=Paper=
{{Paper
|id=Vol-1664/w17
|storemode=property
|title=Towards the Adoption of Agent-Based Modelling
and Simulation in Mobile Health Systems
for the Self-Management of Chronic Diseases
|pdfUrl=https://ceur-ws.org/Vol-1664/w17.pdf
|volume=Vol-1664
|authors=Sara Montagna,Andrea Omicini,Francesco Degli Angeli,Michele Donati
|dblpUrl=https://dblp.org/rec/conf/woa/MontagnaOAD16
}}
==Towards the Adoption of Agent-Based Modelling
and Simulation in Mobile Health Systems
for the Self-Management of Chronic Diseases==
https://ceur-ws.org/Vol-1664/w17.pdf
Towards the Adoption of Agent-Based Modelling
and Simulation in Mobile Health Systems
for the Self-Management of Chronic Diseases
Sara Montagna Andrea Omicini Francesco Degli Angeli Michele Donati
DISI–Università di Bologna
via Sacchi 3, 47521 Cesena, Italy
Email: {sara.montagna, andrea.omicini}@unibo.it
Abstract—The impact of mobile technologies on healthcare is as self-management interventions [5], [6]. M-health has a big
particularly evident in the case of self-management of chronic potential in this context, mainly because mobile devices are
diseases, where they can decrease spending and improve the commonly equipped with hardware and software technologies
patient quality of life. In this position paper we propose the
adoption of agent-based modelling and simulation techniques as for real-time data acquisition, storage, sharing, and elabora-
built-in tools to dynamically monitor patient health state and tion, thus enabling [7]:
provide recommendations for self-management. To demonstrate
• healthcare professionals to be continuously updated on
the feasibility of our proposal we focus on Type 1 Diabetes
Mellitus as our case study, and provide some preliminary the patients health by receiving data such as vital signs
simulation results. measures decreasing the occasions for patients to travel
to health facilities;
I. I NTRODUCTION • patients to be supported in daily decisions by instructions
delivered by Personal Digital Assistant (PDA) applica-
The introduction of information and communication tech-
tions that are based on the elaboration of these data.
nologies into healthcare systems is revolutionising medicine.
In particular, the emergence of mobile things connected to the In this paper we focus on candidate algorithms and computa-
Internet even while moving around – the Internet of Mobile tional technologies for the analysis and elaboration of patient
Things (IoMT) [1] – is opening new frontiers in healthcare. data. While literature typically refers to technologies like big
This field is often called mobile Health [2], [3]—m-Health, in data, machine learning, and decision support systems [8], we
short: humans are typically equipped with wearable devices here advocate the adoption of modelling and simulation tech-
(such as smart watches, wrist bands, smartphones, etc.) that niques, and agent-based modelling and simulation (ABMS) in
measure, store, transmit, and possibly also elaborate vital particular. We claim that the possibility of modelling how a
parameters of the person, providing specific information about complex system – such as the human body – evolves over time,
individual’s health state. M-Health is expected to have a big is crucial for providing predictions of the health conditions
impact in healthcare, since it facilitates clinical data collection, of the patient in the near and far future. Such predictions
access, sharing, and elaboration. are useful input for identifying a set of corrective actions the
A specific issue in m-Health is the self-management of patient should take for a better health and to prevent disease
chronic diseases. Chronic diseases, such as diabetes, respi- exacerbation. Adopting ABMS as a tool associated to mobile
ratory illnesses, and cardiovascular diseases, constitute the devices could allow patients to monitor their own health state,
most common and costly health problems of our society: and provide feedbacks driving them in their daily life.
in 2013, the Pan American Health Organization (PAHO) in The main objective of this work is to demonstrate the
collaboration with the World Health Organization (WHO), feasibility of our proposal. To this end, we present an agent-
estimated chronic diseases to consume a huge percentage of based model for self-management of Type 1 Diabetes Mellitus.
total health-care spending [4]. Also, they are an important Diabetes is a chronic disease that affects the physiological
source of disability, as they strongly impact the quality of mechanisms controlling glucose concentration in the blood
life of patients with ad-hoc dieting, sport activity, regular plasma. It occurs when the normal insulin-glucose-glucagon
treatments, social and work life adjustments, and significant regulatory mechanism is affected, because either the pancreas
emotional consequences. does not release insulin (Type 1) or body cells do not properly
To reduce the cost of health care systems and improve use insulin to uptake glucose in the blood plasma (type 2) [9].
the quality of the patient daily life, an increasing number of In the following we first motivate our work, analysing the
interventions have been developed in the last years to transfer role of IoMT for disease self-management, then present and
aspects of chronic illness control from the caregiver to the evaluate our proposal with preliminary experiments showing
patients themselves. These are characterised by substantial the data-driven modelling of the dynamics of glucose, insulin,
responsibility taken by patients, and are commonly referred to and glucagon in a healthy person and Type 1 DM patient.
