GIMO-PD: Towards a Health Technology Proposal for Improving

the Personalized Treatment of Parkinson’s Disease Patients

E. Enamorado-Díaz and J. A. García-García

University of Seville, Escuela Técnica Superior de Ingeniería Informática, Avda. Reina Mercedes s/n, 41012 Seville, Spain

Keywords: Clinical Practice Guideline, Computer-Interpretable Guidelines, Model-Driven Engineering Paradigm,

Parkinson’s Disease.

Abstract: Parkinson's disease (PD) is the second most common neurodegenerative disease and its pharmacological

treatment usually has unwanted side effects (motor fluctuations, dyskinesias and other motor alterations).

These effects vary from patient to patient, resulting in the use of «trial and error» manual methods by

healthcare professionals to optimize treatment. The GIMO-PD project (Mobile health solution based on

Genetic profile, Image analysis and the permanent Monitoring of symptoms for the personalized management

of Parkinson’s Disease patients) aims to present a technological solution for improving clinical decision-

making on the allocation of appropriate personalized treatments according to the characteristics of each PD

patient. This clinical decision support system integrates and combines patient biomarkers (such as genetic and

neurological markers), motor markers (based on the computerised monitoring of activity and movement) and

the digitization of clinical practice guidelines to optimise the diagnosis and treatment processes of patients

with PD and to improve their quality of life.

1 INTRODUCTION

Parkinson's disease (PD) is the second most common

neurodegenerative disease (Dorsey et al., 2007). One

of the limitations of its treatment is the appearance of

unwanted effects like motor fluctuations, dyskinesias

and other motor disorders. Furthermore, the way in

which patients respond to treatment is not always the

same, resulting in a great disparity of responses and a

high degree of variability in clinical progression

(Jankovic, 2005).

In medical practice, clinicians often still use a

«trial and error» approach to optimizing their patients'

treatments (e.g., increasing or reducing doses,

deciding whether to change one drug or combine it

with another). This approach typically involves high

socio-economic and clinical expenses (Olesen et al.,

2012), a problem compounded by the increase in the

prevalence of PD as the population ages. There is

therefore an urgent need to develop new paradigms in

the PD patient care model.

Another constraint of current clinical practice is

the limited monitoring of patients with PD. Clinical

examinations and follow-ups are limited to short

visits excessively spaced in time. The adoption of

new activity and movement monitoring

methodologies, and the use of new computing,

storage, and data analysis techniques would allow

continuous monitoring and make it possible to detect

symptoms of great value for optimising PD

treatments.

There are also other limitations that currently

make such systems difficult to implement, such as the

lack of digitization of Clinical Practice Guidelines for

treating Parkinson's disease and for integrating those

guidelines with other sources of clinical information

(Espay et al., 2016).

Over the last ten years, basic clinical research has

contributed a considerable amount of Clinical

Knowledge based on highly effective biological

markers capable of accurately predicting the

evolution of PD and patients’ response to treatment

(Poewe et al., 2017). However, these markers are

applied manually by healthcare professionals,

causing variability in clinical practice.

This paper presents the objectives of the GIMO-

PD project (Mobile health solution based on Genetic

profile, Image analysis and permanent Monitoring of

symptoms for the personalized management of

Parkinson’s Disease patients). GIMO-PD will

propose a technological solution for improving

clinical decision-making on the allocation of

Enamorado-Díaz, E. and García-García, J.

GIMO-PD: Towards a Health Technology Proposal for Improving the Personalized Treatment of Parkinson’s Disease Patients.

DOI: 10.5220/0010651900003058

In Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST 2021), pages 267-274

ISBN: 978-989-758-536-4; ISSN: 2184-3252

267

appropriate, personalized treatments adapted to the

characteristics of each PD patient. This clinical

decision support system will integrate and combine:

1. Patient biomarkers. A personalized medicine

model is applied to PD, proposing the

integration of information from different

biological biomarkers, both genetic (drug-

gene interaction and genetic risk factors) and

neuroimaging (SPECT with the [123I] FP-CIT

technique).

2. Motor markers based on the computerised

monitoring of activity and movement. The

GIMO-PD platform will include technologies

based on wearable devices for detecting

symptoms and motor disorders.

3. Digitization of Clinical Practice Guidelines

(CPGs)

associated with the treatment of PD.

For this purpose, our objective is to apply the

Model-Driven Engineering (MDE) paradigm

(Schmidt, 2006) to systematize and automate

the management, definition, and execution of

clinical guidelines.

