Multivariate Time Series Visualization for a Single Individual: A Scoping

Review Using PRISMA-ScR

Hugo Le Baher

1,2,4 a

, J

ome Az

1 b

, Sandra Bringay

1,3 c

, Pascal Poncelet

1 d

Arnaud Sallaberry

1,3 e

and Caroline Dunoyer

4,5 f

LIRMM, UMR 5506, University of Montpellier, CNRS, Montpellier, France

5 DEGR

ES, Paris, France

AMIS, Paul-Val

ery University of Montpellier, Montpellier, France

Health Data Science Unit, Public Health Service, Centre Hospitalier Universitaire de Montpellier, Montpellier, France

Desbrest Institute of Epidemiology and Public Health, UMR UA11, University of Montpellier — Inserm, Montpellier,

France

{hlebaher, aze, sandra.bringay, poncelet, arnaud.sallaberry}@lirmm.fr, c-dunoyer@chu-montpellier.fr

Keywords:

Scoping Review, Visualisation Design and Techniques, Temporal Data, Multivariate Data, Healthcare.

Abstract:

The digitization of hospital information systems is becoming widespread, enabling the increasing integration

of interactive visualization methods into decision support systems. This development facilitates the anticipa-

tion of critical risks in monitored patients and helps reduce the workload of healthcare providers. However,

Electronic Health Records (EHRs) contain large, heterogeneous, and temporal data. Then, providing tools to

understand these complex data is a challenge. Using PubMed and Google Scholar, we conducted a search for

articles using keywords related to time, visualization, and data. Out of 3,197 retrieved articles, we identiﬁed

111 relevant ones through clustering. Applying exclusion criteria to focus on implemented prototypes, we

manually annotated 21 articles for our review. This exploratory literature analysis reveals that while this re-

search area has garnered recent interest, it demonstrates limitations in the proposed solutions. Few approaches

employ temporal axis distortion, and no approach in the medical domain visually integrates model predictions.

The study highlights preferred functionalities for the visual representation of multivariate temporal data, such

as parallel time series and hierarchical views.

1 INTRODUCTION

The digitization of healthcare systems has recently

experienced signiﬁcant development. It promises to

reduce diagnostic and treatment errors, avoid redun-

dant testing, and guide more efﬁcient allocation of

healthcare resources, while fostering innovation in

preventive and therapeutic approaches. Healthcare

professionals use them daily to make critical deci-

sions and monitor the effects of treatments or medical

procedures in typically high-pressure environments.

These records improve the continuity and relevance

of care while facilitating communication and coordi-

https://orcid.org/0000-0003-3107-7070

https://orcid.org/0000-0002-7372-729X

https://orcid.org/0000-0002-2830-3666

https://orcid.org/0000-0002-8277-3490

https://orcid.org/0000-0001-7068-176X

https://orcid.org/ 0000-0002-6789-4075

nation between patients and healthcare professionals.

Researchers leverage them to extract medical data, for

instance, to improve patient inclusion rates in clini-

cal trials, while data engineers use them to correct er-

rors, among other applications. While the main lim-

itations of EHRs, such as integration within a single

information system and interoperability, are now be-

ing addressed, current EHR implementations can still

be improved.

The global assessment of a patient’s condition

is generally performed by analyzing the variations

over time of one or several heterogeneous vari-

ables. For instance, if the measured white blood cell

count and the patient’s temperature increase simul-

taneously, an ongoing infection is suspected. Sim-

ilarly, if hemoglobin levels and platelet counts de-

crease rapidly, a hemorrhage is likely suspected in

the patient. The goal of the current study is to iden-

tify visual features for representing temporal and mul-

tivariate data, i.e., involving more than two distinct

Le Baher, H., Azé, J., Bringay, S., Poncelet, P., Sallaberry, A. and Dunoyer, C.

Multivariate Time Series Visualization for a Single Individual: A Scoping Review Using PRISMA-ScR.

