DataPulse: An Interactive Dashboard for Statistical and Exploratory

Analysis of Multimodal Healthcare Data in Shiny

Adam Urban

, James Connolly

, Gauri Vaidya

1 a

, Krishn Kumar Gupt

2 b

and

Meghana Kshirsagar

1 c

Department of Computer Science and Information Systems, University of Limerick, Ireland

Systems Biology Ireland, School of Medicine, University College Dublin, Ireland

Keywords:

Shiny, R Programming, Bioconductor, Exploratory Data Analysis, Visualization, Medical Datasets,

Bioinformatics, Data Science.

Abstract:

The extraction of actionable insights from data-driven analyses is crucial for efﬁciently proﬁling, analyz-

ing, and visualizing intricate medical datasets. A robust, generic data proﬁling tool is essential to uncover

and understand relationships within medical data. In this context, building Shiny app can lead to numerous

advantages—providing interactive, user-friendly, and dynamic dashboards, and the capacity to deploy scal-

able, web-based solutions. In this article, we introduce DataPulse, a versatile data proﬁling tool designed to

analyze multimodal healthcare data. By leveraging advanced statistical methodologies, DataPulse uncovers

complex relationships in the data and displays them through comprehensive dashboards. For instance, in ra-

diogenomics, sequential imaging visualizations can highlight dynamic changes in disease progression over

time. The article discusses two usecases of DataPulse: one focusing on the analysis of hip fracture patient

pathways in the emergency department, and the other offering a detailed exploration of cancer disease through

multimodal datasets to derive insights on drug outcomes and disease progression over time. In conclusion,

DataPulse exempliﬁes how robust, interactive data proﬁling can transform complex medical data into action-

able insights.

1 INTRODUCTION

Shiny is an R package that is used for creating in-

teractive web applications for data visualization. It is

divided into User Interface (UI) and server sections.

The user interface is easily customizable using Hyper

Text Markup Language (HTML) and Cascading Style

Sheets (CSS), enabling professional design tailored to

user needs (Posit, 2024).

R was developed in 1993 and has grown signiﬁ-

cantly in recent years. With its extensive capabilities

covered by user-made packages, it is especially pop-

ular choice in research. Comprehensive R Archive

Network was introduced later to accommodate user-

made packages. These packages contributed to R be-

ing one of the best choices for data science. The

most widely used data science packages for data sci-

ence are ggplot2, plotly for data visualization and

https://orcid.org/0000-0002-9699-522X

https://orcid.org/0000-0002-1612-5102

https://orcid.org/0000-0002-8182-2465

dplyr, tidyr, readr for data manipulation, which

are also often referred to as the Tidyverse (R Core

Team, 2024).

Shiny was introduced in 2012 and since its ini-

tial development it experienced a great growth. In-

tegrating support for HTML, CSS, and JavaScript it

provides an easily modiﬁable interface for the app.

Later, Shiny Server was launched to allow users to

deploy their Shiny web applications using cloud host-

ing. Over the last few years Shiny has proved efﬁcient

and gained signiﬁcant popularity in data intensive re-

search domains such as healthcare (Kasprzak et al.,

2021).

One of R’s standout features is the Shiny frame-

work, which enables developers to create interac-

tive, web-based applications with minimal effort. Al-

though many Shiny applications serve generic data

visualization and dashboards, DataPulse is speciﬁ-

cally designed to derive crucial insights from a va-

riety of healthcare datasets such as patient tracking

databases, biomedical and pan-cancer datasets. Our

goal is to offer meaningful contributions to the global

Urban, A., Connolly, J., Vaidya, G., Gupt, K. K., Kshirsagar and M.

DataPulse: An Interactive Dashboard for Statistical and Exploratory Analysis of Multimodal Healthcare Data in Shiny.

DOI: 10.5220/0013523500003967

In Proceedings of the 14th International Conference on Data Science, Technology and Applications (DATA 2025), pages 447-454

ISBN: 978-989-758-758-0; ISSN: 2184-285X

447

effort against cancer disease by enhancing our collec-

tive understanding. This tool targets a niche audience

of researchers and clinicians working with complex

datasets in the hopes of helping them better under-

stand and gain crucial insights into datasets they may

be working with.