The integration of these technologies for

monitoring, diagnosing, and treating patients with

Parkinson's disease will make it possible to optimise

their diagnostic and treatment processes and thus

improve their quality of life.

In this regard, GIMO-PD presents a technological

proposal for integrating clinical information obtained

from multiple sources (such as genetic analysis,

molecular markers, neuroimaging, motor monitoring

and clinical practice recommendations).

GIMO-PD also aims to further existing

knowledge about the etiology of PD and introduce

standard mechanisms (supported by Information and

Communications Technologies) to diagnose and treat

patients with this disease. These mechanisms can help

define digitized clinical practice processes to establish

personalized medical treatments for each patient.

Another objective of GIMO-PD is to reduce the costs

incurred through the ineffective use of drugs and

hospital visits, expenditure which has a great sanitary

and socio-economic impact. From a technological

point of view, the project’s application of the MDE

paradigm to the healthcare context is an important

innovation in terms of its potential results (reduction of

errors and costs, increase in quality, etc.).

This paper is organized as follows. Section 2

presents some related works on the digitization of

Clinical Practice Guidelines. To describe the

background, we divided Section 3 into two sub-

CPGs are sets of systematic statements which provide

health professionals and patients with a basis on which to take

sections: Section 3.1 details the model driven

engineering paradigm, and Section 3.2 briefly

describes the project’s genetic background. Section 4

explains the GIMO-PD platform, including its 5

functional modules: the clinical guide management

module (Section 4.1); the decision-making module

(Section 4.2); the motor control module (Section 4.3);

the neuroimaging module (Section 4.4); and the

genetic analysis module (Section 4.5). Finally,

Section 5 presents the main conclusions and sets out

some strategic considerations regarding future lines

of research.

2 RELATED WORKS

This section describes some works related to the

digitization of Clinical Practice Guidelines to

improve the treatment of patients with specific

diseases. No proposals specifically designed to

improve the treatment of patients with PD could be

found, but the works described here are nevertheless

interesting as they provide an idea of the current state

of the art in the digitization of Clinical Practice

Guidelines in general.

Laleci et al. (Laleci Erturkmen et al., 2019)

presented and implemented a semi-automatic care

plan management tool integrated with clinical

decision support services. The tool seamlessly

accessed and assessed patients’ Electronic Health

Records (EHRs) to suggest personalised

recommendations for individually customized care

plans.

Jimenez-Molina et al. (Jimenez-Molina et al.,

2018) proposed a framework for the development of

chronic disease support systems and applications as a

solution to shortcomings in the integration of

applications and existing healthcare systems, the

reusability of technical knowledge in the creation of

new systems and the use of gathered data in the

generation of new knowledge.

El-Sappagh et al. (El-Sappagh et al., 2018)

proposed a semantically fuzzy, rule-based system

framework for diabetes diagnosis using multiple

aspects of knowledge—fuzzy inference, an

ontological reasoning process, and a fuzzy analytical

hierarchy process—to provide a more intuitive,

dynamic, accurate design.

Aborokabah et al. (Aborokbah et al., 2018)

proposed a context-aware clinical decision support

model for heart failure risk prediction. The proposed

decisions about the healthcare responses most appropriate in

specific clinical circumstances (Field & Lohr, 1990).

WEBIST 2021 - 17th International Conference on Web Information Systems and Technologies

268

model was evaluated using a dataset of potential heart

failure patients with metrics including prediction

accuracy, sensitivity, specificity and receiving

operating characteristic.

Afzal et al. (Afzal et al., 2017) proposed an

automated knowledge acquisition methodology with

a comprehensible knowledge model for cancer

treatment based directly on information in existing

cancer treatment documents. This methodology is

supported by software tools and is helpful in finding

hidden knowledge in clinical documents. It is also

generalizable to other domains as a means of assisting

clinicians in decision making and education.

Pombo et al. (Pombo et al., 2016) presented a

Clinical Decision Support System (CDSS) based on

data imputation principles for pain evaluation. The

system produced tailored alarms, reports and clinical

guidance based on collected patient-reported data.

After analyzing previous related works, we can

identify some specific contributions of our paper: (1)

The application and validation of the technological

solution in a poorly treated disease through the

digitization of clinical guidelines (that is, PD); (2)

Previous works are focused on the follow-up of

patients and their treatments, while GIMO-PD

integrates the use of neuroimaging techniques, mobile

technologies for the detection of motor symptoms,

genetic and pharmacological information; (3) The use

of MDE-based mechanisms to systematize the

technological development of the platform.

