=Paper=
{{Paper
|id=Vol-2738/paper13
|storemode=property
|title=Process Mining for Case Acquisition in Oncology: A Systematic Literature Review
|pdfUrl=https://ceur-ws.org/Vol-2738/LWDA2020_paper_13.pdf
|volume=Vol-2738
|authors=Joscha Grüger,Ralph Bergmann,Yavuz Kazik,Martin Kuhn
|dblpUrl=https://dblp.org/rec/conf/lwa/GrugerBKK20
}}
==Process Mining for Case Acquisition in Oncology: A Systematic Literature Review==
https://ceur-ws.org/Vol-2738/LWDA2020_paper_13.pdf
Process Mining for Case Acquisition in
Oncology: A Systematic Literature Review
Joscha Grüger1 , Ralph Bergmann1,2 , Yavuz Kazik1 , and Martin Kuhn1
1
Business Information Systems II, University of Trier, 54286 Trier, Germany
http://www.wi2.uni-trier.de
{grueger,bergmann,s4yakzi,s4makuhn}@uni-trier.de
2
German Research Center for Artificial Intelligence (DFKI), Branch University of
Trier, Behringstraße 21, 54296 Trier, Germany
ralph.bergmann@dfki.de
Abstract Process Mining is a technology family for the analysis of busi-
ness processes based on event logs. The methods are successfully applied
in various areas, including medicine. This paper examines, using a sys-
tematic literature review, whether Process Mining is suitable for case ac-
quisition from Hospital Information Systems in order to construct a case
base for experience-based systems targeted at decision support in oncol-
ogy. The review investigates whether there are special characteristics of
process mining in the oncological field compared to other medical fields
and if the development of similarity measures is discussed in the contri-
butions. For this purpose, 2848 papers were reviewed manually, based
on title, abstract and full text, resulting in 55 relevant papers. These
were analyzed in detail regarding the research questions. The paper can
serve as a basis for further research, identify research opportunities in
this domain and provide a useful overview of the current work.
Keywords: Process Mining · Oncology · Case Based Reasoning ·
literature review.
1 Introduction
Medical guidelines are “systematically developed statements designed to assist
healthcare professionals and patients in making decisions about appropriate
health care in specific clinical circumstances” [28]. These are classified according
to the AWMF3 system into four development levels from S1 to S3, with S3 being
the highest quality level of the development methodology. The classification of
a guideline as S3 means that it has undergone all elements of systematic devel-
opment and the recommendations given therein have a high level of evidence
[11]. In the best case, clinicians can make treatment decisions based on these
high-quality S3 guidelines and are thus able to offer evidence-based treatment.
Copyright © 2020 by the paper’s authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
3
Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften
This is usually possible with well understood disease patterns, such as stroke. In
other areas, such as oncology or paediatrics, there is in many cases insufficient
evidence for a fully evidence-based treatment of patients. This is partly because
studies in these areas are difficult (e.g. with children), diseases are rare or disease
patterns are not yet sufficiently researched due to their complexity (e.g. uveal
melanoma). In addition, the process of developing guidelines is quite slow, i.e.,
it usually takes at least two years. In view of scientific progress, especially in
medicine, the question of the timeliness of guidelines arises.
In the absence of appropriate guidelines and high evidence studies, treatment
decisions are made based on personal experience of medical experts. In contrast
to evidence-based medicine, we then speak of “eminence-based medicine”, as
a treatment decision is based on the comprehensive professional experience of
recognized medical experts in the field [19]. In the field of oncology, for exam-
ple, multidisciplinary experts regularly meet in tumor boards to discuss critical
cases and then make decisions, often based on treatment experience with similar
patients.
Today, the complexity of such decisions is constantly increasing. The decision-
making process is becoming more and more complicated due to the constant de-
velopment of new therapeutic approaches, an ever-wider range of drugs and their
frequently unexplored interaction with given constraints such as comorbidities.
In addition, the departure of experienced physicians can have a negative impact
on the quality of treatment, as their experience also leaves the clinic.
