The volume and diversity of healthcare information available through the use of modern technology ofers great potential for improving health services delivery. Giving healthcare practitioners and health system administrators the ability to investigate and analyse data from various perspectives can promote evidence-based decision making. Current analysis approaches often result in models where essential relations are dificult to depict and/or discern. Further it is not easy to dynamically change the level of detail in order to accommodate user requirements and allow users to analyse data from various perspectives. We outline an approach involving filtering and abstraction of event logs using ontologies, dimensional modeling and cluster analysis so healthcare professionals may access the information they need. Our approach enhances existing process mining techniques by incorporating data mining techniques for dividing patient records into closely related groups, and results in contextual process mining. healthcare information, abstraction, process mining, community detection, visualization, ontology, Today's vast amount of healthcare information needs to be accessed easily, integrated intelligently and analysed appropriately to support better healthcare delivery. With intelligent integration and analysis, data can be used to gain deeper insights into patient health using historical healthcare information (including a patient's symptoms, appointments, exercise routines, lab results, vital signs, prescriptions, allergies, assessments, etc.) and also can be used to determine the efectiveness of treatment. Context relevant clinical information can enable improved decision making. Healthcare data analysis across disciplines can facilitate the discovery of new knowledge which can lead to streamlined workflows, greater eficiency and improved patient care. Systematic analysis of healthcare data can help to detect patterns so that healthcare providers can optimize their resource allocation and clinicians can optimize treatment plans for individuals leading to better health outcomes.
vidual practitioner and at the system level. Clinicians require information regarding various
treatments applied over diferent patient groups to choose the best treatment plan for the
individual patient. At the system level, there is an increasing demand to optimize systems by
the eficient use of the limited resources available. Both clinicians and administrators would
benefit from more intuitive tools that would allow them to more fully utilize the data available to
them without the need for sophisticated technical knowledge. They are particularly interested
to find common pathways for patients, to ascertain how a process model be improved and to
determine to what extent existing systems are following clinical guidelines. However it is not
easy to meet these demands as healthcare processes are highly dynamic, complex, ad-hoc, and
are increasingly multidisciplinary [
Data mining techniques present the opportunity to analyse and learn from healthcare
information, from numerous viewpoints (i.e., contexts) such as with respect to patient populations
with specific diseases, ages, gender, incidences of co-morbidity, type of healthcare service
setting (e.g., clinic, hospital, nursing home), home location (urban or rural), procedures used,
etc. Clustering [
In [
In this paper we propose a data mining approach that integrates with and augments process mining which also utilizes healthcare ontologies. The proposed approach has the potential to analyze patients with similar issues. We present a flexible approach for extracting healthcare data through a data preprocessor designed to pipe the data extracted according to a particular user’s specifications to existing data analysis tools. The experiments described in this paper used data on patient admission, diagnosis, and procedural activities over the period of 6 months taken from the Norwegian Patient Registry (NPR data).
The rest of this paper is organized as follows. Section 2 outlines popular solutions available for analysing healthcare data and discuss some of the problems that remain. Section 3 describes how our data abstraction methods can be used to enhance output from community detection technology. Section 4 discusses related work and in section 5 we conclude the paper and give directions for our future work.
In this section we outline popular techniques for analyzing healthcare data, both timed (i.e., associated with processes) and untimed, and point out several problems that remain.
In many developed countries, most citizens use public healthcare services involving multiple service providers providing diverse services supported by numerous software applications. Various types of data analysis are required due to the various roles played by healthcare professionals e.g., while a clinician is interested to see the progression of diseases and to study the efects of diferent treatment procedures, healthcare managers are interested to study the patient flow from one clinic to another and the number of patients waiting to get services.
In principle, process mining can be used to acquire and explore new knowledge about patient condition, adherence to treatment program, efectiveness of treatment, etc. The main focus of process mining in healthcare is to provide evidence-based process analysis techniques for efective process management [ 10]. It combines data mining and process analysis techniques and is used to discover trends and patterns of process executions by analyzing the trace of activities performed in a system. These traces are referred to as ‘event logs’. However, existing process mining techniques use event logs as input which typically consist of event information such as case-id, event time, event name and attributes of events. Since healthcare information is much more complex than information from most other domains, current process mining techniques cannot adequately mine the information from the event logs required by the various health professionals and therefore are not exploited to their full potential.