3 BACKGROUNDS

3.1 Model Driven Paradigm

In the context of GIMO-PD, the objective of the

model-driven engineering (MDE) paradigm was to

improve the automation and digitization of the

clinical practice guidelines associated with the

treatment of Parkinson's disease.

The MDE paradigm (Schmidt, 2006) emerged in

response to the complexity of software systems,

making it possible to express the concepts of the

problem domain in an effective manner. The paradigm

defines models and establishes transformation rules

based on those models to generate new, more

technologically oriented models. These mechanisms

are intended to increase automation during the

software development life cycle.

To implement this new paradigm in real projects,

standardization was necessary. OMG standardized the

use of the MDE paradigm using Model Driven

Architecture (MDA, 2003). MDA defines

transformation rules between models until source code

or another model with the characteristics of a particular

technology are obtained. It is based on the following

four types of levels or models as shown in Figure 1:

- ICM (Independent Computing Model). This is

considered the highest and the most abstract

level of business model.

- PIM (Platform Independent Model). This

represents the business process and system

structure model. These models are not related

to any one specific technology.

- PSM (Platform Specific Model). This is

specifically related to the platform where the

system is to be implemented: for example,

operating systems, programming languages or

middleware platforms.

- Source code. This refers to the appropriate

coding and implementation of the system.

Figure 1: Model-Driven Engineering.

MDE has big advantages for software

development. It provides specific, relevant results in

software projects. The systematic generation of

models based on previous models assures traceability

through levels and can potentially cut down

development time. If suitable tools were defined, this

process could also even be automatic.

3.2 Genetic Background

GIMO-PD proposes the joint integration of

information from different biological biomarkers

GIMO-PD: Towards a Health Technology Proposal for Improving the Personalized Treatment of Parkinson’s Disease Patients

269

(genetic and neuroimaging) with movement analysis

and the digitalization of clinical guidelines, making it

possible to apply personalized medicine models to PD

patients.

Genetics can impact patient profiles through drug-

gene factors and genetic risk factors:

1) Drug-gene factors. Genes can modulate a

person’s response to drugs, and the study of this

interaction is called pharmacogenomics (Grant,

2001). Studies have been carried out in recent

years into the benefits of certain drugs in the

treatment of PD. Some, for example, focussed

on correlating the clinical responses of patients

who had received different doses of the drug

levodopa with their activity (Bialecka et al.,

2008; Cheshire et al., 2013). These studies also

identified a relationship between clinical

responses and the SLC6A3 gene that encodes

the dopamine transporter. That same gene is

related to levadopa absorption. A

comprehensive review of pharmacogenetics

pertaining to PD can be found in the literature

(Kurzawski et al., 2015; Politi et al., 2018).

2) Genetic risk factors. Genes can also predispose

patients to certain motor and non-motor

symptoms. To adapt the patient's treatment and

improve her quality of life, these genetic factors

must therefore be taken into account. The

genetic risk factors associated with symptoms of

Parkinson's disease have been identified in

different research papers. They include

increased risk of cognitive impairment (Foltynie

et al., 2009), risk of visual hallucinations

(Redenšek et al., 2019), and risk of severe

movement control disorders (Napier et al., 2015;

Redenšek et al., 2019).

4 GIMO-PD ARCHITECTURE

The GIMO-PD platform will have five functional

modules with which to achieve its objectives (see

Section 1):

1. Clinical Practice Guidelines management

model. This covers the definition, execution

and monitoring of clinical guidelines, and

their integration with external systems.

2. Decision-making module. This module

considers combination of the information

collected from the neuroimaging, genetic

analysis, and motor control modules to

establish a clinical recommendation.

3. Motor control module. This includes

technologies based on wearable devices for

detecting symptoms and motor disorders.

4. Neuroimaging module. This analyses DAT-

SPECT images and provides quantitative,

objective information about the patient's

condition.

5. Genetic analysis module. This proposes the

integration of information from different

biological biomarkers.

The above modules will be integrated into a

software platform based on Cloud-computing to host,

exchange and process all genetic, neuroimaging and

motor monitoring information. Figure 2 shows the

architecture of the GIMO-PD platform.

The core of this platform is the clinical guidelines

management module, which will provide

recommendations to be followed by health

professionals. These recommendations will be

established after analysing the genetic, neurological,

and motor variables of the patient. The analysis will

be carried out in conjunction with the Clinical

Decision Support System (CDSS). It will include

machine learning algorithms trained with

experimental clinical data.

The project’s genetic and neurological analysis

will include a chemical component to determine

which genetic variants described in the literature

influence the evolution and treatment of the disease.

