=Paper=
{{Paper
|id=Vol-2759/paper2
|storemode=property
|title=Decentralized Management of Patient Profiles and Trajectories through Semantic Web Agents
|pdfUrl=https://ceur-ws.org/Vol-2759/paper2.pdf
|volume=Vol-2759
|authors=Jean-Paul Calbimonte,Davide Calvaresi,Michael Schumacher
|dblpUrl=https://dblp.org/rec/conf/semweb/CalbimonteC020
}}
==Decentralized Management of Patient Profiles and Trajectories through Semantic Web Agents==
https://ceur-ws.org/Vol-2759/paper2.pdf
Decentralized Management of Patient Profiles
and Trajectories through Semantic Web Agents?
Jean-Paul Calbimonte1 , Davide Calvaresi1 , and Michael Schumacher1
University of Applied Sciences and Arts Western Switzerland (HES-SO),
Sierre, Switzerland
{firstname.lastname}@hevs.ch
Abstract. The usage of healthcare data for analytics and patient appli-
cations has increased in recent years opening a number of technical, eth-
ical and scientific challenges. Among these, those related to the manage-
ment of personal and sensitive health data have been addressed through
decentralized solutions for patient data, often implemented and modelled
using distributed agents and semantic technologies. In this paper1 we
present a technical summary of our previous works in this area, compris-
ing efforts to: (i) use ontology models to represent patient trajectories,
(ii) employ agent-based architectures to model and employ decentral-
ized patient data exchanges, (iii) define agent cooperation and negotia-
tion strategies for healthcare data interactions, (iv) adopt semantic data
models for privacy-aware agents, and (v) implement multi-agent systems
for real-time healthcare data processing.
1 Introduction
An increasing number of patients face the challenge of having to follow treat-
ments at home, often having to cope with complications and issues, having to
rely on healthcare professionals only when the situation becomes critical. Typical
examples include people suffering from chronic diseases, or conditions following
cancer survival [10]. Under these circumstances, physicians and healthcare per-
sonnel need to personalize the treatment and take into account individual char-
acteristics of each patient, e.g., understanding the details of previous medical
encounters, interventions, medication, symptoms, and co-morbidities. Digitiza-
tion of health care records and the use of hybrid care technologies including
monitoring and virtual health coaching have showed a great potential for im-
proving the quality of support for these patients. In fact, nowadays health records
include a considerable amount of information, which is often not fully exploited
for the benefit of patients.
The emergence of methodologies to characterize patient trajectories [1] has
opened a way for systematic analysis of patient contextual data, including health-
care events, conditions, co-morbidities, emotional and social indicators, self-
reported outcomes, and observations during and after treatment [8, 15]. However,
?
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
1
This paper is a Technical Summary of previous published works.
2 J.-P. Calbimonte et al.
so far there is still a need for comprehensive techniques that allow exploiting
trajectory data using Machine Learning (ML) and other related AI techniques.
Although numerous previous works have focused on prediction, classification
and automated learning on specific types/parts of a patient health record, the
key challenge lies on the combination of the different layers that constitute a
trajectory.
In this work, we advocate the use of intelligent agents as the foundational
information management entity for patient trajectories, in conjunction with se-
mantic models to support data exchange. We summarize our previous efforts in
this context, which constitute a solid basis for semantic-aware and decentralized
management of patient trajectory data. We argue that patients can delegate
the management of personal trajectory data to dedicated agents, which in turn
can automatically negotiate and cooperate with other agents, for instance to
share and aggregate anonymized data, to grant access to agents of medical staff,
or to allow ML processing and prediction. The scientific contributions summa-
rized in this work essentially identify two key research axes: patient trajectory
heterogeneity, and decentralization of data and processing. The first dimension
refers not only to the high diversity of potential data sources (EHR, sensor data,
imaging, self-reported outcomes), but also to the heterogeneity of modelling and
representation of trajectories. Regarding decentralization, given the distributed
nature of data sources and processing units, as well as the necessity for pa-
tients to have full control over their personal data, it is mandatory to provide
technological means for autonomous data management.
In the remainder of this paper, we introduce the different challenges addressed
in this context (Section 2), before briefly describing how each of our previous
works addresses them. Hence, we discuss our contributions on ontology-based
modelling of trajectory data in Section 3, decentralized personal data in Sec-
tion 4, agent cooperation and negotiation in Section 5, privacy-aware agents in
Section 7, and real-time and stream processing of trajectory data in Section 6.
We provide a discussion on perspectives and future research lines in Section 8.
2 Challenges in Patient Trajectory Data Management
Among the numerous challenges regarding patient trajectory management and
its exploitation we focus on the following fundamental aspects:
– Trajectory modelling. The difficulty of exchanging heterogeneous data is of
particular importance in the healthcare domain, and more so in trajectories
where information pieces may span from demographics to radiology images,
or multi-parametric sensor observations. A coherent trajectory model can
only be constructed if it relies on solid foundations, linked to existing stan-
dard vocabularies in the medical realm. Moreover, these models must be
machine-understandable in order to allow autonomous entities (i.e., agents)
to take decisions based on their contents.