During the daily treatment of patients, however, physicians systematically
record experiential knowledge in hospital information systems (HIS). A HIS is
the central information system of a hospital and receives, transmits, processes,
stores, and presents information. Date and time of treatments, patient demo-
graphics, and examination results are stored in a HIS along with other infor-
mation [16]. We envision that this information can be used as experience by a
Case-Based Reasoning (CBR) system to support eminence-based decision mak-
ing by the wealth of collected experience available in HIS. For this purpose,
treatment processes from a HIS must be captured as a time series of semanti-
cally described activities and transferred into semantic case descriptions in order
to construct a case base.
In this paper, we therefore investigate based on a literature survey whether
process mining, which is an established technology for extracting process knowl-
edge from events logs, can be applied or has been applied already in order to
acquire semantic case descriptions from HIS. So far there are only a few litera-
ture reviews in the field of process mining in medicine [35,41,13] and only one
systematic literature review in the field of process mining in oncology [22]. None
of the papers examines the use of process mining for case acquisition for CBR.
Processes in the health care sector differ greatly from processes from other do-
mains due to their high complexity, heterogeneity and significant variation over
time [17]. This makes it difficult to adapt approaches from other domains. In the
present work, a literature study in the medical domain of oncology is performed
and used to investigate whether it is possible to generate systematic case descrip-
tions from HIS data using process mining. The paper focuses particularly on the
data source from which data is acquired, the process mining methods used, and
the data formats and descriptions used, with the aim to provide systematic basis
for the topic. By analysing the literature on process mining in oncology, this pa-
per also provides a foundation for future work and helps identifying challenges
and research gaps based on the previous research.
The remainder of this paper is organized as follows: in Section 2 we give an
overview of the basics of Process Mining and Case Based Reasoning and discuss
related work. In Section 3 we present the methodology of the literature review.
Then we evaluate the results of the study in Section 4 and summarize them in
Section 5 and discuss possible directions for future work.
2 Foundations and Related Work
Case-Based Reasoning [21,3] is an established problem-solving methodology for
solving problems based on past experience. Experience is formalized in the form
of cases collected in a cases base. A problem (e.g. to determine the best treatment
option of a patient) is solved by searching for similar cases in the case base and
then reusing the solution contained in the most similar case(s). Unlike black box
algorithms such as deep learning, the solutions of CBR systems can be easily
justified on the basis of similar cases, which can help to strengthen the confidence
of healthcare professionals in the AI system, especially in the medical field [26].
The CBR cycle consists of four sequential phases. In the RETRIEVE phase, the
most similar cases for a given case are searched for in the case base. Then, in the
REUSE phase, the information and knowledge about the most similar cases is
used to solve the problem given. Afterwards the solution found in the REVISE
phase has to be checked. In the RETAIN phase, those parts of the solution are
included in the case base that could be useful for solving later cases [1]. CBR
publications in the medical field usually focus exclusively on retrieve and avoid
automatic adaptation [8].
Process mining technologies enable the extraction of process knowledge from
event logs of information systems. Based on these techniques, process models can
be created (discover) and improved (enhancement) and traces can be validated
for their conformity with existing models (conformance checking) [37]. Process
Mining is already partially used in medicine. The research focuses in particular
on the field of oncology and operations. In other areas, such as care giving, car-
diology, diabetes, dentistry, medication, intensive care, and radiotherapy, there
are considerably fewer publications [13,35]. The focus of most process mining
publications in the medical domain is usually on the control flow perspective,
based on the discovery of the execution sequence of process activities [35].
3 Methodology
To answer the following research questions, a systematic literature review in the
field of process mining in oncology was conducted:
RQ1: What is the state of research in the field of process mining in the
domain of oncology?
RQ2: Are there process mining approaches based on oncological data from
a HIS?
RQ3: Are there approaches to use process mining for case acquisition for
experience-based systems?
RQ4: Are there studies that deal with the similarity of oncological processes?