The genetic variables identified are then included in

the CDSS. The patient’s pathological situation will

also be evaluated by neuroimage analysis.

With regard to the management of motor and non-

motor markers, the GIMO-PD platform will include

mechanisms to monitor these aspects. On the one

hand, it will include the design and development of a

wearable bracelet with sensors to monitor the

patient's motor function and detect motor

complications. On the other, non-motor markers will

be periodically checked with short scales and

validated using the patient's smartphone.

The objective of each module is explained in

detail below.

4.1 Clinical Practice Guidelines

Management Model

This GIMO-PD module will run the CPG

management life cycle. This includes the definition,

execution and monitoring of clinical guidelines, and

their integration with external systems. To achieve

these goals, the module has three main sub-modules:

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Figure 2: GIMO-PD: towards to model-driven software architecture.

1. Definition sub-module. This will contain

model-driven mechanisms (based on MDE

principles; see Section 3.1) for defining static

CPG models. For this purpose, a set of

domain-specific metamodels and languages

will have been designed to describe activity

flows, clinical recommendations, clinical

variables, decision rules, etc. All these aspects

are essential to define any clinical guideline.

This sub-module will also include a set of

transformation rules to obtain an executable

version from a previously defined static

model. The executable version can then be

deployed in the execution sub-module.

2. Execution and integration sub-module. This

sub-module will comprise a process engine for

executing the static CPD models defined in the

previous sub-module. It could be considered

the core of GIMO-PD because it will be

responsible for orchestrating communications

with the other modules on the GIMO-PD

platform. It will also provide the user entry

point to the platform.

3. Monitoring sub-module. This sub-module will

monitor the healthcare professional’s

performance and task flow. For this purpose,

the platform will define key performance

indicators related to running instances,

average running time, etc.

4.2 Decision-Making Module (CDSS

Decision Rule Engine)

Clinical practice guidelines provide protocols for

establishing quality diagnoses and treatments.

Although these guidelines are usually quite broad,

however, they are descriptions that do not fully cover

all the casuistry associated with evaluating clinical

variables to make an optimal decision. This module

will complement the clinical guideline execution

module by providing additional functions for

improving evaluation and decision-making.

In this module, the GIMO-PD platform will

include a Clinical Decision Support System (CDSS).

CDSSs are decision systems that provide specific

recommendations based on the knowledge model that

feeds them (Liu et al., 2006).

The CDSS collects information from the

neuroimaging, genetic analysis, and motor control

modules. Once the combination of this information

has been considered, a clinical recommendation can

be established.

GIMO-PD’s clinical decision module will be

designed and developed considering two design

techniques: (1) Rule Based Reasoning (RBR) (Shoaip

et al., 2019), which establishes a set of clinical rules

considering specific clinical criteria and

recommendations previously defined in CPGs ; and

(2) Case Based Reasoning (CBR) (Li et al., 2018),

which will be capable of automatically generating

clinical rules after analysing prior knowledge stored,

for example, in knowledge bases.

The module’s hybrid design is due to the intrinsic

characteristics of the GIMO-PD project, in which

most of the on-park data comes from previously

diagnosed and treated cases. Diagnosis and treatment

of Parkinson's disease requires the analysis both of

patient information and of historical information

(based on previously populated knowledge bases). In

this regard, the application of CBR techniques makes

GIMO-PD: Towards a Health Technology Proposal for Improving the Personalized Treatment of Parkinson’s Disease Patients

271

it possible to automatically generate rules, thus

complementing the rules defined by the clinical

practice guide using RBR techniques.

4.3 Motor Control Module

Tremor is a primary symptom and one of the most

disabling general symptoms of Parkinson's disease

(Ruonala et al., 2013). In fact, it is one of the aspects

most evaluated by health professionals to determine

the progression of the disease. The monitoring and

evaluation of motor symptoms in PD is mainly based

on historical information and neurological

examinations (usually biannual). These methods have

many drawbacks: (1) the data may be subjective,

because it depends on the patient's memory and

perception of his own symptoms (and his ability to

identify symptoms and medical terminology); and (2)

the data are highly dependent on the experience of the

healthcare professional.

Many research articles have analysed

parkinsonian gait to try to detect movement disorders

(Cifuentes et al., 2010). However, there is a

significant handicap. These movements are disturbed

by other factors (lack of balance, trunk bent forward,

stiffness, tremor, etc.) which are not isolated and

usually cause a high rate of false positives and

negatives. This makes it difficult to determine exactly

what movement the patient is making at any given

moment. It is also important to mention that these

motor symptoms depend on each patient and their

degree of illness. The study of a PD patient’s gait is

therefore a field that still requires protracted research

if useful results are to be obtained.

