Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT

• URL: http://arxiv.org/abs/2402.00137v1
• Date: Wed, 31 Jan 2024 19:30:04 GMT
• Title: Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT
• Authors: Diego Machado Reyes, Hanqing Chao, Juergen Hahn, Li Shen and Pingkun Yan
• Abstract summary: Alzheimer's disease is the most prevalent neurodegenerative disease. Current data driven approaches are able to classify the subtypes at later stages of AD or related disorders, but struggle when predicting at the asymptomatic or prodromal stage. We propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages.
• Score: 15.942849233189664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alzheimer's disease (AD) is the most prevalent neurodegenerative disease; yet its currently available treatments are limited to stopping disease progression. Moreover, effectiveness of these treatments is not guaranteed due to the heterogenetiy of the disease. Therefore, it is essential to be able to identify the disease subtypes at a very early stage. Current data driven approaches are able to classify the subtypes at later stages of AD or related disorders, but struggle when predicting at the asymptomatic or prodromal stage. Moreover, most existing models either lack explainability behind the classification or only use a single modality for the assessment, limiting scope of its analysis. Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages. Similarly, we build prompts and use large language models, such as ChatGPT, to interpret the findings of our model. In our framework, we propose a tri-modal co-attention mechanism (Tri-COAT) to explicitly learn the cross-modal feature associations. Our proposed model outperforms baseline models and provides insight into key cross-modal feature associations supported by known biological mechanisms.

Related papers

• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [46.56667527672019]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion [14.536841566365048]
  Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. In this study, we aim to identify subtypes of AD that represent distinctive clinical features and underlying pathology.
  arXiv  Detail & Related papers  (2024-10-04T21:38:14Z)
• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease Classification [17.549219224802]
  Deep learning (DL) models have shown significant potential in Alzheimer's Disease (AD) classification. We developed a quantitative disease-focusing strategy to enhance the interpretability of DL models. We evaluated these models in terms of their abilities to focus on disease-relevant regions.
  arXiv  Detail & Related papers  (2024-09-07T19:16:40Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis [9.316162293112738]
  We propose an open-set recognition model, OpenAPMax, based on the anomaly pattern to address Alzheimer's disease diagnosis. We evaluate the performance of the proposed method with recent open-set recognition, where we obtain state-of-the-art results.
  arXiv  Detail & Related papers  (2023-07-03T11:21:09Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease [7.594681424335177]
  Alzheimers disease (AD) is a heterogeneous, multitemporal neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model.
  arXiv  Detail & Related papers  (2022-10-12T02:52:00Z)
• Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression [71.7560927415706]
  latent hybridisation model (LHM) integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system. We evaluate LHM on synthetic data as well as real-world intensive care data of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-06-05T11:42:45Z)
• Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations [3.4935855647873293]
  We determined the effect of APOE alleles on the disease progression timeline of Alzheimer's disease using a discriminative event-based model (DEBM) Our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials.
  arXiv  Detail & Related papers  (2020-09-15T14:46:10Z)
• A Comprehensive Study on Temporal Modeling for Online Action Detection [50.558313106389335]
  Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. This paper aims to provide a comprehensive study on temporal modeling for OAD including four meta types of temporal modeling methods. We present several hybrid temporal modeling methods, which outperform the recent state-of-the-art methods with sizable margins on THUMOS-14 and TVSeries.
  arXiv  Detail & Related papers  (2020-01-21T13:12:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.