DOI: 10.5220/0013373000003912

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2025) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 975-982

ISBN: 978-989-758-728-3; ISSN: 2184-4321

975

variables evolving over time. More speciﬁcally, our

aim is to explore visualizations that provide both a

global and detailed view of heterogeneous multivari-

ate temporal data. To achieve this, we conducted an

exploratory review of the literature.

The motivations and state-of-the-art analyses con-

ducted on similar topics are discussed in Section 2.

The protocol and criteria used for the analysis are de-

tailed in Section 3. The results obtained through man-

ual annotation are presented in Section 3.6. Finally,

Section 4 synthesizes these results and concludes this

part with a discussion.

2 RELATED WORK

The visual analysis of multivariate temporal data is

used in various ﬁelds, such as ﬁnancial analysis (Yue

et al., 2019), history (Zhang et al., 2023), and story-

telling support (Shin et al., 2023). In the medical ﬁeld,

speciﬁcally, the visualization of data from Electronic

Health Records (EHR) has been extensively studied.

Previous research has synthesized the common char-

acteristics of these visualizations and their associated

functionalities, which will serve as the foundation for

this literature review.

During the 2010s, several reviews focused on the

visual representation of multivariate temporal data in

the medical domain. Speciﬁcally, the following sec-

tion highlights reviews addressing EHR visualization.

(Combi et al., 2010) established a taxonomy of visu-

alization methods, distinguishing whether they repre-

sent a single individual, such as a patient, or an en-

tire cohort. (Aigner et al., 2011)

provided a detailed

study of approaches for representing temporal data

for one or more individuals. Their study extended

beyond the medical domain. (Rind et al., 2013) fo-

cused on 14 speciﬁc approaches applied in the medi-

cal ﬁeld, also distinguishing between individual and

cohort representations. Finally, (West et al., 2015)

employed the PRISMA protocol (Preferred Reporting

Items for Systematic Reviews and Meta-Analyses)

(Moher et al., 2009) for a systematic review of EHR

data visualization approaches. They highlighted an

increase in publications in this domain up until 2012,

when their work was conducted. The authors em-

phasized the need for further research focused on

representing large-scale multivariate data on a single

screen and addressing the representation of missing

data, which remain relevant challenges.

Since 2019, new reviews have been proposed.

(Scheer et al., 2022) utilized an extension of the

See https://browser.timeviz.net/, accessed 01/08/2024,

for a comprehensive directory of techniques.

PRISMA protocol, PRISMA-ScR (PRISMA Exten-

sion for Scoping Reviews) (Tricco et al., 2018), to

describe 22 approaches focused on cohorts, differing

from the patient-centered approach that is the focus of

this chapter. (Wang and Laramee, 2022) proposed a

detailed taxonomy of 51 studies restricted to the med-

ical domain. The authors noted that the inclusion of

machine learning models is a recent trend. However,

their study did not distinguish between approaches

designed for individual patients and those for cohorts.

The systematic review by (Turchioe et al., 2019) fo-

cused on patient-oriented visualizations, highlighting

the current lack of solutions. Their taxonomy of dis-

played data, visual encodings, and evaluation meth-

ods compared 39 approaches, 80% of which repre-

sented longitudinal data using line charts.

The review of these studies reveals the absence of

a synthesis speciﬁcally dedicated to visualizing pa-

tient trajectories characterized by multivariate tempo-

ral data.

3 METHODOLOGY

We conducted an exploratory literature review focus-

ing on visualizations representing a single individ-

ual. Our approach extends the protocol proposed by

(Scheer et al., 2022), which is based on the PRISMA-

ScR protocol (Tricco et al., 2018). To assess the pres-

ence of functionalities not yet implemented in medi-

cal approaches, we also included studies from ﬁelds

outside of healthcare to explore the representation of

multivariate records of a single individual on a time-

line in a broader context. The complete protocol is

represented by a ﬂow diagram adapted from (Had-

daway et al., 2022), as illustrated in Figure 1. The

diagram details the successive steps of the protocol,

described in the following sections.