In this context, GIMO-PD presents a

technological proposal for identifying motor

disorders in patients with Parkinson's disease. The

platform will include technologies based on

wearable devices for the detection of symptoms and

motor disorders. The wearable nature of these

devices facilitates their continuous, non-invasive

use to capture kinematic information through

inertial sensors, and also through lifelong learning

techniques.

Single-photon emission computed tomography with

ioflupane (123I), also known as 123I-FP-CIT SPECT, is the

most widely used complementary test in this type of

diagnosis. (Olivares Romero & Arjona Padillo, 2013).

The dopamine transporter (DAT) is responsible for

clearing dopamine from the synaptic cleft after its release.

4.4 Neuroimaging Module

Image quantification techniques are common in

medical research, but their complexity has historically

prevented their effective use in clinical practice.

As a solution to this problem, GIMO-PD proposes

integrating neuroimage analysis and clinical practice.

For this purpose, this module will include

functionalities and machine learning algorithms for

analysing SPECT (single photon emission computed

tomography) images with the [123I] FP-CIT

technique. This technique makes it possible to

visualize DAT

(Dopamine Active Transporter)

activity and detect presynaptic dopaminergic deficit.

This is useful in the early diagnosis of Parkinson's

disease and also in differentiating the disease from

other nondegenerative parkinsonian disorders.

DAT-SPECT image analysis provides

quantitative, objective information on the patient's

condition, which can then easily be compared with

historical patient data and clinical knowledge bases.

By comparing data in this way, the clinical specialist

can specify the patient's situation more accurately.

4.5 Genetic Analysis Module

In GIMO-PD, a personalized medicine model will be

applied to PD, proposing the integration of

information from different biological biomarkers,

both genetic and neuroimaging. As explained earlier

in Section 3.2, genetics play a key role in defining

patient profiles in two areas: drug-gene interaction

and genetic risk factors.

GIMO-PD will offer a catalogue of genetic

biomarkers identified in the literature as being relevant

to patients’ responses to treatment (pharmacogenetic

interaction), the evolution of Parkinson's disease, and

the appearance of certain symptoms (genetic risk

factors). These biomarkers are useful for prognosis and

as predictors of response to treatment.

5 CONCLUSIONS AND FUTURE

RESEARCH

Parkinson's disease is the second most common

neurodegenerative disease, and its pharmacological

Imaging DAT availability measures dopamine terminal

functionality and provides a method for detecting states of

striatal dopamine deficiency in idiopathic Parkinson’s

disease and atypical neurodegenerative parkinsonian

disorders such as multiple system atrophy and progressive

supranuclear palsy (Brooks, 2010).

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treatment usually has undesired effects (motor

fluctuations, dyskinesias and other motor alterations).

This paper details a software platform for

improving clinical decision-making and providing

individual Parkinson’s disease patients with the

treatment most appropriate to their own personal

characteristics. This platform is going to be named

GIMO-PD: a project for applying a personalized

medicine model to Parkinson's disease. To achieve

this objective, GIMO-PD will integrate information

from different data sources: biological biomarkers

(both genetic and image), analysis of movement

disorders observed while monitoring patients in real

time, and clinical information from clinical practice

guidelines for the treatment of Parkinson's disease.

Regarding future lines of work, this project can be

expanded in several ways. One area of study would

be to look at new functionalities of the GIMO-PD

platform and the monitoring of more parameters

when analysing patient movement disorders. The

project might also be extended to address other

diseases, taking into account i) different parameters

when monitoring patients and ii) the

recommendations of different clinical guidelines

specific to other diseases.

ACKNOWLEDGEMENTS

This research is framed in the GIMO-PD (RTC2019-

007150-1) project of the Spanish Ministry of

Economy and Competitiveness, which is financed by

European funds. In addition, this article is funded by:

the NICO project (PID2019-105455GB-C31) of the

Spanish Ministry of Economy and Competitiveness:

the TRoPA (Early Testing in Medical Robotics

Process Automation) project (CEI-12) of the

Andalusian Ministry of Economy, knowledge,

companies and university; and Aid for the

Consolidation of Groups of the Junta de Andalucía

(2021-TIC021). Finally, GIMO-PD was carried out

by researchers from the University of Seville, from

the FISEVI foundation, and from the Madrija and

Soltel companies.

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