Process Mining of Disease Trajectories: A Feasibility Study

Guntur P. Kusuma

1,2 a

, Samantha Sykes

, Ciarán McInerney

and Owen Johnson

School of Computing, University of Leeds, Leeds, U.K.

School of Applied Science, Telkom University, Bandung, Indonesia

School of Medicine, University of Leeds, Leeds, U.K.

Keywords: Disease Trajectories, Process Mining, Electronic Health Records.

Abstract: Modelling patient disease trajectories from evidence in electronic health records could help clinicians and

medical researchers develop a better understanding of the progression of diseases within target populations.

Process mining provides a set of well-established tools and techniques that have been used to mine electronic

health record data to understand healthcare care pathways. In this paper we explore the feasibility for using a

process mining methodology and toolset to automate the identification of disease trajectory models. We

created synthetic electronic health record data based on a published disease trajectory model and developed a

series of event log transformations to reproduce the disease trajectory model using standard process mining

tools. Our approach will make it easier to produce disease trajectory models from routine health data.

1 INTRODUCTION

Diseases occur at various points during a person’s

life-course and impact on health, lifestyle, quality of

life, morbidity and mortality. Disease can be seen as

a pathological process that requires judgement from a

clinician to objectify its occurrence (Boyd, 2000).

The record of disease occurrences over time become

the “footprints” that can tell the story of how diseases

have progressed for each individual. This type of

historic patient information is vital evidence that can

help clinicians to diagnosis appropriately and to

decide on appropriate interventions (Muhrer, 2014;

World Health Organization, 2016). More generally,

medical research recognises common patterns of

diseases where one disease is often found to precede

others. These commonly-found patterns for disease

progression are sometimes referred to as disease

trajectories (A. B. Jensen et al., 2014).

The temporal record of diseases can be observed

within electronic healthcare records (EHR) and can

be used to understand the occurrence and behaviour

of diseases. The trajectories of diseases can be

identified by observing the sequence of disease

https://orcid.org/0000-0002-0208-125X

https://orcid.org/0000-0001-7098-3928

https://orcid.org/0000-0001-7620-7110

https://orcid.org/0000-0003-3998-541X

diagnoses and the time intervals between them.

Investigating disease trajectories has the potential to

provide personalised medical treatment (P. B. Jensen

et al., 2012) and to understand the potential cause-

and-effect association between diseases (Hanauer &

Ramakrishnan, 2013; Rothman & Greenland, 2005).

In A. B. Jensen et al.'s (2014) widely cited work,

the authors produced a number of disease trajectories

based on EHR data from the population of Denmark.

Disease trajectories were defined as the time-ordered

sequence of diagnoses observed in the patients. An

example of a disease trajectory model is presented in

Figure 1. The model consist of nodes representing the

diseases and directed arcs representing the common

trajectories between diseases with the thickness of the

arcs representing the relative number of patients.

In many countries, healthcare providers are now

supported by EHR systems containing episodic and

longitudinal data of a patient’s medical history,

diagnosis and treatment (Hemingway et al., 2018).

The World Health Organisation had introduced

standards for medical records (World Health

Organization, 2002) and the European Medicines

Agency suggests that clinicians use of medical record

information is good clinical practice (European

Kusuma, G., Sykes, S., McInerney, C. and Johnson, O.

Process Mining of Disease Trajectories: A Feasibility Study.

DOI: 10.5220/0009166607050712

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 705-712

ISBN: 978-989-758-398-8; ISSN: 2184-4305

705

Figure 1: Example of a disease trajectory model, adapted from Figure 4.b of A.B. Jensen et al. (2014).

Medicines Agency, 2002). In some countries, the

initial motivation of developing EHR was for billing

purpose but in many countries EHR use is now

comprehensive and includes records of disease

diagnoses, with use being expanded for clinical and

research purposes (Casey et al., 2016). The World

Health Organisation provides the International

Classification of Diseases (ICD) with the purpose of

standardising the coding of diseases within EHR to

support evidence-based decision making, sharing and

comparing of health information, monitoring the

incidence of disease, and helping healthcare

organisations in managing disease related billing

(WHO, 2019). Disease codes in EHR are commonly

used, but there are known data quality issues. For

example, in the ICD-10 standard, the code I20 is used

for angina pectoris. This structured encoding

framework has facilitated the construction of disease

trajectories using EHRs (A. B. Jensen et al., 2014)

and for process mining of care pathways (Rojas et al.,

2016).