3.1 Information Sources and Query

Deﬁnition

To identify relevant articles across all application do-

mains, we queried the specialized healthcare database

PubMed

as well as the generalist database Google

Scholar

. This search was conducted on Novem-

ber 13, 2023, using the Python libraries Pymed

and

https://pubmed.ncbi.nlm.nih.gov/, accessed on

05/04/2024.

https://scholar.google.com/, accessed on 05/04/2024.

https://github.com/gijswobben/pymed, accessed on

05/04/2024.

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Identification

Selection

Inclusion

Identification of studies via databases and registers

Documents identified via databases:

Pubmed (n  2,166

Google Scholar (n  1,031

Total (n  3,197

Documents excluded before screening:

Excluded by BERTopic (n  3,086

Pre-selected Documents

(n  111

Documents sought

(n  141

Documents not retrieved

(n  7

Documents assessed

(n  134

Documents excluded:

1. No implementation (n  33

2./3./4. Other criteria (n  80

Included Documents in Review

(n  21

Citation-based identification

of new studies

Documents identified via:

Manual citation search (n  30

Section (3.1)

Section (3.2)

Section (3.4)

Section (3.3)

Figure 1: Flow diagram illustrating the number of documents at each stage of the PRISMA-ScR process. Diagram adapted

from the work of (Haddaway et al., 2022).

Scholarly

. The query used in (Scheer et al., 2022)

incorporates four key concepts: time, visualization,

data, and medicine. To broaden our scope and iden-

tify approaches for visualizing temporal data across

all ﬁelds, we retained only the keywords for the three

concepts: ”time”, ”visualization”, and ”data”. The

query is detailed in Table 1. At the end of this step,

we obtained 3,197 documents.

3.2 Semi-Automatic Preselection of

Articles

Among the query results, a signiﬁcant number of ar-

ticles were unrelated to temporal data visualization

interfaces. Manually annotating thousands of docu-

ments to select relevant articles is a costly process,

feasible only with a large team of evaluators. For this

reason, we opted for an initial selection using semi-

automatic tools.

We applied a topic extraction algorithm called

BERTopic (Grootendorst, 2022). BERTopic is based

on BERT (Bidirectional Encoder Representations

https://github.com/scholarly-python-

package/scholarly, accessed on 05/04/2024.

from Transformers), an unsupervised deep language

representation model that has shown strong perfor-

mance in topic extraction tasks (Devlin et al., 2019).

BERTopic uses a variation of TF-IDF to extract rel-

evant topics from texts and clusters them accord-

ing to these topics. The application of BERTopic to

the titles, abstracts, and keywords of the documents

obtained in the previous step, categorized the arti-

cles into 11 clusters, each corresponding to scien-

tiﬁc themes. To ensure that the automatic clustering

aligned with our preselection criteria, we included 19

articles cited in the reviews within our target domain

These reviews, described in Section 2, all pertain to

medical data visualization. Of the 19 articles, 14 were

placed in the same cluster, which we selected for fur-

ther analysis. This cluster contained a total of 111

documents and was associated with keywords such as

data, visualization, and visual. At the end of this step,

we retained 111 documents.

These 19 articles were manually identiﬁed through ci-

tation analysis and literature reviews conducted at both the

national and international levels.

Multivariate Time Series Visualization for a Single Individual: A Scoping Review Using PRISMA-ScR

977

Table 1: Search keywords by theme used in the PubMed engine. Due to the limitations of the Google Scholar search engine,

the sufﬁx “[tiab]”, which restricts the search to the title and abstract content only, was removed from each keyword. Addi-

tionally, keywords containing the “*” were expanded (e.g., “timeframe*” becomes “timeframe OR timeframes”).