– Decentralization. Patient data is bound to be stored, managed and processed
in a distributed manner. The provenance of trajectory data typically includes
Decentralized Patient Trajectories Management through Semantic Agents 3
different hospitals, clinics and medical cabinets, which are not expected to
respond to central governance. Moreover, given the current regulations on
personal health data [14], patients may now have full control over where and
how their data is stored and/or processed. It is imperative to empower pa-
tients and participants and allow them to take informed decisions regarding
their own data, as well as results, clinical studies and processing outcomes
based on them.
– Cooperation. A comprehensive approach to the exploitation of patient tra-
jectory data can only be possible if multiple decentralized parties exchange
responsibility and processing/analytics duties. Given that trajectory data
may be managed by different entities, it becomes necessary to provide the
mechanisms that guarantee effective interactions among them. These cooper-
ation schemes include participatory data collection, data reuse, collaborative
processing, etc.
– Data Volume & Velocity. It is essential to consider not only the large volumes
of data related to patient trajectories, but also the speed at which they
are produced and processed. We underline the importance of providing the
capability of responding to real-time constraints, as well as to the streaming
nature of certain trajectory data sources, e.g., those provided by sensing and
IoT devices.
– Data Privacy. None of the above challenges can be fully addressed if the
sensitive nature of patient data is not taken into consideration. Privacy
preservation as well as trustful interactions are absolutely necessary for any
trajectory-based system to be acceptable according to current regulations
regarding data protection [14].
In the next sections we indicate how our previous works addressed these chal-
lenges, fundamentally through agent-based models and semantic technologies.
3 Ontology-based Trajectory Modelling
Fig. 1: Excerpt of relevant concepts for patient trajectories from schema.org [9].
In order to cope with the heterogeneity of patient trajectory data sources
and types of data, we have proposed to represent them using semantic models
4 J.-P. Calbimonte et al.
linked to standard vocabularies in the medical, pharmaceutical and healthcare
domain [4, 6]. Our approach regarding trajectory modeling follows an ontology-
based methodology. A trajectory is conceived as a knowledge graph centered
around the patient, and linking different entities that represent medical con-
ditions, therapies, symptoms, drug prescription, diagnosis, monitoring observa-
tions, etc. We depict in Figure 1 a subset of such ontology model, based on
the widely used schema.org [9] vocabulary. The implementation of this data
model provides a solid basis for trajectory data exchange, with the possibility
of mapping to standard formats such as HL7 FHIR. In parallel, our approach
also emphasizes on the use on standard healthcare and bioinformatics ontolo-
gies. In our data model, we link schema.org coding attributes to standard terms
in ontologies such as MeSH [11] or ICD-10 [12]. As a result, and as we will see
later, a trajectory encoded as RDF following this model can be shared, processed
and consumed by autonomous entities, according to established goals. Further-
more, explainable results can be represented using this semantic representation,
providing the means for human-understandable trajectory analysis.
4 Decentralized Personal Data Management
One of the central contributions of our research on patient trajectories is the
design of autonomous agents capable of managing personal health information. A
main application for such agents is to provide behavior change support to patient
with chronic conditions [3]. As depicted in Figure 2, we propose a model in which
each user (patient) agent has exclusive access to personal data, providing with
the ability to manage fine-grained access control and consent for any sharing or
processing actions. Each agent is equipped with a domain model that describes
as a knowledge graph the different factors that may affect behavior change.
For example, this may include motivation or ability factors towards specific
behavior change goals, e.g., adherence to a rehabilitation treatment. The agent
also includes a set of stages, goals and milestones which can be incorporated to
an internal state machine, as well as computational persuasion strategies that
might be launched according to currently detected inputs and interactions. All
these agent processes take into account the patient-specific information contained
in the user profile, which is kept as part of the agent beliefs, and is typically
composed of trajectory information modeled as described in Section 3.
Beyond the patient private sphere, the model also considers the following
additional agents: (i) coordinator: it oversees the incorporation of new patient
agents, as well as governing over stage changes and regulating interactions and
disseminating domain model information that may affect groups of participants
in a behavior change program; (ii) coach: it provides specialized guidance to a
patient through his user agent, demanding access to certain personal data if it
requires to be processed by a behavior agent; (iii) mediator: allows patient-to-
patient interactions that may be useful in order to adopt peer-to-peer strategies
for behavior change; (iv) behavior agent: in charge of providing trajectory and
behavior pattern analysis through AI exploitation of anonymized data.
Decentralized Patient Trajectories Management through Semantic Agents 5
Fig. 2: A model for agent-based behavior change support applications [3].