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II. I O MT AND M -H EALTH In this paper we propose the adoption of modelling and
IoMT holds promise to pave the way for a new era in simulation tools that are gaining acceptance in medicine as
medicine, changing the way health-services will be provided a valuable support for decision making, since they provide
[3], [2], [10]: the adoption of mobile devices, equipped with both, short-term and long-term clinical predictions of patient
sensors and internet connectivity, plays a key role enabling health [15]. Such predictions provide information for making
healthcare services to anyone, anywhere, and anytime, as from the most informed choices between available treatments and
the definition of [11]: “Pervasive healthcare is the conceptual interventions. In particular, among the different simulation
system of providing healthcare to anyone, at anytime, and approaches, we here propose the adoption of the agent-based
anywhere by removing restraints of time and location while model (ABM).
increasing both the coverage and the quality of healthcare”.
A. Agent-Based Model
A number of improvements are expected in healthcare by
the introduction of IoMT: In the literature, agent-based systems, and MAS in par-
• increasing accessibility of health-services, by guarantee- ticular, are considered an effective paradigm for modelling,
ing a wider coverage; most in fact own a mobile device understanding, and engineering complex systems [16], and
with which they can access diverse services devoted to biological systems in particular [17], since they provide a basic
improving their health, from the simplest cases of SMS set of high-level abstractions that make it possible to directly
reminders with dates of appointments, SMS or emails capture and represent main aspects of complex systems, such
for communications from the health professionals, SMS as interaction, multiplicity and decentralisation of control,
or emails with medical reports, to the more complex openness, and dynamism [18], [19], [20], [21].
cases of data acquisition (via sensors), transmission, and In the pioneering work of Bonabeau [22], an ABM describes
elaboration; the system from the perspective of its constituent units. More-
• decreasing the cost of healthcare, since people can avoid over he states that:
to frequently move towards healthcare facilities and, care- The benefits of ABM over other modeling tech-
givers can be automatically updated in case of unexpected niques can be captured in three statements: (i) ABM
changes; captures emergent phenomena; (ii) ABM provides
• supporting chronic disease self-care, by enabling data col- a natural description of a system; and (iii) ABM
lection, sharing and disease tracking, to support diagnosis is flexible. Emergent phenomena result from the
and personalised treatment —well turn to in more detail interactions of individual entities. By definition, they
on that in the following; cannot be reduced to the systems parts: the whole is
• providing suitable tools for timely managing healthcare more than the sum of its parts because of the inter-
in emergencies. actions between the parts. An emergent phenomenon
can have properties that are decoupled from the
III. S ELF -M ANAGEMENT OF C HRONIC D ISEASES
properties of the part. [...] ABM is, by its very
Self-management of chronic diseases is defined as the nature, the canonical approach to modeling emergent
active involvement of patients in their treatment with day-to- phenomena: in ABM, one models and simulates the
day decisions about different actions to be taken: control of behavior of the systems constituent units (the agents)
symptoms, take medicines, make lifestyle changes, undertake and their interactions, capturing emergence from the
preventive actions. It is thus characterised by an extensive bottom up when the simulation is run. [22]
responsibility that the patients need to take on [6], [12]. Since
This is why we consider ABM as a suitable approach for
the expected outcome of the patients self-management is to
modelling the complex dynamics of disease physiopathology.
maintain a satisfactory quality of life, various initiatives are
devoted to identifying how to support patients in their daily B. Self-Management System Model
decisions, without leaving them alone, i.e., guaranteeing health
We model the whole healthcare system as an agent-based
professionals intervention by automatically issuing emergency
system composed of two levels, as shown in Figure 1:
alarms.
IoMT technologies can significantly improve disease self- 1) a high-level model that represents patients and their
management [13], [14]. However, notwithstanding the explo- interactions with a tool supporting diabetes self-
sive improvement of technology, which is crucial in data management, such as a PDA;
acquisition by built-in ad hoc sensors, data sharing, and patient 2) a disease model that represents the disease physiopathol-
interactions, there is a considerable lack of well-established ogy and predicts the state of the patient in the near and
models, theories, and algorithms capable of effectively sup- far future.
porting disease self-management in m-Health. Most of the The high-level model reproduces the behaviour of patients,
available literature proposes algorithms and theories from and how they respond to feedback received through their per-
computer science for elaborating data, such as complex event sonal devices. We assume that PDAs acquire (1) information
processing, data mining tools, big data technologies, machine from the individual about the composition of their meals, (2)
learning, and decision support systems [8]. information about individual physical activity from embedded
101
released into the blood. The blood flow ensures that glucose
is delivered to all cells of our body. Finally, glucose diffuses
from the blood into cells where it is used as an energy source
via the aerobic respiration, or where it is stored as glycogen,
a polysaccharide of glucose composed of varying numbers
of glucose units, depending on the cell type (for instance,
glycogen of muscle cells is composed of around 6.000 units
of glucose, while liver cells require 30.000 units of glucose to
create a glycogen molecule).
