JMIR MEDICAL INFORMATICS

Bellmann et al

Original Paper

Introducing Attribute Association Graphs to Facilitate Medical Data
Exploration: Development and Evaluation Using EpidemiologicalStudy Data
Louis Bellmann1, PhD; Alexander Johannes Wiederhold1, MD; Leona Trübe1, PhD; Raphael Twerenbold2,3,4, Dr Med;Frank Ückert1, Dr Med; Karl Gottfried1
1

Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

2

Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

3German Center for Cardiovascular Research (DZHK) Partner Site Hamburg-Kiel-Lübeck, Hamburg, Germany

4

University Center of Cardiovascular Science, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg,
Germany

Corresponding Author:
Louis Bellmann, PhD
Institute for Applied Medical Informatics
University Medical Center Hamburg-Eppendorf
Martinistr. 52
Hamburg, 20246
Germany
Phone: 49 15228842404
Email: l.bellmann@uke.de

Abstract
Background: Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition,robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method
tailored to the needs of physicians must meet all the abovementioned criteria.
Objective: This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge,
adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of diseaseand control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns
associated with disease and reveal relations between attributes within the data set.
Methods: We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust
statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations
between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j,
Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between
cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is
accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg
City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed
freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the
System Usability Scale, individual questions, and user tasks.
Results: We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with ageneral cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure anddashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants withcardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were
calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians
assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful
task completion of 81.4% were reported.
Conclusions: The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust
to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a

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Bellmann et aluser test, and the validity of the statistical results was confirmed by associations known from literature and standard statisticalinference.
(JMIR Med Inform 2024;12:e49865) doi: 10.2196/49865
KEYWORDS
data exploration; cohort studies; data visualization; big data; statistical models; medical knowledge; data analysis; cardiovasculardiseases; usability

Introduction
The amount and availability of data around us are constantly
increasing. Researchers are increasingly using statistical models
to guide their data-driven scientific work. However, as therelationships discovered increase in complexity, the models
themselves are becoming gradually less transparent. In
high-stake decision fields, such as health care, data explanationand justification of decision-making are essential for theapplicability and distribution of novel technologies. Here, we
present new methods for extracting statistical insights fromlarge data sources and visualizing the results based on graphstructures. The methods balance complexity and comprehensive
description of the results on the one hand and clarity andinterpretability for clinicians and patients on the other hand.
The availability of large quantities of medical data is growing[1,2] and thus enabling machine learning methods to play anever-increasing role in medical research [3-5]. With theundoubtedly numerous advantages of “big data” in medicine
arises the problem of increasing complexity and lack oftransparency for clinicians [6,7]. In this context, the call for
more interpretable statistical models is gaining more attention[8,9]. In addition to the interpretability of the applied models
and results, good data visualization methods are key for theknowledge communication with clinicians and patients. Many
methods have been developed over the years [10-12].
For data-driven analysis, approaches originating from themathematical field of graph theory gain an increasing amountof attention for health care applications [13]. A graph consistsof nodes representing arbitrary objects and edges eachconnecting 2 nodes corresponding to some form of relation
between them. Graph-based database technologies, such as
Neo4j (Neo4j, Inc) [14], allow more efficient retrieval of large
amounts of data compared to traditional relational databasesystems [15,16], and many software tools for interactive,
graphical user interfaces are available [14,17-20].
Knowledge graphs are a form of data representation capturing
large quantities of data from potentially multiple sources in agraph structure. Existing data are usually processed and jointly
represented to enable accessible, often visual, exploration ofcondensed knowledge across different data modalities andsources. Owing to their intuitive and versatile character,knowledge graphs have many applications in the medical
domain [21]. Examples are the representation of biomolecularpathways [22], research related to COVID-19 or diabetes[23,24], knowledge about dietary supplement [25], and networks
of complex disease interactions [26].

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Statistical analysis discovering relations between variables
within a medical data set can be captured within a graphstructure. In this context, Bayesian networks are of increasing
interest in the medical domain [27,28]. They represent
conditional dependencies as edges and the absence of an edge
as probabilistic independence [29]. Using these conditional
dependencies, Bayesian networks can be used for inferring
neural networks [30] or diagnosis prediction [31]. However,they are sensitive to missing data during the model training
process [32]. Markov models describe states, for example, events
during a patient’s hospital stay, as nodes and transition
probabilities between states as edges [33]. As a result, Markov
models are applied for the analysis of time-dependent dynamic
processes in health care [33-35]. In association rule learning,
relations between variables are extracted from a data set basedon different measurements of interest, for example, conditional
probability [36]. This concept is applied to extract patterns fromclinical databases [37] or find suitable drug treatments [38]. All
3 approaches capture variable relations across a complete dataset.
In this work, we developed the attribute association graph(AAG), a new graph structure capturing statistical knowledge
extracted from a data set. We aimed to combine the focus ofknowledge graphs on interpretability, accessibility, and visual
exploration with graph-based statistical methods. We sought todevelop a novel and robust tool for statistical analysis that is
intuitively usable by physicians. We tailored our approach
specifically to the needs of data-driven analysis in the medical
domain by incorporating disease and control cohorts and aiming
for robustness to high-dimensional or not normally distributed
data, small sample sizes, and missing values. The graph isvisualized, and nodes and edges representing variable relationsof interest are highlighted to attract the attention of the user andfacilitate the data analysis. We complemented the AAG with adashboard for further data exploration. Only mouse clicking
and search bar prompting in English are required for thenavigation of the graph and dashboard. We aimed to evaluate
the validity of the statistical analysis represented by the graphstructure and dashboard. Therefore, we conducted an exemplary
data analysis based on a large epidemiological study. The resultsof the analysis were compared with findings from literature andstandard statistical inference using CIs of Pearson correlation
coefficients. In addition, we assessed the usability of thevisualization for medical researchers. We conducted user tests
with physicians using standardized usability tests, user tasks,
open feedback questions, and a free data exploration. The
generated graph structure and dashboard are freely available toclinical researchers for exploration on their own computers.