The search is divided into three main parts: the initial search, the backward
snowballing and the forward snowballing [42]. The results of each step are filtered
through a three-step application of including- and excluding criteria’s (see Fig.
1). Overall, one including, and three excluding criteria were established and
Metadata-
based checking
Initial search results
Abstract- In-depth analysis
based on the query
based checking
Full-text checking
Fig. 1. Applying the including and excluding criteria’s.
applied. These ensure that only relevant and accessible documents are included
in the analysis:
EC1: Duplicates of the same study are excluded.
EC2: Articles that are not written in English or German are excluded.
EC3: Articles that are not published in a journal or at a conference are
excluded.
IC1: Articles written in the field of process mining in oncology or whose
authors use oncological data are included.
The first step of the initial search is the database selection. For this purpose,
published literature searches in the field of process mining in medicine [35,22,25]
were analyzed and the databases used therein were extracted as a basis for
database selection. The following sources were identified: ACM DL, CiteSeerX,
dblp, Google Scholar, IEEE Explore, PubMed, Science Direct, Scopus, Semantic
Scholar, Springer and Web of Science. Based on the databases and a database
selection matrix according to Bethel [4,27] the databases Google Scholar and
Science Direct were selected.
The search query was created based on the PICOC method (Population, In-
tervention, Comparison, Outcome, Context) according to Kitchenham [20]. This
approach is intended to ensure that the query is precise and only considers the
essential components. To ensure that the approach fits the given research ques-
tion, the Data field has been added and the Comparison and Outcome fields have
been removed. The final query is: (“oncology”) AND (“process mining”) AND
(“hospital”) AND (“event log”). The same query was used for both databases.
The initial search took place on 20.12.2019. Google Scholar delivered 174
results and Science Direct 24. After forward and backward snowballing, 60 papers
were classified as relevant. After analyzing the papers, five papers were excluded
due to a lack of information concerning our research questions. Therefore, 55
papers were considered in the analysis process (see Fig. 2).
Identification
Records identified through Google Records Identified backward Records identified through forward
Scholar and Science Direct snowballing snowballing
(n = 198) (n = 1048) (n = 1602)
Screening
Records after etadata checking Records after etadata checking Records after etadata checking
(n = 88) (n = 422) (n = 627)
Eligibility
Included through abstract Included through abstract Included through abstract
(n = 64) (n = 120) (n = 159)
Included
Included through full text browsing Included through full text browsing Included through full text browsing
(n = 30) (n = 11) (n = 19)
Fig. 2. PRISMA Overview of the results of the different steps of the literature search
of the literature review.
To answer the research questions, a data extraction form was developed based
on the core features of process mining in oncology and on metadata of the papers.
4 Results
The first papers on process mining in oncology were published in 2008. However,
the majority of the papers, 48 out of 55, were published between 2013 and 2019.
Most of the papers come from Europe (40 out of 55 papers). With 24 papers
the Netherlands is the most important contributor in Europe. This is probably
due to the large research group in the field of Process Mining at the University
of Eindhoven (TU/e), which was headed by Prof. van der Aalst. From North
America and Asia six papers each were found, from South America only two
were found.
The papers analyzed address a total of 21 different types of cancer. The ma-
jority of the papers referred to gynaecological cancer (19 papers). Other cancers
addressed are lung cancer and breast cancer (10 papers each), followed by col-
orectal cancer (9 papers found), skin cancer (5 papers) and stomach cancer (4
papers). 13 types of cancer were mentioned only once, and in eight contributions
the type of tumour was not mentioned.
4.1 Data and Process Mining Perspectives
In order to answer research question RQ2, it was examined on which data the
papers work and which data sources were used. After examining the process
mining data spectrum, the data used mainly comes from administrative systems
(58 %) and from the clinical part of hospital information systems (30 %). Only
one paper uses data from medical devices. Most papers, 49 of 55, apply process
mining technologies to medical data (diagnosis, prognosis, treatment and pre-
vention of disease activities) and 3 papers use organizational data (management
and financial), 3 papers use both medical and organizational data.