In a healthcare setting, patients may need to visit various health facilities for various health related issues and also may need to use certain online treatment programs. Hence, for efective process mining, event logs representing activities of various systems must be considered. Event logs from a variety of systems using various data definitions and formats must therefore be harmonized before they can be analysed by any process mining algorithm. Another issue with applying process mining in healthcare data is related to the diversity of information. Identifying common pathways for patient flow in healthcare systems is complicated by the large variety of patient conditions and diagnoses. In healthcare, the data preparation task is very critical as healthcare data are very sensitive and therefore semantics of the data must be preserved. Getting the right setup for data preparation is important to get the best understanding from the data as eficiently as possible [ 11]. The current situation in healthcare information systems makes this a complicated problem indeed.
Imagine a situation where we have event logs representing patient information with respect
to patient visits to various clinics (or service points in the hospital), and patient diagnoses.
Table 1 shows a portion of such an event log. The first column of the event log shows case
id; the second column shows when the event occurred; the third column shows event names,
(such as check in at a department or clinic, or a clinical finding) and the fourth column shows
the resource for the information. Due to the complexity in the process flow and diversity of
to be prepared in diferent ways in order to support a wider variety of queries. In the following,
we provide an example to illustrate this need. Considering the event logs from Table 1, suppose
we wish to investigate the flow of those patients to various departments in the hospital who
have been diagnosed with some mental disorders, i.e., they have an ICD-10 code in the range
F00-F99.9. We wish to focus on only those patients who are very similar or, in other words,
belong to a community structure. The event logs in Table 1 consists of some information at a
very low level, i.e., visit to clinics and specific diagnosis information. We need to relate clinics
to their department hierarchy and specific diagnosis information to their diagnosis group from
ICD-10 ontology. Relating clinics to departments and diagnosis to diagnosis group allows us to
choose the desired abstraction layer. This choice of abstraction layer for defining the context
and integrate community detection algorithms are not supported by existing process mining
tools. In [
Healthcare data are received from various healthcare service providers and personal health
applications such as patient monitoring applications and sensor devices. It is becoming a
dificult task for health caregivers to analyze all these data from various sources and extract
information in a meaningful way. Visualization of healthcare information can greatly help
care providers to gain insights from patients data and make important decisions [14]. Data
mining techniques such as community detection are used to identify useful information from
healthcare data and the outcome of such data mining technologies can be useful to healthcare
providers enabling them to understand efectiveness of treatment processes and to streamline
healthcare processes and treatment protocols [
One of the challenges for revolutionizing current healthcare systems is to adapt the treatment procedure according to the various needs of a diverse group of patients. This requires better understanding of each patient’s condition which include patient demographics, symptoms, preferences, interests, etc. In general, healthcare systems consist of various processes inside hospital organizations and several other processes outside the boundary of healthcare organization to support online treatments. The central motive of personalized medicine is the premise that an individual has a significant role in disease vulnerability and the selection of treatments [ 15]. Such personalization and adaptive treatment systems behave diferently for diferent users. The decision on how these systems should behave for any particular user is based on a user model, which is a detailed representation of an individual user’s information, such as user preferences, interests, behavior, background, knowledge, individual traits, and others. As these attributes are dynamic it is essential to create, maintain, and update the user model.
There exists several community detection algorithms to partition the dataset based on the
association of its individuals. Patient demographic information along with other information
can be efectively used to group patients using clustering or community detection algorithms
such as k -Means Clustering Algorithm [
To exploit the vast amount of information available in healthcare information systems, we need
to develop ways to support contextual data analysis. One may need to both filter information
to select certain subpopulations of patients, or certain clinical contexts, and also to group
information to allow patterns to emerge from a more abstract view. The main challenge in
performing data analysis using existing process mining and clustering tools and techniques is
the lack of support for the needed filtering and abstraction which would give the individual
practitioner or administrator the ability to extract and explore data from whatever perspective
they deem appropriate. In [
The NetworkX python library allows us to construct directional and bi-directional graphs with many nodes and edges. The library allows us to export the graphs in various formats such as GraphML which can be used to import graph visualization tools such as Gephi. Figure 2 shows the clustering output of a sample NPR data. For this experiment we filtered the patients by their association with diagnosis. We constructed a graph that consists of nodes with patients identification (shown in integer number), and patient diagnosis (ICD-10 code) and with edges representing the relation between a patient and their diagnosis. In Figure 2 we used the low level ICD-10 code, i.e., leaf nodes from the ICD-10 hierarchy.