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Bellmann et al
metric variables. In addition, we assigned labels to each nodedepending on the broad categories of the represented attribute,for example, Cardiac, Condition, or Medical History.

Methods
AAG Definition
Our goal is to visualize participant attributes and the statistical
traits and relationships between them in a compact, interpretable,
and intuitive way. As a participant attribute, we consider asingular value or semantically meaningful value group for avariable, for example, “the participant was diagnosed withhypertension” or “participant has total cholesterol level above
200 mg/dL.” For the statistical analysis, we use simple metrics,
which were found to be intuitive for clinicians [39]. The metrics
are calculated for a disease and control group and compared to
identify attributes with a large deviation. Thus, in contrast to
traditional association rule mining [36], Bayesian networks [29],or Markov models [34], attributes can be selected, which appear
more often in the disease group compared to the control group.
As we analyze relations of singular attributes instead of
associations between variables, our results are methodologicallydifferent from correlation analysis, such as chi-square tests [40]or Pearson correlation coefficients [41].
In the AAG, single attributes are captured as nodes andvisualized as colored spheres of different sizes. Each node has
parameters for the name of the attribute’s variable, its value,
and a short description including units of measurement for

For metric variables, we calculated reference ranges based on
their value distribution within the whole data set. We defined
the reference range as all values within SD around the mean.
On the basis of reference ranges, we derived 3 additional nodes
for the attribute associated with values below, within, and above
the reference range. The 3 nodes inherit the parameter’s nameand description from the original nodes. They have the value
low, normal, or high. In addition, they contain the lower andupper bound of the reference range. All participants are assigned
to 1 of the 3 nodes based on their attribute value. Thus, metricvalues, for example, patient laboratory results, are labeled incomparison to the whole data set and enriched with semantics.
In addition, we enriched the nodes with several statisticalmeasurements of the described participant attribute within thedata set. The resulting parameters are given in Table 1. Notethat the relative attribute share accounts for the common problemof missing data [42,43] and is an upper bound to the relative
total share. By measuring the difference and quotient of relative
attribute shares, the distinction in attribute distribution between
the 2 groups is expressed. The size and color of the nodevisualization capture parts of these measurements to support
the data exploration with visual highlights.

Table 1. Statistical parameters for a node describing attribute a together with a short description and formulaa.

aParameter

Description

FormulaAbsolute countNumber of group members having attribute aci

Relative total share

Fraction of group members have attribute a

Relative attribute share

Relative total share, missing value adjusted

Relative attribute share difference

Absolute difference of relative attribute shares

Relative attribute share quotient

Fraction of maximum and minimum relative attribute share

Parameters with subscript d refer to the disease group. Parameters with subscript c refer to the control group. Subscript i refers to a definition for bothgroups, that is, i∈{d,c}. Let gi be the group size, andvalue.

be the number of group members having a valid value for the attribute a, that is, not a missing

We assigned a frequency label impacting the node’s size basedon the maximum relative attribute share. Therefore, a node’s
size indicates how common an attribute is within one of thegroups. Let p be the maximum relative attribute share of a node.
The node is assigned to 1 of the following 3 frequency label
types:•••

p≥0.5: labeled as highly frequent; the node has the largest
size.
0.1≤p<0.5: labeled as frequent; the node has a medium size.
p<0.1: labeled as infrequent; the node has the smallest size.

In addition, we assigned a distinction label to each node fromwhich its color is derived. The distinction label, and thus thenode color, indicates how much the attribute distribution differs
between groups. Here, brighter colors signal a larger distinction.

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We reuse the symbols in Table 1. Each node is assigned 1 of 5colors and distinction label types:•

δ≥0.2 or γ≥2.0:•
pd>pc: labeled as highly related; the node is colored inred.
•
pd<pc: labeled as highly inverse; the node is colored in
blue.

•

(δ≥0.1 or γ≥1.5) and δ<0.2 and γ<2.0:•pd>pc: labeled as related; the node is colored in orange.
•
pd<pc: labeled as inverse; the node is colored inturquoise.

•δ<0.1 and γ<1.5: labeled as unrelated; the node is coloredin beige.

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Combining size and color, nodes that are displayed largest andbrightest represent attributes with high frequency and large
distinction between groups. As all parameters calculated for anindividual node depend only on data for a single variable, thecomputation time needed for the calculation of all nodes of thegraph scales linearly with the number of variables and linearwith the sample size.
In the AAG, edges point from a source node to a target node,
indicating the conditional dependence of the target attribute on
the source attribute. The edges are displayed as lines with arrows
pointing from the source node sphere to the target node sphere.
The calculated statistical parameters for the conditional
dependence are presented in Table 2. Note that the relative
conditional share is conceptually equivalent to confidence inassociation rule learning [36]. By measuring the difference andquotient of the relative conditional share and the unconditional
relative attribute share of the target node, the impact of the added
condition is expressed. This impact can be negative if theunconditional relative attribute share is larger than the relative
conditional share. We assign a type to each edge to capture theimpact of the added condition. In the visualization, the line
thickness of the edge is given by its type. We reuse the symbols
in Table 2. Each node is assigned to 1 of the following 3 types:•••

δ'≥0.2 or γ'≥2.0: assigned to the high conditional difference
type; the edge has the thickest line.
(δ'≥0.1 or γ'≥1.5) and δ'<0.2 and γ'<2.0: assigned to themedium conditional difference type; the edge has a thinnerline.
δ'<0.1 and γ'<1.5: assigned to the low conditional difference
type; the edge has the thinnest line.