2 MOTIVATION

To effectively analyze and interpret large-scale med-

ical datasets, gaining clear and actionable insights

is crucial. A framework that supports multimodal

data analysis offers a distinct advantage to simplify

the analysis of complex datasets while maintaining

user-friendliness, making it easier to uncover pat-

terns, trends, and potential biases. By leveraging the

Shiny package, we demonstrate the equitable acces-

sibility of DataPulse to users with varying levels of

programming expertise, broadening its usability and

appeal. DataPulse’s interactive and intuitive inter-

face ensures that even non-technical users can engage

with the data. We demonstrate how our tool can be

harnessed to extract meaningful insights from multi-

modal datasets for lung cancer: tabular, genomic and

image and MIMIC-IV database consisting of multiple

comma separated variables.

2.1 Use Case I: Hip Fracture

Prolonged patient wait times in emergency depart-

ments (EDs) are a critical barrier to effective health-

care delivery, impacting clinical outcomes and strain-

ing hospital resources. Older adults (aged ≥ 65 years)

are disproportionately affected by ED delays due to

their complex medical needs and higher likelihood

of comorbidities (Walsh et al., 2021; European Com-

mission, 2019). Among this demographic, hip frac-

ture patients represent a particularly vulnerable sub-

group requiring urgent care. Timely interventions for

hip fracture patients are essential to improving out-

comes and alleviating systemic pressures on EDs (Ka-

nis et al., 2021; Kelly et al., 2022).

To address these challenges, this use case inte-

grates demographic and admission data to provide ac-

tionable insights for optimizing resource allocation

and improving patient ﬂow. Health-care providers

can use DataPulse to understand both patient pathway

journey and demographics.

2.2 Use Case II: Lung Cancer

Cancer biomarkers are crucial in early detection,

diagnosis, and especially individualized treatment

strategies (Purkayastha et al., 2023). Therefore, ﬁnd-

ing pan-cancer biomarkers has become an important

focus in cancer research. Each year, an estimated

42,000 people in Ireland are diagnosed with cancer,

including both invasive and non-invasive tumors (Na-

tional Cancer Registry Ireland, 2023). Recent re-

search indicates over 24,000 invasive cases annually,

with 13,075 in men and 11,349 in women were re-

ported during 2019-2021, among which lung can-

cer was the third most common cancer in males and

the second most common in females (National Can-

cer Registry Ireland, 2023). Lung cancer, in par-

ticular, is the most deadly in Ireland, making it the

leading cause of cancer fatalities for both sexes (Na-

tional Cancer Registry Ireland, 2023). The future

presents an even more alarming picture, with projec-

tions indicating a rapid increase in lung cancer inci-

dence, especially among women, with rates expected

to rise by 105% between 2015 and 2045, by 131%

in men, and by 119% combined (Irish Cancer Soci-

ety, 2019). In light of these statistics, our research fo-

cuses on extracting vital information from Lung ade-

nocarcinoma (LUAD) data, the most prevalent type

of primary lung cancer within the non-small cell lung

cancer (NSCLC) category. The end users for this

use case are researchers within medical community.

Cancer researchers can use this app to analyze ge-

nomic datasets, possibly helpful in identifying novel

biomarkers in terms of radiation and drug-sensitivity.

3 DATASET MODALITIES

This study leverages medical data sets that encom-

pass three primary modalities, viz., tabular, imag-

ing, and genomic data. Tabular data, consisting of

structured patient information, such as demographics,

treatment histories, and clinical outcomes, are essen-

tial to identify statistical trends and perform stratiﬁed

analyzes. This modality provides a foundation for un-

derstanding patterns in cancer progression and treat-

ment efﬁcacy. Genomic data, facilitating radiation

and drug sensitivity charts and Ribonucleic acid ex-

pression levels, enable the identiﬁcation of molecular

biomarkers and facilitate a deeper understanding of

cancer heterogeneity. Imaging data, in this case com-

puted tomography scans of patients in Digital Imag-

ing and Communications in Medicine (DICOM) im-

age format, offer insights into physical characteristics

and progression markers in various types of cancer.

Together, these modalities provide a holistic view of

patient health, enabling the correlation of clinical, vi-

sual, and molecular factors to uncover actionable in-

sights for personalized treatment strategies.

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Data preparation UI Layout

Server Logic

Web Deployment

Figure 1: Pipeline of DataPulse Framework.

3.1 Tabular Data

The tabular data in this study includes tables provid-

ing an overview and a summary of the dataset, strati-

ﬁed analysis tables that segment data by speciﬁc vari-

ables, correlation matrices, and reactive plots. The

overview tables serve as essential tools for organizing

and analyzing complex clinical information. Strat-

iﬁed analysis tables break down the data into sub-

groups, allowing for a deeper understanding of rela-

tionships within speciﬁc patient populations. Correla-

tion matrices, derived from the tabular data, are used

to identify and quantify the relationships between dif-

ferent variables, such as age and smoking history. The

plots were created from the provided datasets, offer-

ing an interactive way to visualize and analyze key

characteristics such as age, sex, race, and ethnicity.