Process mining is a data mining approach that

examines temporal and sequential data to analyse

processes including the discovery of process models,

conformance checking and process enhancement

(van der Aalst, 2011). Process mining provides a

holistic view, end-to-end analysis, and generates

easy-to-read models and simulations (van der Aalst,

2011). The input of process mining is an event log

detailing who did what and when. More formally, the

event log is a collection of time stamped events

containing at least a case, an activity, and a

timestamp. The output of process mining is often

graphical, producing visual models of processes and

pathways. Conformance of event logs to expected

models can be measured and a process modelling

project may involve multiple iterations of data

extraction, transformation, modelling, measuring and

refinement to construct valuable process models and

process insights.

In our review of the literature, we found that

process mining techniques have not yet been utilised

to extract patients’ disease trajectory models from

health data despite the many similarities between

process models and disease-trajectory models. This is

despite the data required for such an approach often

being available within EHRs.

The aim of this study, therefore, was to assess the

feasibility of using process mining methods and tools

with EHR data to construct disease trajectory models.

In our study, we simulated a scenario from A. B.

Jensen et al.’s trajectories that were centred on

chronic ischaemic heart disease (the example in

Figure 1). Our hypothesis was that by treating the

entry of a disease code in the EHR as the equivalent

of an activity in process mining we could exploit the

rich toolset of process mining for the mining of

disease trajectories.

2 BACKGROUND

2.1 Disease Trajectory

2.1.1 Definitions

Multiple definitions of disease trajectory have been

proposed. Murray et al (2005) defined a disease

trajectory as the progressiveness of physical health

deterioration over time. They described three types of

trajectories: the short period, where the decline of

physical health happens within a few months or a few

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years; long-term limitations, where the decline

happens between 2 to 5 years; and prolonged

dwindling, where the decline happens in 6 to 8 years.

A disease trajectory is also defined as the progression

of a specific disease by observing a clinical measure

of the severity of a disease. An example of work

which follow this definition is a study observing the

progression of chronic kidney disease by measuring

the estimated glomerular filtration rate (eGFR)

(Sumida & Kovesdy, 2017). Finally, A. B. Jensen et

al. (2014) proposed a definition of disease trajectory

as the sequence of diseases that are ordered by the

time of the occurrence. Our work follows this

definition from A. B. Jensen et al. We note that this

definition of disease trajectory is similar to definitions

of patient trajectories found in other literature

(Pavalko, 1997; Pescosolido, 2013). Specifically, we

take the first occurrence of a new disease code as it is

recorded in the EHR.

2.1.2 Disease Trajectory Modelling

Disease trajectory models are typically represented as

acyclic graphs, where each node represents a disease

and each directed arc represents a progression from

one disease to another. Reducing the graph to remove

cycles presents a stronger representation of a general

trajectory (progression) of diseases at the cost of

simplifying the reality of complex real-world cases.

Constructing disease trajectories from EHR data

is a challenging process. Various techniques have

been used to investigate and model disease

trajectories, including a data-driven approach

(Glicksberg et al., 2016; Hanauer & Ramakrishnan,

2013; Hidalgo et al., 2009; A. B. Jensen et al., 2014),

data-mining (Giannoula et al., 2018; Ji et al., 2016;

Wang et al., 2014), network-based (Steinhaeuser &

Chawla, 2009), free-text analysis (K. Jensen et al.,

2017), and more recently by implementing a deep-

learning techniques (Beaulieu-Jones et al., 2018;

Futoma et al., 2015; Pham et al., 2017).

The disease trajectories modelled by A. B. Jensen

et al. (2014) were constructed by joining overlapping

pairs of diagnoses (bi-grams) to form longer

trajectory chains. For example, the A. B. Jensen et al.

method might have identified a bi-gram pair of

diseases I21 (acute myocardial infarction)  I25

(chronic ischaemic heart disease), where I21 is a

disease code that is recorded against a patient some

time before I25. To construct a longer trajectory, A.