Plasma glucose levels are normally maintained within a nar-
row range (70−100 mg/dl) through the combined antagonistic
action of the two pancreatic hormones, insulin and glucagon,
which enable the uptake and release of glucose from the blood
into cells and vice-versa:
Insulin — It is a hypoglycaemic hormone, i.e., it is re-
sponsible for enabling the uptake of glucose, mainly
into fat and muscle cells, thus reducing the level of
glucose in the blood. It is produced by pancreatic
β-cells as a function of glucose concentration in the
blood (formally called glycaemia): a basal secretion
of insulin from β-cells is always observed, ensuring
Figure 1. Two levels model of the self-management system the availability of glucose at all times; instead, sinks
of postprandial secretions are observed, i.e., when
the blood glucose levels are high. Secretions finally
sensors, such as accelerometers, and finally (3) information stop in case of hypoglycaemia.
about glycemia values, gathered wirelessly from a wearable Glucagon — It is a hyperglycaemic hormone that promotes
device. These data are used as input for the chronic disease the release of glucose from the liver cells into the
model. In the following we demonstrate the feasibility of our blood. It is produced by pancreatic α-cells when
proposal by adopting Type 1 Diabetes Mellitus as our case glycaemia is low, normally in fast periods, such as
study. during the night. Therefore, α-cells behave in the
IV. BACKGROUND ON T YPE 1 D IABETES M ELLITUS opposite way than β-cells: they have high secretion
P HYSIOPATHOLOGY rates when the blood glucose concentrations are low,
and low secretion rates when the glucose levels are
Diabetes mellitus, diabetes in short, is a chronic metabolic high [9].
disorder characterised by an excessive amount of sugar cir-
culating in the blood plasma, i.e., hyperglycaemia, for a
prolonged period. Diabetes is strictly related to insulin defects.
Insulin is a hormone produced by the β-cells of the pancreas;
it has a crucial role in the absorption of glucose in two-
third of body cells, but mainly in fat, liver, and muscle
cells, where glucose is a necessary source of energy for the
cells to perform their activities. Diabetes is due to either an
autoimmune process, where pancreatic β-cells do not produce
enough insulin (Type 1 diabetes mellitus, Type 1 DM in short),
or else the cells of the body develop a sort of “resistance”
to insulin action, thus not responding properly to the insulin
produced (Type 2 DM). In the following, we first describe the
metabolic system, then how the physiological functions are
affected by the lack of insulin, as in Type 1 DM.
A. Metabolic System
Every cell in our body needs fuel in order to fulfil its specific
function. Glucose is the main source of such energy. It is
obtained firstly from the food we eat (and drink, possibly)
via the intestinal absorption, which ensures food to be broken
down into the monosaccharide form of glucose that is then Figure 2. Metabolic system c 2001 Benjamin Cummings
102
The metabolic processes and the normal regulation of blood system (described in the following) is performed. From the
glucose levels are depicted in Figure 2. simulation results, feedbacks are provided to the patient, who
could then modify his/her behaviour accordingly, if needed.
B. Disease Physiopathology
Type 1 DM is characterised by the loss of β-cells. It is A. Type 1 DM Model
mainly caused by an autoimmune process, where T-cells of We model the metabolic system as a set of interacting
the immune systems attack and destroy β-cells, thus leading agents, where each agent is a set of cells conducting the
to insulin deficiency. The insulin-dependent uptake of glucose same activities. In particular we include the main organs (or
in cells is no more possible, and the main consequences are a set of cells) involved in metabolic processes—as shown in
high level of blood glucose and low level of fuel for body cells. Figure 1. Agents then interact via an interaction medium,
Typical diabetes complications include cardiovascular disease, the environment, that models the bloodstream where agents
stroke, chronic kidney failure, foot ulcers, and damage to the release and retrieve molecules.