These plots facilitate the identiﬁcation of patterns and

trends, providing insights into the distribution and re-

lationships between patient demographics and clinical

outcomes.

3.2 Genomic Data

The genomic data in this study are used in the radio-

genomic analysis, and the pharmacogenomic analy-

sis. Radiogenomic analysis uses genomic data to de-

termine cell lines responses to radiation. It also allows

generating the radiation sensitivity signatures. Phar-

macogenomic analysis, on the other hand, focuses on

the relationship between cell lines and certain drugs.

It also allows generating the drug sensitivity signa-

tures.

3.3 Image Data

The image data in this study are the CT scans of Non-

small cell lung cancer (NSCLC) patients in the DI-

COM format. DICOM offer crucial visual insights

into the progression of lung cancer in patients over

time. In our app we included four randomly selected

samples. These images provide valuable visual rep-

resentations of tumor development and response to

treatment, enabling clinicians and researchers to ob-

serve changes over time. The DICOM images offer

a comprehensive view of visual data, enhancing the

depth of the analysis and enabling a more thorough

understanding of disease progression.

3.4 Workﬂow of Development

Figure 1 outlines the workﬂow for developing a web

application using Shiny, which includes the follow-

ing key stages: Data Preparation, Structuring the UI

Layout, Developing the Server Logic, and Web De-

ployment via Shiny hosting services.

In the data preparation phase, we imported the

datasets into RStudio as data frames.

4 SOFTWARE DESCRIPTION

This section details the overview of our ShinyApp and

the features of the dashboard.

4.1 Overview of UI

The chosen layout for the Shiny app interface is

fluidPage, enabling it to automatically adjust to dif-

ferent screen sizes. The color theme is set to yeti

from the shinythemes package, providing a modern

appearance to the app. Furthermore, CSS styling was

applied to enhance the design of containers and other

elements. A navigation bar with eight tabs was in-

corporated to efﬁciently organize the app’s features.

Within these tabs, we used conditional panels to dy-

namically display content based on the user’s selec-

tions.

4.2 Features of DataPulse

• Flexible Data Input: DataPulse supports a wide

range of data formats including Excel, CSV, TSV,

and JSON. This ensures compatibility with di-

verse data sources commonly used in clinical re-

search.

• Dynamic UI Elements: The interface adapts

based on the uploaded dataset, allowing users to

DataPulse: An Interactive Dashboard for Statistical and Exploratory Analysis of Multimodal Healthcare Data in Shiny

449

customize input ﬁelds dynamically for a more tai-

lored and intuitive data exploration experience.

• Multiple Data Visualization Options: The tool

offers various visualization techniques such as

histograms, scatter plots, boxplots, and bar charts,

enabling comprehensive graphical representations

of complex medical data.

• Summary Statistics: It provides key summary

statistics, including correlation heatmaps and

stratiﬁed analyses, which facilitate a deeper un-

derstanding of data relationships and underlying

trends.

4.3 Reusability of DataPulse

• Modularized Code: The underlying code is

structured in a modular fashion, promoting

reusability and ease of maintenance, which is crit-

ical for iterative development.

• Multiple Dataset Handling: End users can up-

load and manipulate several datasets concurrently.

• Download Functionalities: The app includes

features to download generated plots and analyti-

cal results, supporting reproducibility.

• Scalable Deployment: DataPulse is deployed on

shinyapps.io, ensuring that the tool can be eas-

ily accessed.

4.4 Tabular Modality Features

The ﬁrst few tabs of the app are dedicated to the tabu-

lar modality. These tabs use data from the LUAD clin-

ical datasets and the GDSC drug response datasets.

The ﬁrst tab allows users to select the dataset they

want to explore, offering an overview and a statistical

summary of the data.This is facilitated by using the

expression renderDataTable() to show the data in

an interactive table, allowing searchability throughout

the dataset and renderPrint() to show the output of

the summary() function.

4.5 Data Summary

The ﬁrst two tabs allows users to upload and work

with different ﬁle formats and summarise the data.