5 Inter-agent Cooperation and Negotiation
Beyond modelling the agents themselves, it is critical to provide well-defined
strategies for cooperation among patient (And trajectory) agents, so that they
are able to interact as described in the previous section. We introduced in [6] the
concept of τ Agents, or patient trajectory management agents (Figure 3), which
are characterized by a set of goals, beliefs, and behaviors; and include specialized
knowledge graphs of patient trajectory data. τ Agents may play different roles
and accordingly adopt different (and even competing) goals and behaviors. For
instance, a patient agent may focus on improving quality of life indicators, or
to retain moderate physical activity over time. In contrast, a coaching agent
may define its goals in terms of level of adherence of its assigned patients a
certain therapy. The same logic applies to beliefs and behaviors, and the resulting
interactions among τ Agents. As seen in Figure 3, some agents may focus on data
acquisition behaviors (e.g., continuous monitoring of health indicators), others
on trajectory analysis (e.g., through machine learning algorithms), or patient
trajectory anonymization and aggregation through a negotiation process (e.g.,
consent granting). We provide further details in [6] about how communication
channels in τ Agents use RDF as underlying representation model to achieve
these interactions, embedded into standard agent protocols.
6 Real-time stream processing agents
The ubiquity of sensor technologies for monitoring and reporting health-related
observations has already had a profound impact on clinical protocols, studies,
and patient support services. The consumption and analysis of data streams pro-
duced by these sensors is challenging, especially for decentralized architectures.
In [5] we propose a model for stream processing agents, capable of acting ac-
cording to beliefs and goals represented as RDF streams, and including real-time
constraints. The proposed architecture is centered on the notion of RDF Stream
Processing RSP agents, which are autonomous entities as those in Section 5,
with the added ability of communicating and exchanging RDF streams.
6 J.-P. Calbimonte et al.
Fig. 3: Schematic view of τ Agents for managing patient trajectories [6].
Following the Semantic Web principles, each agent and its resources are
uniquely identified through URIs, and are equipped with endpoints that can
be used to reach the RSP agent resources. The resources of each RSP agent in-
cludes the metadata of the RDF streams it manages, as well as other information
relative to them (i.e., background RDF datasets, RDF stream buffers, ontology
TBoxes, and RDF constraint rules). Depending on the nature of the RSP agent,
it may implement different types of stream processing mechanisms such as con-
tinuous query processing, complex event processing, and stream reasoning.
As an example, the Stream Receiver in Figure 4 is a special case of a RSP
agent that is able to consume streaming messages arriving at its inbox. The
producer agent, or Stream Sender, can push streaming data at a given rate,
which can be negotiated between the two parties. All streaming RDF stream
messages are semantically annotated, e.g., using the data model in Section 3.
Fig. 4: RSP Stream Receiver interactions [5].
7 Privacy-aware Agents for Patient Data Exchange
Having described our approach for agent-based decentralized management of pa-
tient information, it would not be complete without considering complying with
privacy protection regulations and guarantees. The enforcement of the General
Data Protection Regulation (GDPR) [14] imposes, for instance, the need for
explicit consent for data reuse, the right to timely receive all collected data, or
the right to completely delete personal data. Beyond existing GDPR-compliant
Decentralized Patient Trajectories Management through Semantic Agents 7
frameworks [2] that rely on centralized solutions, we explored and proposed in [7]
the adoption of decentralized agent-based data privacy negotiation, coordination,
and enforcement, using semantic representations of personal data privacy.
Fig. 5: Agent-based personal data privacy interactions [7].
We defined a set of minimal personal data privacy interaction requirements
among agents and the design principles of privacy-aware agent interactions re-
garding personal data handling. Then, we integrated these concepts into an ar-
chitecture design based on multi-agent protocol specifications, encoded as RDF
messages. We base this specification in the Data Privacy Vocabulary (DPV) [13],
developed by the Data Privacy Vocabularies and Controls Community Group of
the W3C. We identified three main design principles to enable privacy compliant
agent interactions:
– Decentralized agents: Following the nomenclature of the GDPR these privacy-
aware agents can be: data controllers, subjects, recipients, and processors
(Figure 5). Controllers refer to people or organizations that govern personal
data processing. Subjects are the persons to which the data is related, while
recipients are those to which personal information is disclosed. Processors
are entities that process personal data on behalf of the controller.
– Shared semantic vocabulary: Semantic interoperability among these agents
is dictated by the use of a common ontology for representing privacy data.
We advocate the use of the Data Privacy Vocabulary (DPV)2 .
– Data privacy agent interactions: In principle, we base the definition of these
interactions in existing FIPA protocols. For instance, a data consent request
can be embedded in a request interaction protocol, or a data crowd-sourcing
request can be represented as a ContractNet protocol.