Table 2. Statistical parameters for an edge pointing from a source node x to a target node ya.

aParameter

Description

FormulaAbsolute cooccurrence

Number of group members having both attributes of x and yoi

Relative conditional share

Fraction of group members with attribute of x, also having attribute of y

Conditional and unconditional target sharedifference

Absolute increase of relative conditional share compared to relative attribute share of y

Conditional and unconditional target sharequotient

Quotient of relative conditional share and relative attribute share of y

Subscript i refers to a definition for both groups. Let

be the absolute count of x and

The computation time for the generation of all the AAG’s edges
scales quadratically with the number of variables in the data set
and linear with the sample size.
In the last step, the nodes and edges are filtered by theirstatistical parameters to highlight the most relevant attributes
and conditional dependencies. A detailed description of thefiltering procedure is provided in Multimedia Appendix 1[41,44,45]. We represented the extracted data in a graphstructure using the graph data platform Neo4j [14] and thegraphical user interface Neo4j Bloom (Neo4j, Inc) [19]. The
graph structure can be navigated by mouse clicking and via asearch bar typing prompts in English.
Figure 1 [46] shows a minimal fictional example of an AAG
with 2 nodes capturing fictional data about history of
hypertension and high C-reactive protein (CRP) measurements
as well as their relationship in participant group 1 (control
group) and 2 (disease group). We conducted a hypothetical data
analysis, as we intend the AAG to be used. For CRP
measurements (mg/dL), a fictional reference range of 0.0-0.8
was derived. From the difference of the relative total share andhttps://medinform.jmir.org/2024/1/e49865

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be the relative attribute share of y.

relative attribute share, we can infer existing missing values ongroup 2 for both attributes. In group 1, no missing values exist
because relative total share and relative attribute share do notdiffer. Regarding the quotient of relative attribute shares, we
can infer group 2’s participants being almost twice as likely toshow a high CRP value. Thus, a CRP measurement >0.8 mg/dL
might be highly related to the condition or property of group 2compared to participants of group 1. A history of hypertension
appears approximately 30% more often in group 2, giving a
60% proportional increase. As a result, its node is labeled ashighly related to the condition or property of group 2. Viewing
the data of the edges, we find that almost all participants witha high CRP measurement also have a history of hypertension
in both groups. Therefore, high CRP values could be an indicatorfor hypertension in both fictional groups. Conversely, only
approximately one-third of participants with a history of
hypertension also show high measurements of CRP. This patternof conditional relationship is similar between groups and could
thus be independent of the group definitions, for example,
medical condition and control group.

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Figure 1. An attribute association graph with 2 nodes represented as spheres and 2 edges represented as lines with arrows. The arrow indicates thetarget node of the edge. Node parameters are depicted next to the spheres. Labels are shown inside the spheres with one label per line. The edge’s
parameters are depicted on top of the edge. The heading above the edge’s parameters specifies the edge type (MEDIUM_COND_DIFF for medium
conditional difference, HIGH_COND_DIFF for high conditional difference). Absolute counts (groupAbsCounts), relative total shares(groupRelShareTotals), relative attribute shares (groupRelShareAttrs), difference between relative attribute shares (diffRelShareAttr), quotient between
relative attribute shares (quotRelShareAttr), absolute cooccurrence (groupAbsCoOccurs), relative conditional share (groupRelShareConds), difference
to target relative attribute share (groupDiffTargets), and quotient to target relative attribute share (groupQuotTargets) are depicted as lists with the score
for group 1, followed by the score for group 2. Group 2 is the disease group (posGroup), and group 1 is the control group (negGroup). The color of thesphere indicates the deviation label of the node: orange (related) and red (highly related). The size of the sphere indicates the frequency label frommedium (frequent) to the largest size (highly frequent). The line thickness indicates the type of edge from medium (medium conditional difference) tothickest (high conditional difference). Descriptions of all parameter names, edge types, labels as well as color, size and thickness encoding can be foundin the ZFDM repository. CRP: C-reactive protein.

Dashboard
To complement the AAG, we generated a dashboard using the
NeoDash (Neo4j, Inc) [17] toolkit. With the dashboard, users
can investigate the average and distribution of metric variables
across participant groups in more detail. In addition to thecardiovascular disease and control cohorts, the group of allparticipants contained in the Hamburg City Health Study(HCHS) data set was included. We developed 2 different tabs.
The first tab allows for comparison of participant groups. We
included the sizes of disease and control group. In addition,
variable distributions can be compared between groups. For
this purpose, we applied the following workflow to all metricvariables and participant groups. First, we measured the variableaverage within the group. Second, we generated a binned
distribution by rounding the measurements to multiples of 0.1,
0.5, 1, 5, 10, or 50 depending on the SD within the group. Bins
containing <0.5% of the participants or <3 participants are
summarized. We removed distributions without any bins
fulfilling these requirements. The user can select 2 groups andvariables for the distributions shown in the first tab of thedashboard. The averages of all metric variables for all 3 groups
are shown in the first tab as well. To make them comparable ina figure, the averages of each variable are normalized by the
maximum average of that variable. In the second tab, the user
can investigate the relationship between 2 variables within aparticipant group. For the first variable, the generated binned
distribution across the group is shown. For the second variable,
we use precalculated averages of participants within a bin. The
x-axis of the resulting figure shows the bin values of the firstvariable, and the y-axis shows the average value of the second
variable for participants of that bin.

