A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights

• URL: http://arxiv.org/abs/2409.12853v2
• Date: Sat, 19 Oct 2024 04:21:12 GMT
• Title: A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
• Authors: Hakan T. Otal, Stephen V. Faraone, M. Abdullah Canbaz,
• Abstract summary: Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD. The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder.
• Score: 0.4915744683251151
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD, constructed by integrating scientific literature and clinical data with the help of cutting-edge large language models. The analysis, including k-core techniques, identified critical nodes and relationships that are central to understanding the disorder. Building on these findings, we curated a knowledge graph that is usable in a context-aware chatbot (Graph-RAG) with Large Language Models (LLMs), enabling accurate and informed interactions. Our knowledge graph not only advances the understanding of ADHD but also provides a powerful tool for research and clinical applications.

Related papers

• Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs [33.755845172595365]
  Growing evidence suggests that social determinants of health (SDoH) affect individuals' risks of developing Alzheimer's disease (AD) and related dementias. This study presents a novel, automated framework to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities. Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas.
  arXiv  Detail & Related papers  (2024-10-04T21:39:30Z)
• Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
  We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations [9.544065991313062]
  We propose a novel graph neural network (GNN) technique for explainable Parkinson's disease (PD) detection using resting state EEG. We employ structured global convolutions with contrastive learning to better model complex features with limited data. We developed and evaluated our method using the UC San Diego Parkinson's disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation.
  arXiv  Detail & Related papers  (2024-08-01T20:54:33Z)
• Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects [70.37277191524755]
  We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge.
  arXiv  Detail & Related papers  (2023-06-15T16:22:57Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Detection of ADHD based on Eye Movements during Natural Viewing [3.1890959219836574]
  ADHD is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose. We develop an end-to-end deep learning-based sequence model which we pre-train on a related task. We find that the method is in fact able to detect ADHD and outperforms relevant baselines.
  arXiv  Detail & Related papers  (2022-07-04T12:56:04Z)
• Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation [150.52617238140868]
  We propose low-resource medical dialogue generation to transfer the diagnostic experience from source diseases to target ones. We also develop a Graph-Evolving Meta-Learning framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease.
  arXiv  Detail & Related papers  (2020-12-22T13:20:23Z)
• Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation [33.818136671925444]
  We propose the neural-symbolic learning framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning. Our algorithm remarkably exceeds existing methods in the detection of spinal structures.
  arXiv  Detail & Related papers  (2020-04-28T15:06:24Z)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

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.