Understanding the Clinical Modalities Important in NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis
Falls among the elderly and especially those with NeuroDegenerative Disorders (NDD) reduce life expectancy. The purpose of this study is to explore the role of Machine Learning on Electronic Health Records (EHR) data for time-to-event survival analysis prediction of injuries and the role of sensitive attributes, e.g., Race, Ethnicity, and Sex, in these models. We used multiple survival analysis methods on a cohort of 29,045 patients 65 years and older treated at PennMedicine for either NDD, Mild Cognitive Impairment (MCI), or another disease. We compare the algorithms and explore the role of multiple modalities on improving prediction of injuries among NDD patients, specifically medications and laboratory tests. Overall, we found that medication features resulted in either increased Hazard Ratios (HR) or reduced HR depending on the NDD type. We found that being of Black race significantly increased the risk of fall/injury in the models that included only medication and sensitive attribute features. The combined model that used both modalities (medications and laboratory information) removed this relationship between being of Black race and increases in fall/injury. Therefore, we found that combining modalities in these survival models in the prediction of fall/injury risk among NDD and MCI individuals results in findings that are robust to different Racial and Ethnic groups with no biases apparent in our final combined modality results. Furthermore, combining modalities (both medications and laboratory values) improved the survival analysis performance across multiple survival analysis methods, when compared using the C-index.
Learning Objectives
- Understanding how to address biases in Electronic Health Records (EHR) through the inclusion of sensitive attributes in model construction and evaluation
Speaker
- Mary Regina Boland, MA, MPhil, PhD, FAMIA (Saint Vincent College)
SLR: A Modified Logistic Regression Model with Sinkhorn Divergence for Alzheimer’s Disease Classification
Logistic regression is a widely used model in machine learning, particularly as a baseline for binary classification tasks due to its simplicity, effectiveness, and interpretability. It is especially powerful when dealing with categorical features. Despite its advantages, standard logistic regression fails to capture the distributional and geometric structure of data, especially when features are derived from structured spaces like brain imaging. For instance, in Voxel-Based Morphometry (VBM), measurements from distinct brain regions follow a clear spatial organization, which standard logistic regression cannot fully leverage. In this paper, we propose Sinkhorn Logistic Regression (SLR), a variant of logistic regression that incorporates the Sinkhorn divergence as a loss function. This adaptation enables the model to leverage geometric information about the data distribution, enhancing its performance on structured datasets.
Learning Objectives
- Recognize the significance and challenge for early diagnosis and disease progression monitoring of Alzheimer’s disease
- Understand the concept of optimal transport and Sinkhorn divergence
- Explain the development and methodology of a modified logistic regression model that incorporates Sinkhorn divergence
- Apply the modified logistic regression model to classify Alzheimer’s disease and evaluate its effectiveness
Speaker
- Li Shen, Ph.D. (University of Pennsylvania)
Phenotyping Cognitive Presentations in Alzheimer’s Disease: A Deep Clustering Approach
This study applied Deep Fusion Clustering Network (DFCN) to phenotype patients with clinician-diagnosed early-stage Alzheimer's disease (AD). When evaluated with data from the GERAS-US study, DFCN outperformed K-Prototype clustering in identifying patient subgroups with distinct baseline cognitive profiles and differing risks of cognitive decline within three years. These findings suggest that deep clustering techniques like DFCN can potentially enhance our understanding of the heterogeneity in disease progression of early AD.
Learning Objectives
- Learn about the approaches for applying and evaluating deep clustering techniques in phenotyping complex, mixed-type clinical data
- Understand the challenges in clustering complex clinical data and how advanced deep learning models outperform traditional clustering methods in addressing these challenges.
- Learn about the heterogeneity in patients with early-stage Alzheimer's Disease, including differences in impaired cognitive domains and risks of cognitive decline.
Speaker
- Jinying Chen, PhD (Boston University)
Leveraging Social Determinants of Health in Alzheimer’s Research Using LLM-Augmented Literature Mining and Knowledge Graphs
Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals’ risks of developing Alzheimer’s disease (AD) and related dementias. Nevertheless, the etiological mechanisms underlying such relationships remain largely unclear, mainly due to difficulties in collecting relevant information. This study presents a novel, automated framework that leverages recent advancements of large language models (LLM) as well as classic natural language processing techniques to mine SDoH knowledge from extensive literature and to integrate it with AD-related biological entities extracted from the general-purpose knowledge graph PrimeKG. Utilizing graph neural networks, we performed link prediction tasks to evaluate the resultant SDoH-augmented knowledge graph. Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas, offering a new tool for exploring the impact of social determinants on health outcomes. Our code is available at: GitHub Repository.
Learning Objectives
- Understand the importance of social determinants of health (SDoH) in the context of Alzheimer’s disease (AD) and its related dementias (ADRD) as nonmedical risk factors
- Discuss challenges and possible solutions in studying the effects of SDoH on AD etiology
- Appreciate the utility of new approaches based on the latest advancements of large language models (LLMs) and AI to unravel the relationship between SDoH and AD entities
- Learn a novel, automated framework that leverages LLMs to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities extracted from some established knowledge graph
Speakers
- Tianqi Shang, MS (University of Pennsylvania)
- Shu Yang, PhD (University of Pennsylvania)
Early Alzheimer's Detection Through Voice Analysis: Harnessing Locally Deployable LLMs via ADetectoLocum, a Privacy-Preserving Diagnostic System
Diagnosing Alzheimer's Disease (AD) early and cost-effectively is crucial. Recent advancements in Large Language Models (LLMs) like ChatGPT have made accurate, affordable AD detection feasible. Yet, HIPAA compliance and the challenge of integrating these models into hospital systems limit their use. Addressing these constraints, we introduce ADetectoLocum, an open-source LLM-equipped model designed for AD risk detection within hospital environments. This model evaluates AD risk through spontaneous patient speech, enhancing diagnostic processes without external data exchange. Our approach secures local deployment and significantly surpasses previous models in predictive accuracy for AD detection, especially in early-stage identification. ADetectoLocum therefore offers a reliable solution for AD diagnostics in healthcare institutions.
Learning Objectives
- Critically evaluate the feasibility and limitations of using locally deployable Large Language Models (LLMs) for early detection of Alzheimer’s Disease (AD), considering factors such as data privacy, accuracy trade-offs, and potential biases.
- Analyze the broader implications of AI-driven diagnostic tools in clinical practice, focusing on their reliability, ethical considerations, and practical challenges in real-world deployment.
- Assess the impact of deploying LLMs for AD detection on patient privacy, clinician decision-making, and healthcare outcomes.
- Develop an informed perspective on the ethical and practical challenges associated with integrating AI-based diagnostic tools into clinical workflows.
Speaker
- Genevieve Mortensen, B.S. (Indiana University)
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.5 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2024 Symposium sessions attended for ACHIPsTM Recertification.
Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.