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HCHS Data Set and Cohort Selection
To evaluate the AAG and dashboard, we used an exemplary
data exploration workflow of a large epidemiological cohort
study. We compared the results with findings from literatureand standard statistical analysis. The HCHS is a single-center,
prospective, observational, population-based cohort study of
45,000 randomly selected residents of the metropolitan regionof Hamburg, Germany, aged between 45 and 74 years. The
study design has been published [47], and the study is registered[48]. The study focuses on major chronic diseases, causes fortheir development, as well as factors for survival and support
for life in survivorship. The study considers >6000 properties
per participant. The data are raised from 18 examinations,primarily targeting major organ systems, as well as
questionnaires about medical and family history, physicalcondition, dietary habits, lifestyle, and various other topics. The
examinations will be repeated after 6 years to obtain large-scale,
long-term assessments. For this analysis, the HCHS committee
provided a subset of the whole HCHS data set focusing on
cardiovascular and cancer diseases. The subset consists of 524
selected attributes for the first 10,000 participants enrolled in
HCHS, including information about laboratory analyses;
electrocardiography (ECG); magnetic resonance imaging;vascular ultrasound examinations; blood pressure measurements;
cardiovascular and cancer medical history questionnaires; aswell as dietary, lifestyle and sleeping habits. We selected 131(25%) of these 524 attributes, translated their descriptions toEnglish, assigned labels to each variable to broad variablegroups, and added Systematized Nomenclature of Medicine
Clinical Terms [49] or Logical Observation Identifier Names
and Codes [50] codes. When no directly fitting code was found,
we chose the code of a related term. A full list of all variables,
descriptions, labels, vocabulary codes, and data types can be
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freely accessed [46]. In some cases, the reference ranges
calculated for the AAG deviated from the usual reference ranges
known from the literature because of a different value
distribution in the HCHS data set. In these cases, we manually
adjusted the reference intervals according to the Merck Manualof Diagnosis and Therapy manual [44]. A full list of the adjusted
reference ranges can be found in Table S1 in Multimedia
Appendix 1. In this work, we focused on participants with ageneral cardiovascular condition. We included participants inthis cohort who met any of the following criteria: showed any
pathological cardiovascular findings during the cardiac magnetic
resonance imaging examination; had a missing sinus rhythm;
had a finding of atrial fibrillation or flutter in the ECG check;or reported a medical history of cardiac infarction, coronary
artery disease, angina pectoris, congestive heart failure,
myocarditis, or valvular endocarditis in the questionnaire. As
a result, the disease cohort contained 1917 participants. In
addition, we derived the control group of 8083 participants notexhibiting any of the conditions and findings.

Bellmann et al

Participant Recruitment
The study participants for the user tests included 10 physiciansfrom various specialties and fields of activity. This group
comprised 2 anesthetists, 2 cardiologists, 1 neurologist, 1
radiologist, 2 resident doctors in the field of child and adolescent
psychiatry, 1 medical student in the final year, and 1 physician
working in the public health sector. With this heterogeneousgroup composition, we aimed for a comprehensive usability
assessment of the presented methods across the clinical field.
The recruitment of participants was conducted on a voluntarybasis, supported by the research team’s network. It was assumed
that the participants had no bias regarding the AAGs, as themethodology and visualization had not been officially released
and were therefore not used by the participants at the time of
the user test.

Ethical Considerations

User Tests

The HCHS study was approved by the Ethics Committee of the
Hamburg chamber of physicians (PV5131) and has beenregistered at ClinicalTrial.gov (NCT03934957).

Study Design

Results

We conducted a user test using a mixed methods approach toevaluate the usability of the AAG. The associated questionnaire
can be found in Multimedia Appendix 2. We did not consider
the proposed dashboard in the user test, as dashboards are widelyused in the medical domain [11,51-53]. The usability testing
consisted of 3 main parts in the following order: (1) in a
30-minute preparation phase, participants independently worked
through the AAG user manual and the Neo4j Bloom overview
website [19]; (2) a semistructured interview with open feedbackquestions and user tasks was conducted; and (3) participants
completed the System Usability Scale (SUS) [54]. The SUS isa standardized and validated instrument for usability testing ofsystems, frequently used in this context [25,52,55,56]. The SUS
comprises 10 questions rated on a 5-point Likert scale. The total
score, ranging from 0 to 100, is calculated from all questions
to ensure comparability. With the addition of user tasks andfeedback questions tailored to the AAG, we aimed to create
additional insights on the usability of the specific parts of thegraph as well as observe the data exploration conducted by theusers. The user tasks can be grouped into three categories: (1)reproducing the introduced labels and metric parameters; (2)using the application functionalities necessary for exploration;and (3) conducting a free exploration of 2 AAG subgraphs ofthe HCHS data set: first, the 10 nodes with the highest quotientof relative attribute shares related to the cardiovascular diseasegroup; and second, the subgraph of nodes regarding laboratory
measurements. The user results for tasks of categories 1 and 2
were evaluated as correct or incorrect by the authors. During
the exploration of the 2 subgraphs, the users were asked toverbalize their findings, and the results were recorded andcategorized by the authors. The participant answers to the open
feedback questions were also broadly categorized by the authors.

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Exemplary Data Analysis
We have generated the AAG for the disease and control groupwithin the HCHS data set based on our definition of a generalcardiovascular disease. In this paragraph, we give an exemplary
data analysis using the graph and some aspects of the dashboard.
This analysis was conducted by the authors of this work
independently of the exploration of users during the usability
test. The analysis is meant to showcase the usability of the graphrepresentations and is by no means exhaustive. The Neo4j
database dumps, configuration files, and user guide can be freely
accessed [46]. In addition, the software tool used to generate
AAGs was made publicly available [57] and will be presented
in an upcoming publication. To assess the compatibility of thepresented methods with standard statistical inference, we
calculated Pearson correlation coefficients [41], 1-tailed CIs at
the confidence level of 95% using the Fisher transformation[45], and P values for 1-tailed null hypothesis testing ofstatistical independence for all associations discussed in thefollowing data analysis. The results can be found in Table S2
in Multimedia Appendix 1.
For brevity, we define the cardiovascular disease group as group
A and its control group as group B. Group A contains 1917
participants, and group B contains 8083 participants. The
generated AAG is shown in Figure 2 [14,46]. The nodes labeled
as related or highly related form a cluster in the middle of thegraph with the highest density of edges between them. Most of
the inverse and highly inverse labeled nodes are primarily
located on the periphery of the graph with many interconnections
but few connections to the inner cluster. This observation
indicates a clear distinction highlighted by the graph between
the attributes based on their cooccurrence with cardiovasculardisease within the HCHS data set.