4.6 Stratiﬁed Analysis

The tab provides stratiﬁed analysis using the table1

package (Rich, 2023). This lets the user choose a

speciﬁc dataset, and a speciﬁc grouping variable they

want to stratify the data. The selected and ﬁltered data

Figure 2: Correlation matrix depicting patient’s smoking

history, mutation count, age on overall survival months.

on the server logic side are updated through reactive

expressions, allowing seamless output change when

necessary. Unique columns, like the patient ID, were

ﬁltered out from the list of variables as those are unﬁt

for stratiﬁed analysis. The renderTable() function

was used to print out the resulting table.

4.7 Plots

This tab shows plots provides insights through vi-

sualizations for preliminary exploratory data analy-

sis. These data visualizations were created with the

Plotly package, which allows interactive actions,

like zooming, panning or providing information on

hovering over data bins or categories (Sievert, 2020).

There are four demographics charts that were created

for the datasets: the distribution of age, sex, race and

ethnicity among the patients. The age distribution plot

is initially created with the ggplot2 package, how-

ever it is then converted to a plotly graph using the

renderPlotly() function to leverage the interactive

features of plotly (Wickham, 2016). Null-value data

rows are ﬁltered out and the number of total samples

and ﬁltered samples is provided under the plots. Pa-

rameter settings to the plots were changed extensively

to ensure high quality design. This involved provid-

ing a title, subtitles for axis, changing the colors used,

and the font sizes and margins. Reactive expressions

were used to ﬁlter selected data, ensuring that only

necessary computation are being executed.

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450

DataPulse

(a)

(b)

(c)

Figure 3: Overview of dashboard. (a) Dashboard for use case 1, illustrating critical insights with respect to hip fracture

occurrences: histogram plot is used to depict the occurrence of hip fracture events amongst different age groups, bar plot

illustrates its occurrence with respect to male and female population, while box plot illustrates the mode of arrival of patients

in emergency departments; (b) Dashboard for use case 2 showcasing information about the distribution of age in the lung

cancer dataset while pie-charts are used to illustrate the proportion of people afﬂicted with lung cancer disease with respect to

population demographics which includes age,race and ethnicity; (c) Dashboard for displaying the preview of the contents for

both LUAD TCGA dataset and Radiogenomics dataset.

DataPulse: An Interactive Dashboard for Statistical and Exploratory Analysis of Multimodal Healthcare Data in Shiny

451

4.8 Correlation Matrix

This tab illustrates correlation matrix displayed using

the Corrplot package, which is useful when deter-

mining relations between variables. For instance, in

Figure 2, we can spot a correlation between patient’s

smoking history and mutation count. We can also no-

tice the negative correlation between age and over-

all survival months, indicating that younger people

have greater chance of surviving lung cancer. Reac-

tive expression numeric data() is used to ﬁlter data

as the correlation matrix only works with numeric val-

ues. The matrix is in upper triangular form, omitting

unnecessary data duplicates and designed with cus-

tom colors making it easily understandable (Wei and

Simko, 2017).

4.9 Drug Sensitivity

This discusses Drug Sensitivity T-test analysis. For

this, we utilized R’s built-in t.test() function to

compare IC50 values, a measure of drug sensitiv-

ity, across different drugs, cell lines, or tissue types.

Speciﬁcally, we used the genomic datasets GDSC1

and GDSC2, focusing on the LUAD tissue. This anal-

ysis can help selecting the right personalized treat-

ment decisions, possibly revealing drug resistance or

sensitivity in a particular cell line or tissue type.

4.10 Radiogx Analysis

This tab is dedicated for a radiogenomic analysis. The

RadioGx package from BioConductor allows us to get

insights into how different cell lines respond to radia-

tion. Dose response metrics are computed within a re-

active expression. Linear Quadratic model is used to

describe the relationship between radiation dose and

cell survival and the user can select to view this in

the form of a survival fraction after a 2 Gy dose of

radiation, computed using computeSF2(), a dose for

10% survival computed using computeD10(), or the

the dose-response curve. These metrics ensure bet-

ter understanding of the data and determining the cor-

rect radiation dosage for the patient as we have to bal-

ance eliminating cancer cells with trying not to dam-

age too many healthy cells. Another important fea-

ture of this tab is the option to generate radiation sen-

sitivity signatures, which was implemented using the

radSensitivitySig() function on the clevelandS-

mall dataset.

4.11 Pharmacogx Analysis

This tab of our app showcases pharmacogenomic

analysis, utilizing the PharmacoGx package from Bio-

Conductor. This package provides a comprehensive

framework for exploring the relationships between

drug sensitivity and molecular features as seen in Fig-

ure 4.

Figure 4: Line graphs that measures the outcome of drug

administered with respect to cell lines and prescribed dose.