Data coming from HIS is described to be very complex, containing hetero-
geneous structured and unstructured data [10] and sometimes scattered across
multiple HIS [5]. Poor data quality and the distribution of data across different
HIS can significantly hinder the process extraction [5]. Mans et. al. [36] evaluate
data quality issues in the data of a HIS. Among other things, they point out
that manual documentation of events leads to the fact that individual events
are not documented (”missing events”). In addition, the distribution of the data
to different systems leads to imprecise timestamps and executing actors are im-
precisely documented (imprecise resource).
Many authors emphasize the complexity of clinical processes (30 papers).
They attribute this, among other things, to the high degree of flexibility, the
dynamics in treatment processes and in everyday clinical life and a high number
of interactions of interdisciplinary actors in a treatment path.
Regarding the process mining perspectives, it can be said that most papers
focus on the control flow perspective (48 %). With 23 % follows the time per-
spective, which was mostly used to identify bottlenecks. The case perspective
was only used in 17 % of the papers and the organizational perspective in 11 %
of the papers.
The most used process mining technique is process discovery (found in 48
papers). One reason for this is that the other three process mining techniques
require a process model, which is often generated via process discovery. Confor-
mance checking was applied in 13 papers and process re-engineering in 6 papers.
Operational Support was only used in three papers.
4.2 Process Mining Methodology
The methodology used in the papers clusters the papers according to the tasks to
be performed when applying algorithms and techniques for process evaluation.
Following [35], the present paper distinguishes between three methodological
approaches. The non-domain-specific ad hoc method is used in 21 papers. The
clustering method, consisting of the five phases log preparation; log inspection;
control flow analysis; performance analysis; and role analysis [6], is used in two
papers. The L* life cycle[37], as the third methodological approach, also consists
of 5 phases: Planning and justification; extraction; generating the control flow
model and linking the event log; generating the integrated process model; and
providing operational support [37]. This method was used in 4 papers. Most
papers (29 contributions) do not describe a concrete procedure based on known
methods.
4.3 Techniques, Algorithms, Tools and Software
In 30 papers special process mining algorithms are used, 36 % of the papers use
data mining and machine learning algorithms and 9 % use algorithms from other
areas. The most used algorithms are the process discovery algorithms [40] (10
papers), followed by the fuzzy miner [15] (6 papers).
Nearly half of the papers examined use the ProM4 software (42 %), 7 papers
use the R programming language and the Process Mining Toolkit Disco [14]
is used in 6 papers. Eight papers have not mentioned any software. ProM is
probably the most used tool as it comes with many plugins, offers an interface
to develop own plugins and the ProM core is open source5 [9].
4.4 Clinical Path Similarity
To answer research question RQ4, it was examined which papers cover the simi-
larity of paths. Eight papers deal with the similarity of mined clinical pathways.
The main challenge in the application of process mining techniques to medical
processes and the subsequent comparison of clinical paths is, in the eyes of 5
out of 8 authors, the flexibility with which the activities are performed. There-
fore, many clinical events occur randomly and often without a specified order.
Thus, many common similarity measures for processes cannot be applied. Fur-
thermore, it is stated that clinical processes are always time-linked. Therefore,
they can change significantly over time and as research progresses [18].
To be able to compare these flexible and heterogeneous clinical pathways, the
authors developed and used clustering approaches. The authors used these ap-
proaches to cluster activities and then calculated the similarity of the pathways
based on the identified clusters of a pathway instead of the specific pathway
with treatment activities. Only one approach defines a multidimensional simi-
larity measure and includes besides the pure procedural data also performing
actors/resources, and data values to calculate the similarity.
4
promtools.org
5
ProM 6 core, GNU Public License
4.5 Process Representation
None of the papers examines explicitly the use of process mining for case acquisi-
tion for CBR. Most papers use a procedural process modeling language like Petri
Nets [32] (9 papers), BPMN6 (2 papers) and PWF7 [12] (2 papers). However,
in most cases the exact representation is not given and the procedural charac-
ter of the process modeling language can only be inferred from the algorithms
used. Another representation was chosen by 7 authors, by using a declarative
approach. All seven papers chose the declarative process modeling language [38],
based on Linear Temporal Logic (LTL). The frequent use of Declare is due to
its integration into ProM. The authors usually justify this approach by the suit-
ability of declarative approaches for very flexible processes.