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Figure 2. The attribute association graph describing the cardiovascular disease cohort and control group extracted from the Hamburg City Health Study
data set. Screenshot taken from the Neo4j Browser. Nodes are depicted as spheres, and edges are depicted as lines between spheres. The color of thesphere indicates the deviation label of the node: vanilla (unrelated), orange (related), red (highly related), turquoise (inverse), and blue (highly inverse).
The size of the sphere indicates the frequency label from the smallest (infrequent) to the largest size (highly frequent). The line thickness indicates thetype of edge from thinnest (low conditional difference) to thickest (high conditional difference). The text inside the node spheres states the variable
name, followed by the value of the attribute. Data and variable descriptions can be found in the ZFDM repository. For a higher-resolution version ofthis figure, see Multimedia Appendix 3. Variable descriptions are found in Multimedia Appendix 4.

For a more detailed analysis of this AAG, we focused on thelaboratory results data shown in Figure 3 [14,46]. Within thegraph, 3 nodes are labeled as highly related, along with several
adjacent nodes labeled as related. The nodes representing
glomerular filtration rate <60 mL/min/1.73 m2 (“GFR-CKD,low”) and creatinine levels >1.2 mg/dL (“creatine, high”) are
identified as highly related and are interconnected. Furthermore,they are also connected to the node representing elevatedpotassium levels >4.15 mmol (“potassium, high”) through highconditional difference relationships. The presence of a low
glomerular filtration rate, high creatinine, and elevatedpotassium levels are all correlated with chronic kidney disease[58], which in turn is a risk factor for the development ofcardiovascular conditions [58,59]. Thus, all 3 laboratory results
are associated with heart disease in clinical settings [60], whichcoincides with the findings presented in this graph. The
respective 95% CIs lie fully above 0 for creatine and potassium
levels and fully below 0 for the glomerular filtration rate. The
relative attribute share of the nodes for glomerular filtration rate
<60 mL/min/1.73 m2 (“GFR-CKD, low”) in group A is, withhttps://medinform.jmir.org/2024/1/e49865

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12%, more than twice as high as the relative total share. This
indicates missing values for glomerular filtration ratemeasurements in participant with a cardiovascular condition.
The related node in the center of Figure 3 (“proBNP, high”)
represents elevated N-terminal prohormone of B-type natriuretic
peptide (proBNP) levels >125 ng/L, which were identified asa biomarker for cardiac diseases [61]. With 47%, group A has
a 1.7-fold increased relative attribute share for this attributecompared to group B. The associated CI for the Pearson
correlation coefficient is strictly positive. The node has 3
incoming edges of high conditional difference. Of these 3 edges,
2 describe the relationship between low glomerular filtration
rate and high creatinine levels to elevated proBNP levels.
Participants of group B with 1 of these properties are at least
1.6-fold more likely to show elevated proBNP levels >125 ng/Lcompared to general patients of group B. The same pattern canbe observed in group A, which is consistent with the impact ofworsening kidney function on proBNP concentration [62,63].
The CIs of the Pearson correlation coefficient of proBNP andglomerular filtration rate is strictly negative, and the CI for
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creatinine and proBNP levels is fully positive. The thirdincoming edge is of type high conditional difference. It indicatesa relationship between hemoglobin levels <13 g/dL (“HBKC,low”) and elevated proBNP measurements. Although the node
for low hemoglobin levels is labeled as unrelated, measurements<13 g/dL appear with a 1.4-fold increase in group B comparedto group A. The associated CI is close to, but fully above, 0.
Interestingly, participants of both groups with low hemoglobin
levels are approximately 1.5-fold more likely to exhibit highproBNP measurements compared to general participants of theirgroup, a phenomenon observed in other studies [64-66]. The
Pearson correlation coefficient CI for proBNP and hemoglobin
levels are close to, but fully below, 0. Overall, these 3
relationships confirm that while elevated proBNP levels serve
as a biomarker for cardiac conditions, other factors may also
contribute to its elevation.
Figure 4 was extracted from the dashboard and discloses therelationship of hemoglobin and proBNP levels across the whole

Bellmann et aldata set in more detail. Average proBNP values increase forparticipants with hemoglobin levels <13 g/dL. Interestingly,
proBNP levels also increase in participants with highhemoglobin values >17 g/dL. For further investigation, we
returned to the graph and inspected the node (“HBKC, high”)
for high hemoglobin levels >15.5 g/dL. This threshold isexceeded by 21.5% of the participants in group A and by only
15.3% of the participants in group B. These observations alignwith the calculated, strictly positive CI and findings of otherstudies associating high hemoglobin concentrations withcardiovascular disease [67,68]. The third node (“cholesterol,low”), which is labeled as highly related, can be seen in thelower center of Figure 3. It represents total cholesterol levels<150 mg/dL, which is exhibited by 16.3% of group A and only
5.5% of group B. Conversely, total cholesterol levels >200
mg/dL are observed in 47.3% of group A and 61.2% of group
B. As a result, the corresponding node (“cholesterol, high”) islabeled as inversely related.

Figure 3. A subgraph of the full attribute association graph describing the cardiovascular disease cohort and control group extracted from the Hamburg
City Health Study data set. Screenshot taken from the Neo4j Browser. Only nodes representing laboratory measurements and edges between them areshown. The color of the sphere indicates the deviation label of the node: vanilla (unrelated), orange (related), red (highly related), turquoise (inverse),
and blue (highly inverse). The size of the sphere indicates the frequency label from the smallest (infrequent) to the largest size (highly frequent). Theline thickness indicates the type of edge from thinnest (low conditional difference) to thickest (high conditional difference). The text inside the node
spheres states the variable name, followed by the value of the attribute. Data and variable descriptions can be found in the ZFDM repository. CKD:
chronic kidney disease; CRP: C-reactive protein; GFR: glomerular filtration rate; HBKC: hemoglobin level; HDL: high-density lipoprotein; LDL:
low-density lipoprotein; proBNP: prohormone of B-type natriuretic peptide. For a higher-resolution version of this figure, see Multimedia Appendix
5. Variable descriptions are found in Multimedia Appendix 4.