B. Jensen et al. combined multiple bi-grams such as

I21I25 and I25N30 to form a longer sequence of

three diagnoses, I21I25N30. Although appealing

in its simplicity, the concatenated trajectory might not

be evidenced in any patient’s record nor does it

consider the conditional likelihood of the latter bi-

gram given the former. No standard tools for disease

trajectory modelling are evident in the literature. In

contrast, process mining methods and tool are well

established and have the potential to efficiently define

such longer trajectories and also provide diagnostics

to evaluate representativeness.

2.2 Process Mining in Healthcare

Process mining in healthcare is now well established

with strong support from commercial and open-

source tools, for example ProM Framework (Process

Mining Group, 2010), Celonis (Celonis GmbH,

2019), or Disco (Fluxicon BV, 2019). Process mining

also boasts a growing body of literature (Rojas et al.,

2016) and an international research community

Process Oriented Data Science for Healthcare

(“PODS4H,” 2019).

The implementation of process mining in

healthcare has been proven applicable to analyse care

pathways (Mans et al., 2015; Rojas et al., 2016).

Process mining is commonly used for mining the

sequence of activities but the time between activities

can also be analysed. Process mining has been used

in cancer (Kurniati et al., 2016), cardiovascular

disease (Kusuma et al., 2017), dentistry (Fox et al.,

2018; R S Mans et al., 2012), frailty (Farid et al.,

2019), sepsis (Mannhardt & Blinde, 2017), and in

primary care (Williams et al., 2018). Process mining

is suitable for answering frequently posed questions

by extracting information from an EHR (Mans et al.,

2013). Unlike disease trajectory models which use the

first occurrence of a disease, process mining is able to

model multiple simultaneous and recurrent activity.

3 MATERIALS AND METHOD

The goal of this exploratory data-driven study is to

explore the feasibility of producing disease

trajectories using a process mining approach. This

study uses synthetic data and has been made available

on GitHub (Kusuma et al., 2019) and therefore useful

for reproducibility. We examined A. B. Jensen et al.’s

(2014) trajectories to simulate a set of EHR data that

reflected a subset of the disease trajectories shown in

Figure 1. Table 1 summarises the variables simulated

that contained 50 patients with 146 diagnosis codes

from 10 distinct diagnosis, using the first three

characters of ICD-10 format. We treat each patient as

a case and use the diagnosis codes in place of the

standard process mining event-log activity names.

Process Mining of Disease Trajectories: A Feasibility Study

707

The extracted data was formatted following the

structure of a process mining event log, see Figure

2(a).

The synthetic event log was created by

constructing event data for each case (patient) with

the minimum of two events to form a diagnoses pair

(D1D2). We created the time of D1 earlier than the

time of D2, to ensure D1 occurred as an antecedent of

D2. We created some cases with 3 or more events to

represent a sequence of diagnoses D1D2D3…

which follow trajectories recognised by A. B. Jensen

et al. as a collection of diagnoses pairs (D1D2,

D2D3, D3…). To make the event log even look

similar with the real-life EHR, then we added some

repeating events as noise in the event log.

Table 1: The sources of required data from the synthetic

dataset.

Variables Data Field name

Case

identifier

Patient identifier subject_id

Event Diagnosis code diagnosis

Time

stamps

Time when the

diagnosis was

recorded

admittime

For conducting the process mining experiments,

we followed the Process Mining Project

Methodology, PM

(van Eck et al., 2015). Our use of

the PM

method is summarised below.

In Stage-1: Planning, research questions were

identified from a literature review and confirmed by

the project team during study planning. The team

included a clinician, epidemiologist and computer

scientists.

In Stage-2: Extraction, we defined the scope of

the extraction by determining the granularity of the

data, the time period, and selected the related

attributes. We used the synthetic dataset as the input

for creating an event log in the next stage. Only the

first of any recurring diagnosis codes for each patient

were used to create an acyclic, disease-trajectory

model. We treated each patient as a case and used the

diagnosis codes in place of the standard process

mining event-log activity names.