The figure shows some diagnoses were made for many individuals (the large purple cluster associated to F900) and some diagnoses were made for only a few individuals (the small grey cluster associated to F422 in the lower left hand region of Figure 2); it also shows that there are some isolated clusters (such as the grey cluster associated to the F422 diagnosis) as well as some clusters which seem to contain some of the same individuals, i.e., we see some individuals which occur between two or more diagnosis (individual 10 in green appears to belong to both the F401 and F321 diagnoses) indicating that individual 10 had both diagnoses.
To produce Figure 2 we used a community detection algorithm [
135
Although this visualization gives us an idea of overall clustering associated with diagnoses, and indicates that some patients are associated with more than one diagnoses, the fine grained detail prevents us from getting a quick visualization giving meaningful information on patients having more than one diagnoses.
We constructed another graph, see Figure 3, where we used the group code of ICD-10 diagnostics code (i.e., diagnoses in the range F00_F09 are grouped, etc). Here diferent colours denote diferent clusters and the size of the node representing the group code is determined by the degree of the node (i.e., the number of patients with that group code). This abstraction yields a better visualization: we can more easily pick the patients who have been diagnosed with multiple mental issues. From this figure we can see that many of the patients were diagnosed with F40_F48 (Anxiety, dissociative, stress-related, somatoform and other nonpsychotic mental disorders), F30_F39 (Mood [afective] disorders), F20_F29 (Schizophrenia, schizotypal and delusional disorders), F90_F98 (Unspecified mental disorder), F10_F19 (Mental and behavioural disorders due to psychoactive substance use) and the overlap is indicated by the patient nodes in the center of the graph with their association with multiple diagnosis nodes.
Now that we have seen how ontologies can be incorporated in community detection, we show how the selected group of patient event logs can be analyzed with process mining tools. An analyst may be interested in investigating the progression of diseases or the admission flow of patients in diferent departments. In our approach, appropriate events are prepared based on Raw data
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DiSmeeanrcshion ZZ0909Community detection basedon a chosen abstraction level The flow shows theprogression of diseases for a selected group of patients.
Processmining based on Selected events from a community of patients.
User also specifies the dimension for processmining.
Patients with similar issues are found in the samecommunity.
The flow shows themovement of patients to
differentdepartments in the hospital.
the selection of event types e.g., diagnosis, admission, etc. We enrich the model based slicing
approach presented in [
Bistarelli et al. presented a prototype tool called PrOnto in [20] which can discover business processes from an event log and classify them w.r.t a business ontology. The tool takes an event log file as input and produces an UML based activity diagram in XML format. The aim of the approach is to raise the level of abstraction in process mining by utilizing business ontologies. They proposed an ontology representing the hierarchy of resources. In their approach, resources are the actors of the activities. Since the resources are given an ontological hierarchy, it is possible to define which level of abstraction will be used for process mining. They proposed to use integer numbers to define the level of abstraction. Defining a high level of abstraction would merge several activities being performed by all the actors that belong to the high level classification of resources. Our work is diferent from the approach presented in [ 20] in a sense that we proposed to use dimensional models and ontologies to classify event logs. Our approach is more flexible, since it is possible to be more specific in one portion of the process model while being more generic in another portion of the process model. The idea of combining dimensional modeling with ontologies is novel in this paper. We have shown that the mining process includes some preprocessing steps. In these preprocessing steps the user specifies the context (using dimensional modeling and ontologies) to define a patient group and also specifies the level of abstraction (using dimensional modeling and ontologies) that will be used to visualize the process mining output.
In [21], the authors discussed the application of process mining in healthcare and provided an overview of frequently asked questions by medical professionals in process mining projects. The questions reflect the medical professionals’ interest both in learning common pathways of diferent patient groups, to determine their compliance with internal and external clinical guidelines, and also in gathering information about the throughput times for treating patients. The authors pointed out the need for accumulating data from diferent data sources and they claimed this to be a major challenge in healthcare. In the conclusion of the paper the authors suggested that ontologies can be used for defining appropriate scope and for identifying the cases from diferent data sources. They urged the exploitation of ontology-based process mining approaches in the healthcare domain.