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Figure 4. (A) Distribution of hemoglobin levels (g/dL) across all participants of the Hamburg City Health Study data set. (B) The average N-terminal
prohormone of B-type natriuretic peptide (proBNP) level (ng/L) per participant of the data set with a rounded hemoglobin level specified on the x-axis.
This figure is a screenshot from the dashboard.

However, in Figure 5, we can observe that the highest numberof participants in both groups exhibit a slightly elevated totalcholesterol level of 210 mg/dL. Next, we inspected the 2 nodes(“CholLDL, normal” and “CholLDL, high”) for low-density
lipoprotein (LDL) cholesterol levels. Measurements >130 mg/dL(“CholLDL, high”) appear with a 1.3-fold increase in group B.
LDL cholesterol levels <130 mg/dL (“CholLDL, normal”)
appear in 68.1% of group A and 59.7% of group B. Theseobservations are peculiar because elevated total and LDLcholesterol are commonly recognized as important risk factors
for cardiovascular diseases [69-73]. A similar pattern can beinferred from the 2 nodes (“CholHDL, low” and “CholHDL,
high”) for measurements of high-density lipoprotein (HDL)
cholesterol. Levels <45 mg/dL appear with a 1.7-fold increasein group A, whereas measurements >83 mg/dL showed a
1.8-fold increase in group B. This observation coincides withthe widely accepted inverse association of HDL levels withcardiovascular diseases [74,75]. It is noteworthy that the nodes
for high LDL and HDL cholesterol levels share an edge with
the node for high total cholesterol levels. The same holds true
for low HDL, normal LDL, and low total cholesterol
measurements. These edges are all labeled with “highhttps://medinform.jmir.org/2024/1/e49865

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conditional difference.” The CIs for all 3 cholesterol
measurements and the membership to group A are strictly
negative. The CIs for total cholesterol levels and HDL as well
as LDL cholesterol measurements are strictly positive, with thecorrelation coefficient of LDL and total cholesterol being close
to 1. In summary, reduced overall cholesterol, LDL cholesterol,and HDL cholesterol levels appear more often in the
cardiovascular disease group compared to the control group andare associated with each other. As stated earlier, this observation
contradicts the commonly accepted association of elevatedoverall and LDL cholesterol with cardiovascular diseases. It
could be attributed to the widely used therapy with statins [76],which mainly targets the reduction of LDL and overallcholesterol [77]. On the basis of this idea, the high conditional
difference relation between elevated creatinine levels and low
total cholesterol measurements found in Figure 3 and theassociated strictly negative CI for the Pearson correlation
coefficient could be explained by statin-associated muscle
symptoms [78]. However, additional information about the
medication history of the participants would be required andcould be a starting point for further investigation.

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Figure 5. (A) Distribution of total cholesterol levels (mg/dL) for the cardiovascular disease group (group A) and (B) its control group (group B) derived
from the Hamburg City Health Study data set. This figure is a screenshot taken from the dashboard.

User Tests
The participants indicated a work experience in the current field
ranging from 1 to 10 years, with an average of 5.8 years. The
data exploration tools mostly used by the participants were
SPSS (IBM) [79], R [80], and Microsoft Excel (Microsoft) [81].
No users mentioned any prior experience with graph-based
statistical analysis tools. The results of the user test can be foundin Multimedia Appendix 3.
In Figure 6, the results of the SUS questionnaire are shown andrange from 62.5 to 85.0. The mean of 70.5 indicates the passingof usability criteria [82] and a rating of “good” usability [83].
In addition, physicians rated the user-friendliness on a scalefrom 1 (very bad) to 10 (very good), with a mean of 7.0 in
accordance with the SUS results.
In Figure 7, the percentage of the 10 participants with successful
completion is shown for each user task. The average score acrossall tasks is 81.4%, with 6 (86%) of 7 navigation tasks being
correctly completed by all participants. However, only 20%(2/10) of participants queried successfully for the 10 nodes moststatistically associated with the disease group by the quotientof relative attribute shares. Regarding the description tasks ofcategory 1, all but 1 task of reproducing label and parameter
meaning was completed by at least 70% (7/10) of users. An
exception was task C3.2 where participants should describe themeaning of the edge parameter for the difference of relative
conditional share and relative attribute share of the target node.

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This task was only completed correctly by 30% (3/10) of theparticipants. In addition, only 30% (3/10) of the participants
found the parameter names for nodes understandable, and only
10% (1/10) of the participants classified the edge parameternames as clear.
During the free data exploration, all participants noticed theunusually low levels of total and LDL cholesterol in the
cardiovascular disease group compared to the control group,
which is also discussed during the exemplary data analysis
conducted by the authors. In addition, 40% (4/10) of theparticipants suspected this association to be caused by
medication not represented in the data set. Overall, 60% (6/10)of the participants discussed ECG signals, and 60% (6/10) of
the participants discussed kidney metabolism. Moreover, 70%(7/10) of the physicians mentioned the results of their data
exploration to be plausible, except for total and LDL cholesterol
unprompted. Regarding the answers to the open feedbackquestions, 80% (8/10) of the participants mentioned the colors
and sizes of nodes to be helpful, and 40% (4/10) of theparticipants referred to the display of attribute connections asedges becoming apparent. Moreover, 30% (3/10) of theparticipants mentioned the benefit of initial data exploration
without the need for numerical values. As to disadvantages of
the AAG, 30% (3/10) of the users mentioned the edge definitions
being hard to understand, 20% (2/10) assessed the graphs to be
too crowded to get a good overview, and 20% (2/10) stated thatthey would need more practice to use the tool efficiently.