Time

(“temporal data”[tiab] OR “temporal sequence*”[tiab] OR “temporal pattern*”[tiab] OR “temporal abstrac-

tion*”[tiab] OR “temporal event*”[tiab] OR “time sequence*”[tiab] OR “time series”[tiab] OR “time pe-

riod*”[tiab] OR “time frame*”[tiab] OR “timeframe*”[tiab] OR timeline*[tiab] OR time-oriented[tiab] OR

(time[tiab] AND events[tiab])) AND

Visualization

(visuali*[tiab] OR “visual analy*”[tiab]) AND

Data

(data[tiab] OR information[tiab])

3.3 Manual Citation-Based Search for

Articles

To complement the semi-automatic approach and en-

sure the most comprehensive selection of documents,

we manually extracted citations from review articles

dedicated to the representation of temporal data in the

medical domain (Combi et al., 2010) (Aigner et al.,

2011) (Rind et al., 2013) (West et al., 2015) (Turchioe

et al., 2019) (Scheer et al., 2022). Documents identi-

ﬁed in these literature reviews that were not included

in the results of the previous step were retained. This

additional search identiﬁed 30 additional documents,

bringing the total number of selected documents to

141.

3.4 Evaluation by a Reviewer

To ensure that the selected articles aligned with our

context, they were analyzed by a reviewer specializ-

ing in interface development for the medical domain.

Of the documents identiﬁed in the previous steps, 7

could not be retrieved, resulting in a ﬁnal total of 134

documents. Articles were retained if they met the fol-

lowing four criteria:

1. They present an implemented application tested

on real or simulated data. This excludes articles

describing a proof of concept, evaluation proto-

col, assessment of a visual or technical aspect, or

a literature review.

2. They provide a view of an individual targeted by

the user, extracted from a dataset. This excludes

articles presenting only a synthetic view of the en-

tire dataset or a subset of it.

3. They project data onto a single or synchronized

temporal axis across different views.

4. They display multivariate or multimodal data re-

lated to the targeted individual.

Among the 134 documents reviewed, 33 were ex-

cluded for failing to meet the ﬁrst criterion, and 80

did not meet the other three criteria. At the end of this

step, 21 documents were retained.

3.5 Information Extraction from

Articles

To extract relevant information, we developed a ques-

tionnaire that was reﬁned over two successive itera-

tions to characterize the content of the articles. These

criteria are presented in the following two paragraphs:

ﬁrst, the functionalities, and then the evaluation meth-

ods used. Speciﬁcally, the functionalities describe the

visual encodings chosen to represent the data or the

interactions enabling the user to modify the display.

In all cases, a criterion is satisﬁed if the stated as-

sertion applies to the content of the annotated article.

The assertion must be explicitly conﬁrmed within the

article text or illustrations.

Functionalities. The following criteria describe the

interface’s ability to present the entirety of the data

within the pathway in a detailed and readable manner:

• Context and Focus: The user can choose to dis-

play a speciﬁc area or element in detail while al-

ways being able to assess the context in which the

targeted area is embedded.

• Expansion and Reduction: The interface offers

the choice between a compact or detailed view of

an element.

• Minimum Granularity: The interface displays

the ﬁnest possible representation of the data.

The following criteria describe the visual methods

used to represent patient pathway records. Two crite-

ria describe the heterogeneous or structured nature of

the records:

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Table 2: Synthesis of the content of the 21 identiﬁed articles on functionalities and interactions, based on the criteria developed

in Section 3.5.