4.6 Research Gaps
To answer research question RQ3, research gaps were identified based on the
papers analyzed. For this purpose, the three-step procedure proposed by Müller-
Bloch et. al. [31] for identifying research gaps and the PICOS framework [34]
was used. This process consists of the localization and characterization of the
gaps in step one, the verification of the gaps in step two and the presentation of
these in step three. The following research gaps were identified.
No papers were found in the area of case acquisition using process min-
ing for knowledge-based systems (including CBR) in oncology. Studies on the
transferability of process mining-based approaches to case acquisition from other
domains to oncology are still missing.
One of the papers explicitly examines data quality issues in the process min-
ing context in data from a Dutch hospital. There is no equivalent study for
German oncology clinics. The complexity of the data from HIS is mentioned in
the papers, but not examined in detail. However, this is interesting for the more
advanced and especially for the multi-perspective process mining approaches.
Therefore, further studies could provide a basis for further research in this area.
The cancer best researched with process mining technologies is gynecological
cancer due to the BPI Challenge data set. Other data sets, such as the MIMIC
III data set or the data sets used in [30,23] are not suitable for performance
analysis due to data problems [24]. This indicates the urgent need for other
available data sources in this domain.
The next gap describes the need of a data quality indicator [2,5,39]. There
should be a method to measure the data quality of event logs. This is necessary
for unsupervised learning techniques like Sched-Miner which rely on data quality
due to the use of unsupervised learning [2]. The three noise types mentioned
in [39] are a good starting point for further research concerning the quality
indicators.
Research gaps were also identified in process reengineering. Declarative Pro-
cess Mining deals well with highly variable processes which are the standard for
6
Business Process Model Notation, https://www.omg.org/spec/BPMN
7
Pseudo-WorkFlow Language
healthcare processes. In particular, there is a need for research in the preparation
of a correct declarative constraint set based on guidelines and an adapted real
log to be replayed [33,29].
5 Conclusion and Future Work
The analysis of the papers shows that most papers focus on the analysis of data
using process mining and less on describing the process and difficulty of exporting
and extracting HIS-data and transforming them into event logs. Data from HIS
is described as noisy, incomplete, and complex. This results in a complexity of
the mining models, which is due to the lack of data quality on the one hand, but
also to the high flexibility of the treatment processes in hospitals.
With regard to process representation and semantification, it can be noted
that none of the papers examines the use of process mining for case acquisition
for CBR. Most approaches rely on a procedural process modeling language, while
7 papers chose a declarative approach. The authors usually justify this approach
by the suitability of declarative approaches for very flexible processes.
In applying similarity measures to oncological processes, the authors see par-
ticular challenges in the fact that the processes are highly flexible and change
over time as research progresses. Specific challenges for oncological data that
differ from other medical domains were not mentioned.
The application of process mining in oncology especially focuses on the con-
trol flow perspective. This is probably partly due to the fact that the control flow
perspective is often used as the basis for the other process mining perspectives
[13]. In terms of methodology, the ad hoc approach is followed mostly by the
papers. Compared to the other methodology, it can cope with the complexity of
real-world clinical processes [7]. In technical terms, the authors used the heuristic
miner most often, arguing that the miner is particularly good at handling noisy
data. The most widely used software is ProM.
The results provide a basis for future research in the field of case acquisition
from oncological procedural data in HIS using process mining. The investigation
of approaches to case acquisition using process mining and the answering of the
question of the transferability of the approaches to oncology would be of par-
ticular interest. Also, the analysis of data and data quality in German oncology
departments in the context of process mining would be of interest for further
research. It would also be interesting to systematically investigate the potentials
of process mining in CBR approaches.
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