graphical representation health

Learn how healthcare data visualization with infographics can help patients understand data, making it easier for health professionals to save lives.

This Perspective outlines the successes and limitations of graph deep learning for biomedical and healthcare applications.

For example, most articles now have more authors than in previous decades, and a much larger menu of journals creates a diversity of article lengths and other requirements. Despite these changes, the demand for visual representations of data and results remains high, as exemplified by graphical abstracts, overview figures, and infographics.

Data visualization in healthcare includes charts, graphs, tables, and more. Let's consider software-based methods for custom health information visualization and graphical data representation.

MP4 File - ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup. Video introduction of ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup at SIGKDD 2024 ... Xu Chu, Yasha Wang, Yang Lin, Junfeng Zhao, Liantao Ma, and Wenwu Zhu. 2023. Fused Gromov-Wasserstein Graph Mixup for Graph ...

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward ...

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current ...

We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying ...

This experimental study examined graphical representation of data otherwise represented as text and numbers in clinical decision-making. This study was conducted at the Health-Care Associates practice at Beth Israel Deaconess Medical Center, a 621-bed tertiary care center in Boston, MA, and a principal teaching hospital of Harvard Medical School.

On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal het-erogeneous graph.

Background Australian epidemiologists have recognised that lay readers have difficulty understanding statistical graphs in reports on population health. This study aimed to provide evidence for graph design improvements that increase comprehension by non-experts. Methods This was a double-blind, randomised, controlled trial of graph-design interventions, conducted as a postal survey. Control ...

Design flaws in longitudinal database studies are avoidable but can be unintentionally obscured in the convoluted prose of methods sections, which often lack specificity. We propose a simple framework of graphical representation that visualizes study design implementations in a comprehensive, unambiguous, and intuitive way; contains a level of ...

The Power of Good Data Visualization. Data visualization involves the use of graphical representations of data, such as graphs, charts, and maps. Compared to descriptive statistics or tables, visuals provide a more effective way to analyze data, including identifying patterns, distributions, and correlations and spotting outliers in complex ...

With the rapidly growing volume and variety of Electronic Health Records (EHR) data, deep-learning models exhibit state-of-the-art performance for many predictive tasks in the health domain. To overcome the challenge of high dimensionality in EHR data, many representation learning methods have been proposed to learn low-dimensional diagnosis representations. Another challenge is how to ...

Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can ...

We aimed to improve the understanding of conveying health-related statistical information with graphical representations compared with numerical representations. First, we investigated whether the iconicity of representations (i.e., their abstractness vs. concreteness) affected comprehension and recall of statistical information.

Displaying the Data in a Health Care Quality Report. Graphs and tables remain the most efficient and practical way to convey a large amount of information, especially comparative information and numbers. Visual presentations are powerful tools for concisely making points that are hard to put into words. However, while some consumers prefer a ...

Data and information visualization ( data viz/vis or info viz/vis) [ 2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount [ 3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a ...

Molecular property prediction is a crucial foundation for drug discovery. In recent years, pre-trained deep learning models have been widely applied to this task. Some approaches that incorporate prior biological domain knowledge into the pre-training framework have achieved impressive results. However, these methods heavily rely on biochemical experts, and retrieving and summarizing vast ...

Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. These visualizations allow us to easily understand any patterns, trends, or outliers in a data set. Data visualization also presents data to the general public or specific audiences without technical knowledge in an accessible ...

In this paper we propose an approach to constructing a knowledge graph of depression using semantic web technology to integrate those knowledge resources. Semantic web technology allows us to achieve a high degree of inter-operability over heterogeneous knowledge resources about depression.

Video presentation about the Quaternion Graph Representation Learning for Heterogeneous Feature Data Clustering. Download; 30.82 MB; References [1] Amir Ahmad and Lipika Dey. 2007. A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set. Pattern Recognition Letters, Vol. 28 ...

Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive learning, which cannot capture global graph structure. The key challenge ...

Tables offer a clearer representation that simplifies data manipulation, sorting, filtering, and statistical analysis when compared to graphical representations of the same data. In this study, we employ various tabular classifiers, including SVM, Logistic Regression, Random Forest, XGBoost, AdaBoost, and Perceptrons .

Knowledge Graph Enhancement for Improved Natural Language Health Question Answering using Large Language Models. Authors: ... and automated question answering based on them requires careful knowledge engineering involving knowledge representation, deduction, context recognition and sentiment analysis. In this ...

Minard presents the army's advance in the form of a graph which both represents a geographic movement over time, and its decreasing size. In the combination of the graph and the (admittedly very minimalistically drawn) geographic map, an "flow map" emerges, which was Minard's invention.

Consult a mental health professional. When you're worried about a child's ability to socialize well or you sense behavioral concerns, such as anxiety and depression, reaching out to a doctor or mental health professional is wise. SOURCE: Baylor College of Medicine, news release, Aug. 19, 2024.

Knowledge graph entity typing (KGET) aims to predict the missing entity types in knowledge graphs (KG). The relationship between entities and their corresponding types is often expressed using a single relation, hasType.However, hasType has a limited capability for modeling diverse entity-type relationships in the embedding space. In this paper, we first introduce multiple auxiliary relations ...

Analyzing Minard's Visualization Of Napoleon's 1812 March. In The Visual Display of Quantitative Information, Edward Tufte calls Minard's graphic of Napoleon in Russia one of the "best statistical drawings ever created.". But what makes it so good? Before we analyze this graphic, we need to know a bit of history.

Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive power of node representation, but recent developments reveal the inferior performance of low-degree nodes owing to their sparse neighbor connections, known as the degree-based long-tailed problem.