In Stage-3: Data Processing, we followed the

activities to create the event log as defined in PM

creating views, filtering logs, and event log

transformation into pair log, the collection of event

data in the form of pairs. The filtering was done by

(a)

(b)

(c)

Figure 2: The filtering and transformation steps of event

log: (a) the extracted event log from simulated data; (b) the

recurrent diagnoses for each patient were filtered; and (c)

the pair log with duplicate diagnoses removed.

removing the recurring diagnoses for each patient and

keep the first occurrence.

In Stage-4: Mining and Analysis, the event log

was analysed by applying process discovery and

conformance-checking methods using process

mining tools Disco and plugins in the ProM

Framework. The discovered model was evaluated

using the measures of fitness, precision and

generalisability (van der Aalst, 2011). Fitness is a

measure of how many traces in the event log can be

replayed through the discovered model. Precision is a

measure of how much the discovered model over

estimates the traces in the event log; Low precision,

or under-fitting, indicates that the model can

represent traces that never occur in the event log,

while high precision, or over-fitting, indicates that the

model can represent the traces in the event log, only.

Generalisation is a measure of how often activities in

the model occur in the event log.

Disease trajectories were ‘discovered’ using

Disco, conformance-checking and the measurement

of precision and generalisation were done in ProM

Framework using the plugin Replay a Log on Petri

Subject_id Diagnosis Time

3 I21 01/01/2100

3 I25 02/03/2100

4 I21 21/02/2100

4 I25 14/06/2100

6 I21 01/01/2100

6 I25 02/01/2100

6 J18 03/01/2100

Subj ect _i d Ant ecedent Subsequent T i me1 T i me2

3 I21 I25 01/01/2100 02/03/2100

3 I25 I25 02/03/2100 12/05/2100

4 I21 I25 21/02/2100 14/06/2100

6 I21 I25 01/01/2100 02/01/2100

6 I25 J18 02/01/2100 03/01/2100

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Figure 3: Disease trajectory model using process mining (the model generated from Disco).

Net for Conformance Analysis (Ardiansyah, 2012)

and Measure Precision/Generalization respectively.

Because our discovered process model and the

trajectories of A. B. Jensen et al. (2014) can both be

considered directed graphs, we were able to assess

agreement between them by checking if both were

isomorphic. Two graphs are said to be isomorphic if

they have the same (“iso-“) structure (“-morph”),

where structure is defined (Goldberg, 2003). In our

case, by the count of diseases, the count of disease-

pair connections and pattern of connected nodes and

arcs is identical. More formally, two graphs are

isomorphic if there exists a mapping that is a bijective

function that preserves the branch structure of the

graphs. We applied Cordella et al.'s (2001) method to

check for isomorphism using the NetworkX Python

library.

All processing other than discovery and

conference checking were conducted in Python

through Jupyter Notebook (Kluyver et al., 2016).

4 RESULTS

By following the PM

, the result of each stage is

described. In Stage-1,we aimed to mine the disease

trajectory agnostically without any specific selection

of diagnosis and time window. We defined the main

research question as Can disease trajectories be

identified from an EHR, using a process mining

approach?

In Stage-2, the synthetic data was formatted to

follow the structure of a process mining event log

(Figure 2a). In Stage-3, the recurring diagnoses for

each patient were filtered out and we kept the first

occurrence. The filtering step reduce the total number

of events from 126 to 117 for the next stage.

In Stage-4, using the process-mining tool Disco a

disease trajectory model was discovered (Figure 3).

The trajectory model shows the same characteristics

as the sub-trajectory of A. B. Jensen et al. (2014) in

Figure 1. Both models have 10 nodes and 13 arcs

including the thickness representation despite the

difference on the scale and the addition of the case

frequency (which is represented in Figure 3 by darker

shades of nodes colour for more frequently

occurring). The application of an isomorphic checker

(following Cordella et al., 2001) determined that the

two graphs could be considered isomorphic.

One benefit of our process-mining approach is

that the temporal information about the time elapsed

between disease occurrences is preserved and can be

examined using standard tools. For example, the

median duration between events can be displayed by

process mining software such as Disco and ProM.