8 Perspectives and Future Directions
Our vision for decentralized management of patient trajectory data using se-
mantic agents has shown the potential of relying on autonomous processing and
delegation of sensitive data processing. Nevertheless, it still remains necessary to
take these building blocks and use them to implement our vision of decentralized
2
https://www.w3.org/ns/dpv
8 J.-P. Calbimonte et al.
patient trajectory management. In this section we provide a first glimpse of how
this could be materialized.
As shown in Figure 6 we can represent the past events and circumstances of
the patient history as a (retrospective) trajectory, in which each item is encoded
as a semantically annotated entity (as described in Section 3). This trajectory
is therefore part of a patient knowledge graph, in which —for example– a diag-
nosis can be represented as a MedicalCondition, with a specific ICD-10 code.
The trajectory itself can also characterize the patient degree of distress/well-
ness, not only from a physical perspective, but also considering the social and
psychological dimensions. For instance, the patient trajectory may exhibit a
general physical decline after a surgery, while she can show general emotional
improvement following physical therapy. If these trajectories are collected for a
large number of participants, it is then possible to compute patterns and build
models that allow predicting future outcomes. In Figure 6 we also show how
predictive trajectories can be build using ML models, providing alternative tra-
jectories depending on previous events, as well as on actions taken and achieved
goals. As an example, given a certain retrospective trajectory, the system may
predict three distinct trajectory outcomes depending on the degree of adherence
to a therapy, enabling a personalized assessment of risks.
Fig. 6: Retrospective and predictive semantic trajectories.
Following our architectural and conceptual propositions described in Sec-
tions 4, 5, and 6, we provide a use-case example of how agent-based interactions
can be used to enact trajectory-based analytics and support (Figure 7). Consider
a cancer survivor who follows physical therapy to regain and maintain muscular
strength. She is equipped with wearable sensors that collect motion data that
can constitute part of her trajectory, using a stream-based model as explained
in Section 6. However, this trajectory data is controlled by her through a patient
trajectory agent (τ Agent), and is not available to others unless explicit permis-
sions are granted. As explained in Section 7, a coaching τ Agent can emit a call
for data, specifying purposes and other details through a consent, to which the
patient agent can affirmatively respond. Once the access is granted, the data pro-
Decentralized Patient Trajectories Management through Semantic Agents 9
vided by the patient’s sensor agents can be shared with the coaching agent. Sim-
ilarly, retrospective trajectory data can be collected from a set of patient agents,
and used to build a predictive trajectory model. The interaction between the
coaching and processing τ Agents allows the former to provide a personalized
advice to the patient, while informing a therapist (through his corresponding
agent) about the general progress, risks, and other relevant information.
Fig. 7: Sequence of tauAgent interactions for the collection, processing and shar-
ing of trajectory data.
Although our contributions in the works summarized in this paper provide
preliminary evidence of applicability in the eHealth domain, there are still several
research directions to be explored, included in our road-map. In the following
we mention the most prominent ones:
(i) Trajectory modelling. Our proposed semantic model approach for patient
trajectories is a first attempt that still needs to be validated in more com-
plex scenarios, possibly in conjunction with data exchange, for instance
in clinical trials where consent management and data restitution are criti-
cal. We also anticipate the need for domain-specific vocabularies/ontologies
that may be required in order to better represent trajectory events, decision
support, and delegation scenarios.
(ii) Implementation and validation. A key step in future research is the im-
plementation and deployment of the proposed agent-based architecture,
under real conditions and with a large cohort of patients. The complexity
of the evaluation and validation of this approach is an additional challenge
that will need to be addressed in order to provide an assessment from a
technological and health-care point of view.
(iii) Decentralized explainability. A topic that has not been sufficiently explored
concerns the explainability of trajectory analysis, and more so when ML
algorithms are executed in a decentralized manner, with the risk of losing
provenance information or missing relevant contextual information.
(iv) Trust and accountability. Unlike top-down approaches for data privacy
compliance, our vision for decentralized personal data privacy interactions
10 J.-P. Calbimonte et al.
has the potential of allowing more flexible and scalable processing and pro-
tection of sensitive data. Nevertheless, it remains necessary to study the
degrees of trust among participating agents, and to establish negotiation
procedures when undesirable behaviors are detected. Accountable trajec-
tory agents should be studied in order to address these concerns.
(v) Patient empowerment. Privacy protection is not the only aspect in which
patients have the right to manage their own data. Patients should be pro-
vided with mechanisms for tracking their own data (e.g., for a clinical
study) or even providing feedback, reusing it for her own benefit in a dif-
ferent context, or even exploring it through a set of third party services.
Acknowledgements
This work is partially supported by the H2020 project PERSIST: Patient-centered
survivorship care plan after cancer treatment (GA 875406); and the HES-SO
RCSO ISNet Project PERSA.
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