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Figure 6. The System Usability Scale (SUS) score for each of the 10 participants of the user test. In addition, the average score is represented by ahorizontal dashed line in red.

Figure 7. Correct task completion by participants during the user test in percentage. Task numbering is taken from the questionnaire. A short descriptionof the tasks is given on the left. Bars for description and reproduction of labels and metrics (task category 1) are depicted in turquoise. Bars for graphnavigation tasks (task category 2) are depicted in pink. Average percentages of correct tasks are plotted as dashed lines for description, navigation, andall tasks.

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Discussion
Principal Findings
In this work, we presented the AAG for visual exploration of
medical data sets using disease and control cohorts. The graphstructure represents attributes as nodes and identifies as well as
visually highlights attributes, which are linked to the observed
disease by robust statistical metrics. Relations between attributes
are captured as edges by conditional frequencies. As a result,attributes associated with the observed disease are visuallyclustered and clearly separated from attributes, which areassociated with the control group. The graph structure detects
and handles missing values without the need for data deletion.
The usability of the AAG and dashboard was assessed using an
exemplary data analysis. All but 1 association of laboratory
measurements and cardiovascular diseases extracted from the
HCHS data set are in line with findings from the literature. The
exceptions are unusually low total and LDL cholesterol levels
in participants with cardiovascular disease, which might becaused by lipid-lowering therapy. All results extracted from the
AAG were confirmed by standard statistical inference using
null hypothesis testing and CI for the Pearson correlation
coefficient. In addition, a user test with physicians was
conducted using the standardized SUS questionnaire,
nonstandardized open feedback questions as well as user tasks,
and a free data exploration. The SUS score of 70.5% and average
successful task completion of 81.4% show a general acceptanceand good usability of the AAG. After the initial 30-minutepreparation period, all users were able to navigate the graph andcould extract medical knowledge that they considered plausible
and meaningful. In addition, all participants identified theunusual lipid levels in participants of the cardiovascular diseasegroup and some suspected medication not represented in thedata set to be the cause. The encoding of statistical results bycolor, size, and clustering of nodes as well as thickness of edges
was seen as helpful by the users. The users regarded the tool as
useful for accessible hypothesis formation during the initialresearch phase.

Comparison With Prior Work
Other existing data-driven approaches based on graph structures
focus mainly on the connection of different data sources as
knowledge graphs [23,24,26] or direct clinical decision support
through outcome prediction [27,31,35,38,84-86]. To ourknowledge, a graph structure capturing statistical measurementsof a medical data set using disease and control cohorts with aclear focus on interpretability and visualization is a novel
approach. In addition, as our proposed methods consider single
attributes and pairs of attributes, they are robust to
high-dimensional data, which pose a problem for many otherstatistical models applied to the medical domain [87]. We
believe that the usability of graph-based visualizations in the
medical field is rarely assessed using standardized tests such as
the SUS questionnaire. The only other results known to theauthors reported a slightly lower SUS score of 64.4 [25].
Regarding the graph-based statistical framework, we see ourwork closest related to Bayesian networks [29] and association
rule learning [36]. While Bayesian networks can hold strong
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Bellmann et al
predictive power [88], the choice of prior distribution andsensitivity to data quality can be challenging for clinicians [89].
In association rule learning, conditional relationships between
attributes are partially expressed through the confidence
parameter, which is quite similar to our methodology in thatregard. However, we enrich the added condition with semantics
by calculating difference and quotient to the unconditional
relative frequencies. Finally, none of the 2 methods measurestatistical differences between disease and control cohorts. We
believe this to be vital in our approach for generation of insightand adoption in the medical domain.

Limitations
We intended the AAG and dashboard as a compact visualization
for data exploration in the initial phase of research projects. We
aimed to incorporate easily interpretable, robust metrics in theform of conditional and unconditional absolute and relative
frequencies as well as their deviations between disease andcontrol cohorts. However, because of this choice of metrics, theaccuracy could be lower when used in prediction tasks comparedto, for example, Bayesian networks or other nonlinear models.
In addition, CIs and null hypothesis significance testing play a
key role in statistical inference of medical data [90]. They are
not incorporated into the methods presented here but could bea follow-up to the initial exploration using the AAG. Finally,
temporal data cannot be handled with the proposed methodology
in the current form, and Markov models [33] could be appliedinstead.
Regarding the usability of the visualization, the results of theuser test indicate a need for simplification of the parameternames regarding the statistical measurements. In addition, thecomparison of conditional and unconditional frequencies
captured in the edges of the graph structure was not accessible
enough for the users. Moreover, the prompt for retrieval ofnodes most associated with one of the groups was considered
too lengthy by the users. The authors will incorporate thisvaluable feedback in the next update iteration of the presented
methods.

Conclusions
In this work, we introduced the AAG, a novel graph-based
representation of statistical data combined with a dashboard.
These structures can be visually explored and allow for data
analysis tailored to the needs of the medical domain. The
usability of the graph structure and dashboard was confirmed
by user tests conducted with physicians. In addition, the validityof the incorporated statistical analysis was assessed through an
exemplary data analysis of a large epidemiological study, andits compatibility with standard statistical methodology andfindings from the literature was established. For the future, it
might be of interest to enable clinicians in generating their own
AAGs without the need for programming experience as anextension to their existing data analysis workflow. To achievethis, we developed a software package [57], which will bepresented in an upcoming publication. We think that accessible
data analysis and intuitive presentation for clinicians and patientsis the way forward in a world of ever-growing data availability
and complexity.