Context and Focus

Expansion and Reduction

Minimum Granularity

Multimodal

Hierarchy

Temporal Distortion

Time Series

Importance

Plages standard

Standard Ranges

Missing Values

Proximity Map

Prediction

Open Environment

General Individual Information

LifeLines (Alonso et al., 1998) ✓ ✓ ✓

KNAVE-II (Martins et al., 2004) ✓ ✓ ✓

MIDGARD (Bade et al., 2004) ✓ ✓ ✓ ✓ ✓ ✓

Caregiver (Brodbeck et al., 2005) ✓ ✓ ✓ ✓ ✓

CareVis (Aigner and Miksch, 2006) ✓ ✓ ✓ ✓ ✓ ✓

Lung Transp. (Pieczkiewicz et al., 2007) ✓ ✓ ✓ ✓

TimeLine (Bui et al., 2007) ✓ ✓ ✓ ✓ ✓ ✓

MIVA (Faiola and Newlon, 2011) ✓ ✓ ✓ ✓ ✓

VisuExplore (Rind et al., 2011) ✓ ✓ ✓ ✓

CareCruiser (Gschwandtner et al., 2011) ✓ ✓ ✓ ✓

UHS Lifelines (Hales et al., 2019) ✓ ✓

Tumor Board (Steinhauer et al., 2020) ✓ ✓ ✓

ClinicalPath (Linhares et al., 2023) ✓ ✓ ✓ ✓ ✓ ✓ ✓

LastHistory (Baur et al., 2010) ✓ ✓ ✓

ChronoLenses (Zhao et al., 2011) ✓ ✓ ✓ ✓ ✓

Temporal MDS (J

ackle et al., 2016) ✓ ✓ ✓ ✓

BitExtract (Yue et al., 2019) ✓ ✓ ✓

PromotionLens (Zhang et al., 2022) ✓ ✓ ✓ ✓ ✓ ✓

Roslingﬁer (Shin et al., 2023) ✓ ✓ ✓ ✓

Life Mountain (Zhang et al., 2023) ✓ ✓ ✓ ✓ ✓ ✓

LiveRetro (Wu et al., 2023) ✓ ✓ ✓ ✓ ✓

8 1 13 5 11 4 16 4 5 8 0 5 2 3 9

Functionalities

Medical

Non-medical

Total :

• Multimodal: The interface displays numerical or

categorical data alongside data of different types,

such as text, images, etc.

• Hierarchy: The interface displays data organized

hierarchically with two or more levels. This struc-

ture must be visible.

Two additional criteria describe the temporal as-

pect of the records:

• Temporal Distortion: The interface displays the

temporal axis on a non-linear scale to emphasize

certain areas.

• Time Series: The interface shows the evolution

of multiple continuous variables along a temporal

axis.

Four criteria describe the interface’s ability to

highlight key elements important for decision-making

or understanding trends and data quality. Visual em-

phasis can be applied as follows:

• Importance: The interface draws attention to im-

portant values, with importance determined by

variable criteria.

• Standard Ranges: The interface visually distin-

guishes measurements whose values fall outside

standard ranges.

• Missing Values: The interface highlights data

types or points that might be missing in the pre-

sented data. It also visually represents the absence

of numerical values in series.

Two criteria describe the interface’s ability to

leverage an underlying predictive model to provide

additional information:

• Proximity Map: The interface includes a panel

showing relationships or proximity of the targeted

Multivariate Time Series Visualization for a Single Individual: A Scoping Review Using PRISMA-ScR

979

Table 3: Synthesis of the content of the 21 identiﬁed articles

on evaluation methods, based on the criteria developed in

Section 3.5.

Accuracy

Resolution Time

Graded Questions

Open-Ended Questions

Open Feedback

Think-Aloud Protocol

Case Study

LifeLines ✓ ✓ ✓

KNAVE-II ✓ ✓ ✓

MIDGARD ✓

Caregiver ✓

CareVis ✓

Lung Transp. ✓ ✓

TimeLine

MIVA ✓ ✓ ✓

VisuExplore ✓ ✓

CareCruiser ✓

UHS Lifelines

Tumor Board ✓

ClinicalPath ✓ ✓ ✓ ✓ ✓

LastHistory ✓ ✓ ✓

ChronoLenses

Temporal MDS ✓

BitExtract ✓

PromotionLens ✓ ✓ ✓

Roslingﬁer ✓ ✓ ✓

Life Mountain ✓ ✓ ✓ ✓

LiveRetro ✓

6 5 8 4 4 0 1

Evaluation

Medical

Non-medical

Total :

individual to other individuals. These relation-

ships are derived from a predictive model or di-

rectly from the data.

• Prediction: The interface visually integrates pre-

dictions from an underlying model into the data

presentation.

Two additional criteria complete the set of criteria

already outlined:

• Open Environment: The interface displays data

from an open data model or allows the user to up-

load their data in an interoperable format.

• General Individual Information: The interface

includes a panel displaying general information

about the targeted individual.

Evaluation. The following seven criteria describe

the evaluation process for the interface:

• Accuracy: Participants are required to solve

closed tasks using the interface, and the success

rate in resolving these tasks is measured.

