Graph AI in Medicine
- URL: http://arxiv.org/abs/2310.13767v2
- Date: Mon, 11 Dec 2023 23:04:55 GMT
- Title: Graph AI in Medicine
- Authors: Ruth Johnson, Michelle M. Li, Ayush Noori, Owen Queen, Marinka Zitnik
- Abstract summary: Graph neural networks (GNNs) process data holistically by viewing modalities as nodes interconnected by their relationships.
GNNs capture information through localized neural transformations defined on graph relationships.
Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge.
- Score: 9.733108180046555
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In clinical artificial intelligence (AI), graph representation learning,
mainly through graph neural networks (GNNs), stands out for its capability to
capture intricate relationships within structured clinical datasets. With
diverse data -- from patient records to imaging -- GNNs process data
holistically by viewing modalities as nodes interconnected by their
relationships. Graph AI facilitates model transfer across clinical tasks,
enabling models to generalize across patient populations without additional
parameters or minimal re-training. However, the importance of human-centered
design and model interpretability in clinical decision-making cannot be
overstated. Since graph AI models capture information through localized neural
transformations defined on graph relationships, they offer both an opportunity
and a challenge in elucidating model rationale. Knowledge graphs can enhance
interpretability by aligning model-driven insights with medical knowledge.
Emerging graph models integrate diverse data modalities through pre-training,
facilitate interactive feedback loops, and foster human-AI collaboration,
paving the way to clinically meaningful predictions.
Related papers
- Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies [0.757843972001219]
We propose a systematic approach to examine the results of GNN models trained with structured and unstructured information from the MIMIC-III dataset.
We show the robustness of this approach in identifying predictive features in PKGs for the task of readmission prediction.
arXiv Detail & Related papers (2024-08-27T09:48:25Z) - Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks [15.04251924479172]
This paper introduces a personalized medical decision algorithm utilizing graph neural network (GNN)
The proposed personalized medical decision algorithm showed significantly superior performance in terms of disease prediction accuracy, treatment effect evaluation and patient risk stratification.
arXiv Detail & Related papers (2024-05-23T04:30:41Z) - AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine [31.424781716926848]
We propose a novel algorithm named ours, which can automatically select important features to construct multiple patient similarity graphs.
ours is evaluated on two real-world medical scenarios and shows superiors performance.
arXiv Detail & Related papers (2023-11-24T06:27:25Z) - Radiology Report Generation Using Transformers Conditioned with
Non-imaging Data [55.17268696112258]
This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information.
The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
arXiv Detail & Related papers (2023-11-18T14:52:26Z) - Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling
Model [64.29487107585665]
Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases.
Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks.
In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
arXiv Detail & Related papers (2022-07-14T20:03:52Z) - Graph-in-Graph (GiG): Learning interpretable latent graphs in
non-Euclidean domain for biological and healthcare applications [52.65389473899139]
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain.
Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task.
We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
arXiv Detail & Related papers (2022-04-01T10:01:37Z) - EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting.
We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
arXiv Detail & Related papers (2022-02-21T18:59:03Z) - Brain dynamics via Cumulative Auto-Regressive Self-Attention [0.0]
We present a model that is considerably shallow than deep graph neural networks (GNNs)
Our model learns the autoregressive structure of individual time series and estimates directed connectivity graphs.
We demonstrate our results on a functional neuroimaging dataset classifying schizophrenia patients and controls.
arXiv Detail & Related papers (2021-11-01T21:50:35Z) - Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
We propose a deep co-attention network for multi-view subspace learning.
It aims to extract both the common information and the complementary information in an adversarial setting.
In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
arXiv Detail & Related papers (2021-02-15T18:46:44Z) - Graph representation forecasting of patient's medical conditions:
towards a digital twin [0.0]
We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions.
We provide a proof of concept of integrating a large set of composable clinical models using molecular data.
arXiv Detail & Related papers (2020-09-17T13:49:48Z) - Learning Dynamic and Personalized Comorbidity Networks from Event Data
using Deep Diffusion Processes [102.02672176520382]
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition.
We develop deep diffusion processes to model "dynamic comorbidity networks"
arXiv Detail & Related papers (2020-01-08T15:47:08Z)
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.