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Acknowledgments
The authors would like to thank the Hamburg City Health Study (HCHS) committee for granting access to the HCHS cohort
study data set. The authors received no specific funding for this work. In terms of overall funding for the underlying HCHS,
various institutes and departments at the University Medical Center Hamburg-Eppendorf contribute with their own individual
and scaled budgets. The HCHS is additionally funded by the Joachim Herz Foundation, the Leducq Foundation (grant 16 CVD03), the euCanSHare grant agreement (grant 825903-euCanSHare H2020), and the Innovative Medicine Initiative (grant 116074).
The HCHS is further supported by Deutsche Gesetzliche Unfallversicherung (DGUV), Deutsches Krebsforschungszentrum(DKFZ), Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Deutsche Stiftung für Herzforschung, Seefried Stiftung,
Bayer, Amgen, Novartis, Schiller, Siemens, Topcon, and Unilever and by donations from the “Förderverein zur Förderung der
HCHS e.V.” and TePe (2014). Sponsor funding has in no way influenced the content or management of this study. RT reports
research support from the German Center for Cardiovascular Research (DZHK), the Kühne Foundation, the Joachim Herz
Foundation, the Swiss National Science Foundation (Grant NoP300PB_167803) and the Swiss Heart Foundation.

Data Availability
The data sets generated and analyzed during this study are available in the ZFDM repository [46]. The HCHS data set itself is
not publicly available due participant data privacy. The attribute association graph (AAG) and dashboard data as Neo4j dumps,
Neo4j Bloom configuration as JSON files, as well as a detailed installation and user guide as PDF file and descriptions for all
variables of the Hamburg City Health Study data subset can be found in the repository [46]. Adjusted reference ranges and filter
criteria for the AAGs, Pearson correlation coefficients, as well as the user test questionnaire and results can be found in Multimedia
Appendices 1, 2 and 6. Code repository, Python package, and software tool to create custom AAGs [57] will be described in an
upcoming publication.

Conflicts of Interest
RT reports speaker honoraria/consulting honoraria from Abbott, Amgen, Astra Zeneca, Psyros, Roche, Siemens, Singulex andThermo Scientific BRAHMS. RT is co-founder and shareholder of the ART-EMIS Hamburg GmbH, which holds an international
patent application on a computing device for estimating the probability of myocardial infarction (International Publication Numbers
WO2022043229A1, TW202219980A).

Multimedia Appendix 1
Attribute association graph filter criteria, manually adjusted reference ranges, Pearson correlation coefficients, CIs at the confidence
level of 95% using the Fisher transformation, and P values for 1-tailed null hypothesis testing of statistical independence.
[DOCX File , 34 KB-Multimedia Appendix 1]

Multimedia Appendix 2User test questionnaire.
[PDF File (Adobe PDF File), 161 KB-Multimedia Appendix 2]

Multimedia Appendix 3
The attribute association graph describing the cardiovascular disease cohort and control group extracted from the Hamburg City
Health Study data set. Screenshot taken from the Neo4j Browser. Nodes are depicted as spheres, and edges are depicted as lines
between spheres. The color of the sphere indicates the deviation label of the node: vanilla (unrelated), orange (related), red (highly
related), turquoise (inverse), and blue (highly inverse). The size of the sphere indicates the frequency label from the smallest(infrequent) to the largest size (highly frequent). The line thickness indicates the type of edge from thinnest (low conditional
difference) to thickest (high conditional difference). The text inside the node spheres states the variable name, followed by thevalue of the attribute. Data and variable descriptions can be found in the ZFDM repository.
[PNG File , 4960 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Variable descriptions for Figures 2 and 3.
[XLSX File (Microsoft Excel File), 24 KB-Multimedia Appendix 4]https://medinform.jmir.org/2024/1/e49865

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Bellmann et alMultimedia Appendix 5
A subgraph of the full attribute association graph describing the cardiovascular disease cohort and control group extracted fromthe Hamburg City Health Study data set. Screenshot taken from the Neo4j Browser. Only nodes representing laboratory
measurements and edges between them are shown. The color of the sphere indicates the deviation label of the node: vanilla(unrelated), orange (related), red (highly related), turquoise (inverse), and blue (highly inverse). The size of the sphere indicatesthe frequency label from the smallest (infrequent) to the largest size (highly frequent). The line thickness indicates the type ofedge from thinnest (low conditional difference) to thickest (high conditional difference). The text inside the node spheres states
the variable name, followed by the value of the attribute. Data and variable descriptions can be found in the ZFDM repository.
CKD: chronic kidney disease; CRP: C-reactive protein; GFR: glomerular filtration rate; HBKC: hemoglobin level; HDL:
high-density lipoprotein; LDL: low-density lipoprotein; proBNP: prohormone of B-type natriuretic peptide.
[PNG File , 1350 KB-Multimedia Appendix 5]

Multimedia Appendix 6
Results of the user test.
[XLSX File (Microsoft Excel File), 25 KB-Multimedia Appendix 6]

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Abbreviations
AAG: attribute association graph
CRP: C-reactive protein
ECG: electrocardiography
HCHS: Hamburg City Health Study
HDL: high-density lipoprotein
LDL: low-density lipoprotein
proBNP: prohormone of B-type natriuretic peptide
SUS: System Usability Scale
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Bellmann et al

Edited by C Lovis; submitted 12.06.23; peer-reviewed by A Scherag, L Loeb, M Bjelogrlic, C Gaudet-Blavignac; comments to author
28.08.23; revised version received 11.10.23; accepted 04.05.24; published 24.07.24
Please cite as:
Bellmann L, Wiederhold AJ, Trübe L, Twerenbold R, Ückert F, Gottfried K
Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using EpidemiologicalStudy Data
JMIR Med Inform 2024;12:e49865
URL: https://medinform.jmir.org/2024/1/e49865
doi: 10.2196/49865
PMID:©Louis Bellmann, Alexander Johannes Wiederhold, Leona Trübe, Raphael Twerenbold, Frank Ückert, Karl Gottfried. Originallypublished in JMIR Medical Informatics (https://medinform.jmir.org), 24.07.2024. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, isproperly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as wellas this copyright and license information must be included.

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