In [22], the authors discussed the necessity of relating elements in event logs with their semantic concepts. By linking event logs with the concepts from an ontology they presented a process mining approach that performs concept-based analysis. The idea of using semantics makes it possible to automatically reason or infer relationships of concepts that are related. They distinguished between the application of process mining in two diferent levels: instance level and conceptual level. They illustrated their ideas with an example process model to repair telephones in a company. That process model included three diferent ontologies: a Task ontology, a Role ontology and a Performer ontology. The idea of using an ontology for process mining presented in [22] is very similar to our approach. The idea of filtering based on ontological concepts and the idea of grouping nodes by a high level ontological concept is similar. However, in our approach we emphasize the benefits of on ontology based process mining for the healthcare domain. While in [22] the authors implemented their technique in ProM, our approach is more general as it ofers a pre-processing step where we filter events for patients from a tightly-knit group using data mining technique and import the filtered events to Fluxicon Disco process mining tool.
A K-means clustering algorithm was presented in [23] where the authors enhanced the
traditional K-means algorithm by means of a semantic model. The approach is similar to our
community detection analysis where we utilize an abstract layer which gives semantics for our
model. In our approach we focused on the usability of incorporating a flexible abstraction layer
for healthcare data analysis. Rosvall et al. in [
Machine learning techniques have been employed in a variety of healthcare studies such as diagnostic code assignment [24], patient representation [25], etc. However, the potential for these machine learning algorithms and their integration with process mining techniques for analyzing healthcare information needs to be exploited. Healthcare data, due to its complex nature, can be modeled as a heterogeneous network. Representation learning methods [26] can be used to analyze the community structure of healthcare information represented as heterogeneous networks. According to [27] representation learning methods on heterogeneous networks could be divided into three categories: Path based, Semantic unit based and Other methods. Similar to node2vec [28] for homogeneous node embedding methods that preserves the random-walk probabilities in the feature space, for heterogenious networks, one might try to preserve metapath probabilities, which is a more sophisticated variation of path. MetaPath2vec [29], MetaGraph2vec [30] fall into the Path based category which use path-based random-walks and a heterogeneous skip-gram model to learn node representation vectors. The basic principle of graph representation algorithms is to preserve the relationship and structural properties of network in a low dimensional vector space which therefore can essentially be used for extracting information about similarity of nodes from a network. Semantic unit based based algorithms such as [31] define particular semantic units by means of capturing semantic information in the embedding space. Du et al. [32] presented an algorithm for network representation learning based on a graph partitioning strategy where a heterogeneous network is partitioned into homogeneous and bipartite subnetworks and the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Although these machine learning based approaches are relevant for studying healthcare information, they need to be adapted to support the analysis of healthcare information which includes large healthcare ontologies, and temporal aspects.
This paper proposes a model-based approach for determining patient pathways based on contextual process mining. We incorporate visualization techniques for filtering and analyzing patient records. Setting the context for process mining using community detection together with dimensional analysis enriched with healthcare ontologies is novel in this paper. The approach we presented here uses a flexible abstraction layer which can be tailored to meet the needs of the user. We envision a healthcare information system that supports an easy-to-use tool based on a graphical query language that permits a variety of healthcare professionals to investigate data from their personal and specialized perspective. In future we plan to develop methods and tools incorporating state-of-the art artificial intelligence techniques to apply our approach to a large healthcare dataset.
This research was supported by the Research Council of Norway with funding to INTROMAT (INtroducing personalized TReatment Of Mental health problems using Adaptive Technology), project number 259293. health problems / World Health Organization, 10th revision, 2nd ed. ed., World Health Organization Geneva, 2004. [9] O. Bodenreider, R. Cornet, D. J. Vreeman, Recent developments in clinical terminologies snomed ct, loinc, and rxnorm., Yearbook of medical informatics 27 (2018) 129–139. [10] A. Partington, M. Wynn, S. Suriadi, C. Ouyang, J. Karnon, Process mining for clinical processes: A comparative analysis of four australian hospitals, ACM Trans. Manage. Inf.
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