eyes. However, prevention and treatment are possible: Type 1 We consider the following agents:
DM must be firstly managed with insulin injections; however,
• intestine-cells agent — it absorbs and breaks down food
medical evidence shows that patients affected by Type 1 DM
benefit from a healthy diet, sport activity, maintaining a normal substances, releasing the glucose derived by digestion in
body weight, strict controlling glycaemia, and avoiding use of the bloodstream
• β-cells agent — if glucose level in the blood exceeds
tobacco. For this reason, Type 1 DM is subjected to several
initiatives for supporting the self-management of the disease. the threshold of 75 mg/dl it secretes insulin in the
bloodstream; this process ends as soon as glycaemia get
V. T YPE 1 DM SELF - MANAGEMENT back into physiologic values
• α-cells agent — if glucose level in the blood falls below
Diabetes is nowadays one of the most common and known
chronic disease. Data from the World Health Organization the threshold of 70 mg/dl it secretes glucagon in the
(WHO) [23] refers that 1.5 million of deaths are attributed bloodstream; this process ends as soon as glycaemia get
to diabetes each year, and that 9% of the adults population is back into physiologic values
• liver-cells agent — if glucagon is available in the blood,
affected by diabetes. For this reason, the self-management of
diabetes is nowadays supported by a wide range of systems it begins the process of glycogenolysis, breaking down
exploiting mobile health technologies, mainly aimed at guiding glycogen molecules and realising glucose in the blood;
patients towards healthy lifestyle changes [10], [24], [25]. The plus, if glucose level in the blood exceeds the threshold of
main goal is to find solutions for identifying personalised 75 mg/dl, it uptakes glucose from the blood and begins
therapies and lifestyle suggestions for patients to improve their the process of glycogen synthesis
• muscle-cells agent — if insulin is available free in the
outcome.
In the following we present an ABM of Type 1 DM self- bloodstream, it absorbs glucose and begins the process
management. The whole model architecture is depicted in of glycogen synthesis; plus, during physical exercises, it
Figure 3: from an initial state that reproduces the health consumes – according to the type of activity – a balanced
condition of a patient, a low level simulation of the metabolic quantity of glycogen
• brain-cells agent — it continuously absorbs glucose from
the blood
The model of Type 1 DM is hence obtained by simply stopping
the β-cells agent.
B. Early Simulation Results
The model is implemented on top of the MASON infrastruc-
ture [26]. We here presents the results obtained with the low-
level model, showing that it correctly reproduces the dynamic
of the metabolic system in both the cases of healthy and Type 1
DM patient. We leave to future work the implementation and
verification of the whole self-management system.
Figure 4 shows the dynamic over 3 days of glucose, insulin,
and glucagon concentration in blood in a healthy patient.
There, the glycaemia value varies during the day following
meals (breakfast, morning snack, lunch, afternoon snack, and
dinner). Insulin and glucagon follow these dynamics: (1)
insulin increases as a response of glucose increase, while (2)
glucagon is secreted mainly during the night when patient does
Figure 3. Simulation Architecture not eat.
103
Glycaemia Plot Glycaemia Plot
Glycaemia Glycaemia
110 425
400
100 375
350
90
325
80 300
275
70
250
mg/dl
mg/dl
60 225
200
50
175
40 150
125
30
100
20 75
50
10
25
0 0
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500
Time (min) Time (min)
Insulin Plot Insulin Plot
Insulin Insulin
160
7.0
150
6.5
140
6.0
130
120 5.5
110 5.0
100 4.5
microUI/ml
microUI/ml
90 4.0
80 3.5
70
3.0
60
2.5
50
2.0
40
1.5
30
20 1.0
10 0.5
0 0.0
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500
Time (min) Time (min)
Glucagon Plot Glucagon Plot
Glucagon Glucagon
55
22.5
50
20.0
45
17.5
40
15.0
35
pg/ml
pg/ml
30 12.5
25 10.0
20
7.5
15
5.0
10
2.5
5
0 0.0
0 250 500 750 1,000 1,250 1,500 1,750 2,000 2,250 2,500 2,750 3,000 3,250 3,500 3,750 4,000 4,250 4,500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500
Time (min) Time (min)
Figure 4. Simulation results for a healthy patient Figure 5. Simulation results for a patient with Type 1 DM
Figure 5 shows the dynamic over 3 days of glucose, insulin, network and rule-based systems – that autonomously identifies
and glucagon concentration in blood in a Type 1 DM affected suggestions on the best behaviour that the patient should
patient. There, the glycaemia value is no longer under control, follow in order to contain the Type 1 DM effects.
and the patient enters soon into a hyperglycaemia state. Insulin
is no longer produced, and glucagon also is no longer secreted, VI. C ONCLUSION
since glucose concentration is over the threshold. In this position paper we explored some future directions
We plan to interpret these dynamics as predictions on in the field of self-management of chronic diseases. Given
the state of the patient in the close future, and to use this the widespread diffusion of chronic diseases, a set of specific
information as input for algorithms – such as artificial neural interventions are suggested by the health organisations, such
104
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