Our app uses this package to offer drug sensi-

tivity metrics, providing insights into the effectiv-

ness of a certain drug on chosen cell line, dose re-

sponse curve visualization, showing the relationship

between drug concentration and cell viability, and

lastly the drug sensitivity signatures that can help

identify genes linked to drug sensitivity, possibly dis-

covering novel biomarkers of drug sensitivity. The

drugSensitivitySig() function was used on the

CCLESmall dataset to provide the signatures.

4.12 Imaging Modality Features

Figure 5: DICOM viewer for displaying the sequence of ra-

diogenomics images useful for understanding disease pro-

gression.

This tab provides a DICOM viewer feature as illus-

trated in Figure 5. DICOM is an image format used

in medical imaging that holds not only the image data

but also the essential metadata, containing informa-

tion about the patient in the same ﬁle. These images

are not directly viewable using the standard image

viewers. For that reason, we have integrated a DI-

COM Viewer on the last feature tab of our app, facil-

itating an easy way to view DICOM images in their

designated sequences.

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452

4.13 Hip Fracture Analysis

This tab provides an interactive exploration of hip

fracture patient pathways using MIMIC-III data. It

enables users to analyze critical factors inﬂuencing

the length of hospital stays during high-priority events

in older patients.

5 DASHBOARD DESIGN

To build the interactive dashboards for our use

cases as seen in Figure 3, we utilize the MIMIC-III

database, a publicly available, de-identiﬁed patient

tracking dataset. For the hip fracture study, the dataset

was ﬁltered to include only hip fracture patients ad-

mitted through the ED. This focused approach en-

sures the analysis aligns with our use case for hip frac-

ture patients with the objective of improving patient

ﬂow and outcome for this category of patients.

Similarly, for lung cancer study, we employed

multimodal datasets such as Genomics of Drug Sen-

sitivity in Cancer (GDSC1) and GDSC2 (Yang et al.,

2012), LUAD TCGA Pan Cancer Atlas 2019 (Cerami

et al., 2012; Gao et al., 2013; de Bruijn et al., 2023;

Heath et al., 2021), NSCLC-Radiomics (Aerts et al.,

2019; Manem et al., 2023). Key features of the dash-

board include:

• Dynamic Filtering: Users can ﬁlter data by de-

mographic attributes (e.g., age, gender, ethnicity)

and admission characteristics (e.g., mode of ad-

mission, ED wait times,and length of stay).

• Interactive Visualizations: The dashboard pro-

vides a range of graphical tools for data explo-

ration, including:

– Bar Charts: Highlight demographic distribu-

tions, admission trends, and length of stay.

– Pie Charts: Pie charts display the proportion of

people affected with the disease.

– Scatter Plots: Correlate variables such as age

versus wait times or mode of admission versus

length of stay.

– Histograms: Display the distribution of key

variables such as patient age, wait times, and

length of stay.

– Box Plots: Summarize variability and detect

outliers in variables such as wait times or time-

to-surgery.

– DICOM Viewer: To display the DICOM im-

ages using CT scans of non-small cell lung

cancer patients from the NSCLC-Radiomics

dataset. The app converts the ﬁle from DICOM

to PNG, ensures proper scaling and other errors

like missing ﬁle or directory and then shows

the images in succession. A scroll bar was im-

plemented to allow proper viewing of an image

dataset.

– Drug Response Plots: The line plots displays

the radiation response curves with respect to

both cell lines and administered dosage.

– Correlation Plots: Correlation plots displays

the impact of features such as age, smoking and

mutation count on overall survival.

6 CONCLUSIONS

This study provides a data proﬁling framework for an-

alyzing big medical datasets, and provide real-time

visualizations through interactive dashboards. Al-

though this study is based on a small sample size, the

methodologies developed can be easily adapted and

applied to larger datasets, enabling other researchers

to gain similar insights. By incorporating multimodal

data analysis, DataPulse holds the potential for ad-

vanced data analytics within healthcare ecosystem.

The immediate scope of this study will focus on inte-

grating various healthcare datasets for diverse disease

categories.

7 DATA AVAILABILITY

The data that support the ﬁndings of this study is

available at adamurban.shinyapps.io/OncoEx/.

ACKNOWLEDGEMENTS

This project is funded under the National Challenge

Fund which was established under the government’s

National Recovery and Resilience Plan (NRRP),

funded by the EU’s Recovery and Resilience Facility.

The fund is coordinated and administered by Taighde

Eireann – Research Ireland grant 22/NCF/DR/11289.

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