engineering, University of Florida, Gainesville, FL, USA; Xiang Zhang, PhD - College of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, US; Jie Xu, PhD - University of Florida; Yu Huang, Ph.D.; Mingyi Xie, PhD - Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA; Jiang Bian, PhD - University of Florida; Rui Yin, PhD - University of Florida;

Stratification of Alzheimer’s Disease Patients Using Knowledge-Guided Unsupervised Latent Factor Clustering with Electronic Health Records Data

Presentation Time: 08:30 AM - 08:45 AM

Abstract Keywords: Machine Learning, Knowledge Representation and Information Modeling, Personal Health Informatics, Population Health, Aging in Place
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

In this work, we effectively stratified Alzheimer's disease patients at the time of AD diagnosis using a latent factor clustering model guided by knowledge-graph embeddings of EHR concepts. We evaluated the identified patient clusters in terms of the predictive value of two clinically relevant outcomes: time to nursing home admission and time to death, and investigated demographic and clinical characteristics of the clusters. Such patient stratification can enable better prognosis and disease management for AD patients.

Speaker(s):
Linshanshan Wang, MA Biostatistics
Harvard University

Author(s):
Shruthi Venkatesh, MS - University of Pittsburgh School of Medicine|; Zongqi Xia, MD, PhD - University of Pittsburgh School of Medicine; Tianxi Cai, ScD - Harvard University;

Exposure to autoimmune disorders increases Alzheimer's disease risk and accelerates disease onset in an electronic health record analysis

Presentation Time: 08:45 AM - 09:00 AM

Abstract Keywords: Bioinformatics, Population Health, Personal Health Informatics, Biomarkers, Infectious Diseases and Epidemiology, Data Mining, Causal Inference, Omics (genomics, metabolomics, proteomics, transcriptomics, etc.) and Integrative Analyses
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

The relationship between immune dysregulation and Alzheimer’s Disease (AD) is not well understood at the clinical phenotype level in humans. We quantified AD risk associations with a clinical manifestation of immune dysregulation, autoimmune disorders, and we found that autoimmune patients exhibit significantly increased Alzheimer’s Disease risk in multiple epidemiological study designs across two electronic health record databases. Identifying this risk provides valuable insight into AD modifiable risk factors and the neuroimmune interface for patient care.

Speaker(s):
Grace Ramey, BS
University of California, San Francisco

Author(s):
Grace Ramey, BS - University of California, San Francisco; Alice Tang, PhD - UCSF; Thanaphong Phongpreecha, PhD - Stanford University; Monica Yang, MD - University of California, San Francisco; Sarah Woldemariam - UCSF; Tomiko Oskotsky, MD - UCSF; Isabel Allen, PhD - University of California, San Francisco; Zachary Miller, MD - University of California, San Francisco; Nima Aghaeepour - Stanford University; John Capra, PhD - University of California, San Francisco; Marina Sirota, PhD - University of California, San Francisco;

Cross-Modal Retrieval for Alzheimer's Disease Diagnosis Using CLIP-Enhanced Dual Deep Hashing

Presentation Time: 09:00 AM - 09:15 AM

Abstract Keywords: Clinical Decision Support, Information Retrieval, Machine Learning, Deep Learning
Working Group: Clinical Decision Support Working Group
Primary Track: Foundations
Programmatic Theme: Clinical Informatics

Cross-modal data retrieval is crucial for effectively utilizing the vast amount of multimodal data available in healthcare. However, existing methods often fail to capture the intricate semantic relationships between modalities, limiting their retrieval accuracy. In this study, we propose an Unsupervised Dual Deep Hashing (UDDH) method with a CLIP (Contrastive Language-Image Pre-training) mechanism to align semantic meanings across modalities for enhanced cross-modal retrieval. The UDDH framework employs a dual hashing scheme consisting of a semantic index (head code) and content codes (tail code) to capture both high-level semantics and modality-specific details. We also integrate CLIP loss directly between the modality-specific content codes to improve semantic coherence and retrieval precision. The proposed model is evaluated on the Wikipedia image-text dataset for an object retrieval task and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset for a patient diagnosis task. By incorporating category contrastive loss during supervised fine-tuning, our UDDH-clip-ccl model achieves a map@50 score of 0.7984 and an accuracy of 0.7958. The retrieved patient exemplars are used in a Weighted K-nearest neighbors classifier to provide interpretable diagnostic insights based on similar cases. Our approach demonstrates the importance of semantic alignment in cross-modal retrieval and its potential for enhancing patient diagnosis, outcome prediction, and treatment planning by leveraging multimodal data.

Speaker(s):
Xiang Li, PhD
Massachusetts General Hospital and Harvard Medical School

Author(s):
Xiaoke Huang, Master Degree; Bin Zhang, PhD - Guangdong Institute of Intelligence Science and Technology, Zhuhai, Guangdong, China; Wenxiong Liao, PhD - South China University of Technology; Fang Zeng, PhD - Massachusetts General Hospital, Boston, MA, USA; Hui Ren, MD PhD MPH - Massachusetts General Hospital; Zhengliang Liu, PhD - University of Georgia, Athens, GA, USA; Haixing Dai, PhD - University of Georgia, Athens, GA, USA; Zihao Wu, PhD - University of Georgia, Athens, GA, USA; Tianming Liu, PhD - University of Georgia, Athens, GA, USA; Hongmin Cai, PhD - South China University of Technology, Guangzhou, Guangdong, China; Xiang Li, PhD - Massachusetts General Hospital and Harvard Medical School;