The preservation of temporal data is a significant

improvement over the simple models of disease

sequence produced by the trajectory method of A, B.

Jensen et al. (2014). Further, we can use process

mining tools to measure the quality of the discovered

model. In our experiment: the fitness value is 0.961,

precision 0.79 and generalisation 0.963.

Process Mining of Disease Trajectories: A Feasibility Study

709

5 DISCUSSION

In this work we sought to assess the feasibility of

process mining to identify disease trajectories in a

simulated dataset representative of a published

disease trajectory. We applied the Process Mining

Project Methodology PM

to discover and

conformance check a process model that was

qualitatively similar to the sub-graph of trajectories

from A. B. Jensen et al. (2014). In combination with

good estimates for fitness, precision and

generalisability, we conclude that process mining is a

feasible approach for identifying disease trajectories

using data similar to that found in EHRs.

The originality of this study is around the method

where disease trajectories can be discovered using

process mining techniques. We further suggest that

our process mining method is an improvement on the

disease trajectory method of A. B. Jensen et al.

(2014). The high fitness, precision and generalisation

scores permit us to make the follow statement:

process models discovered using our methods on data

similar to ours would permit the behaviour seen in the

event log, would be precise enough to not allow

behaviour unrelated to what was seen in the event log,

and would be general enough to reproduce future

behaviour of the trajectories. This is in contrast to the

concatenation of bi-grams approach of A. B. Jensen

et al. (2014), which implies the existence of long

trajectories of diseases based on combining direct

disease pairs, end to end, without being in a position

to validate from the data.

By default, process mining methods also provide

additional information not found in the purely-

sequential output provided by A. B. Jensen et al.

(2014). The output of some process mining

algorithms present the durations and counts of cases

that follow the trajectories. For example, the median

duration among the patients is one day, while the

longest duration is 150 days. Following our approach

disease trajectory models can be automatically

visualised in keeping with the graphical and

exploratory ethos of the process mining. A major

benefit of process mining is that its application is

supported by commercial and open-source software

(Fluxicon BV, 2019; Process Mining Group, 2010), a

healthcare-specific literature base (Rojas, Munoz-

Gama, Sepúlveda, & Capurro, 2016) and an

international research community.

A limitation identified by our particular

implementation of PM

was the decision to include

only the first occurrence of the primary diagnoses as

the main event. This step promotes a representational

bias that cannot be avoided in the study of model

discovery. It is possible that different trajectories

exist for recurrent diagnoses but we constrained our

investigations for the purpose of demonstrating

feasibility.

For future work, process mining should be

applied to real-life EHRs to identify disease

trajectories. Using high-volume datasets is necessary

to evaluate could evaluate the scalability of the

method. The role of a clinical domain expert could

help to limit the number of variables of interest to

build a more tightly-focussed model examining

specific disease trajectory patterns and this approach

would also make the discovery task more efficient.

Despite the limitations, our approach has

demonstrated that we can use process mining tools to

mine disease trajectories and has opened up an

interesting field for further work.

6 CONCLUSION

In this paper we have demonstrated the feasibility of

mining of disease trajectories using process mining.

The mining was conducted using a synthetic dataset

which is similar to the data available from many EHR

systems. Our study included the use of the PM

framework to mine a representative disease

trajectories model from EHR format data and

addressed several quality dimension standards.

This feasibility study opens opportunities for

future works in implementation the technique using

population sized EHR data. The application of

different discovery algorithms to mine the disease

trajectory model may improve the conformance

measurement and the disease trajectory model’s

quality dimension. Our approach will be of interest to

the wide range of multi-disciplinary researchers

interested in exploring healthcare record data for

identifying disease trajectories to improve medicine

and health.

ACKNOWLEDGEMENTS

This research is part of the first author’s PhD study

funded by the Indonesia Endowment Fund for

Education (LPDP). The research was supported by

the National Institute for Health Research (NIHR)

Yorkshire and Humber Patient Safety Translational

Research Centre (NIHR YH PSTRC). The views

expressed in this article are those of the author(s) and

not necessarily those of the NHS, the NIHR or the

Department of Health and Social Care.

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