• Resolution Time: Similarly, the time required to

complete each task is measured.

• Graded Questions: Participants respond to tar-

geted questions about speciﬁc aspects of their in-

terface usage on a predeﬁned scale. This can be

binary options (Yes/No), a numerical scale like

the Likert scale (Likert, 1932) (1–5), a criticality

scale, etc.

• Open-Ended Questions: Similarly, participants

answer questions without constraints on their re-

sponses.

• Open Feedback: After using the interface, par-

ticipants are invited to share their experience with-

out restrictions.

• Think-Aloud Protocol: During interaction with

the interface, participants are encouraged to artic-

ulate their thought process, exploration, and inter-

action strategies, as well as any factors aiding in

problem resolution.

• Case Study: The usage scenario of the interface

by a participant is described in detail. The par-

ticipant’s interaction with the interface is directed

toward solving a pre-identiﬁed task.

3.6 Results

The results are presented in Tables 2 and 3. We distin-

guish studies originating in the medical domain from

those in other ﬁelds, sorted by publication date.

The following observations are derived from the

data presented in Table 2, which addresses function-

alities and interactions. According to our ﬁndings, no

approach facilitates easy identiﬁcation of missing val-

ues. Most approaches display multivariate temporal

data as parallel time series (16 out of 21). Few ap-

proaches are compatible with open environments (3

out of 21). Temporal distortion is rarely utilized (4

out of 21). Not all approaches display raw data with

the ﬁnest possible granularity (13 out of 21 do). Few

approaches support multimodal data visualization (5

out of 21). Additionally, none of the approaches in

the medical domain provide latent space exploration

through predictions or the projection of similar indi-

viduals.

The following observations are based on the data

presented in Table 3, which focuses on methods for

evaluating the needs that visual solutions should ad-

dress. Some approaches incorporate user-centered de-

sign (9 out of 21), but all address identiﬁed user needs.

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980

Qualitative and quantitative evaluations are combined

only in the most recent approaches. None of the ap-

proaches identiﬁed in this study report collecting par-

ticipants’ think-aloud reﬂections to supplement qual-

itative results. Methods for collecting user-centered

needs are challenging to classify, as each identiﬁed

study presents a distinct protocol.

4 DISCUSSION

The methodology presented in this section, although

based on the principles of the PRISMA-ScR method,

has certain limitations. First, the query deﬁned in Sec-

tion 3.1 resulted in documents that were unrelated to

the research question addressed in this chapter. Re-

ﬁning the combination of keywords could reduce the

number of irrelevant documents. Furthermore, the

success of using a semi-automatic approach, as de-

scribed in Section 3.2, to ﬁlter similar approaches

depends on the quality of the partitioning algorithm.

In this work, we used only the BERTopic algorithm

(Grootendorst, 2022), but it could be compared to

other approaches, particularly LDA (Blei et al., 2003).

Enhancing the representation of articles by incorpo-

rating full-text analysis and illustrations into multi-

modal models (Lopez, 2009) is another avenue to im-

prove partitioning quality. Finally, the choice of data

sources, PubMed and Google Scholar, was driven by

the respective coverage of these sources in the medi-

cal and general domains. Alternative sources, such as

Web of Science or DBLP, could also have been con-

sidered.

5 CONCLUSION

We studied 21 visualizations of multivariate time se-

ries for a single individual using the PRISMA-ScR

protocol. This exploratory literature analysis reveals

that this research area has gained recent interest but

exhibits limitations in the proposed solutions. No-

tably, few approaches utilize temporal axis distortion

to allow users to examine records at the minimal gran-

ularity level. Moreover, no approach in the medical

domain visually integrates model predictions, a func-

tionality present in non-medical approaches such as

(Zhang et al., 2022) (Wu et al., 2023), which could be

valuable for proactive patient care. Finally, the num-

ber of interoperable approaches remains limited. This

study highlighted preferred functionalities for the vi-

sual representation of multivariate temporal data, in-

cluding parallel time series and hierarchical views.

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