ABMS in m-Health Self-Management

Towards the Adoption of Agent-Based Modellingand Simulation in Mobile Health Systems

for the Self-Management of Chronic Diseases

Sara Montagna Andrea Omicini{sara.montagna, andrea.omicini}@unibo.it

Francesco Degli Angeli Michele Donati

Dipartimento di Informatica – Scienza e Ingegneria (DISI)Alma Mater Studiorum – Universita di Bologna

XVII Workshop “From Objects to Agents” (WOA 2016)Catania, Italy, 30 July 2016

Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 1 / 29

Outline

1 Motivation & Goals

2 IoMT and m-Health

3 Self-Management of Chronic Diseases

4 Background on Type 1 Diabetes Mellitus Physiopathology

5 Type 1 DM self-management


Motivation & Goals

Outline


2 IoMT and m-Health





Motivation & Goals

Context & Motivation

Context

The emergence of mobile things connected to the Internet even whilemoving around – the Internet of Mobile Things (IoMT) – is opening newfrontiers in healthcare

Motivation

big issue: self-management of chronic diseases

IoMT technologies can significantly improve disease self-management

! lack of well-established models, theories, and algorithms capable ofeffectively supporting disease self-management


Motivation & Goals

Goal

To demonstrate feasibility of our proposal by simple experiments onType 1 DM disease

Our proposal

to adopt modelling and simulation tools for supporting diseaseself-management, since

they provide both short-term and long-term clinical predictions ofpatient healthpredictions provide information for making the most informed choicesbetween available treatments and interventions

agent-based modelling and simulation (ABMS) tools in particular


IoMT and m-Health

Outline


2 IoMT and m-Health





IoMT and m-Health

IoMT: A New Era in Medicine

the introduction of ICT into healthcare systems is revolutionisingmedicine and opening new frontiers in healthcare

crucial role of mobile devices for data collection, access, sharing, andelaboration

IoMT applied to health systems changes the way health-services willbe provided

→ mobile Health (m-Health) & pervasive healthcare

[Var09]

“Pervasive healthcare is the conceptual system of providing healthcare toanyone, at anytime, and anywhere by removing restraints of time andlocation while increasing both the coverage and the quality of healthcare”.


IoMT and m-Health

Main Expected Improvements

A number of improvements in healthcare are expected to come by theintroduction of IoMT

increasing accessibility of health-services

decreasing the cost of healthcare

supporting chronic disease self-care

providing suitable tools for timely managing healthcare in emergencies


Self-Management of Chronic Diseases

Outline


2 IoMT and m-Health






Definition and Main Facts

Self-management of chronic diseases [NSM04, ENK+13]

. . . is defined as the active involvement of patients in their treatment withday-to-day decisions about different actions to be taken: control ofsymptoms, take medicines, make lifestyle changes, undertake preventiveactions

Promising approach to. . .

decrease health spending in chronic diseases

improve the patients quality of life

IoMT technologies can significantly improve disease self-management

explosion of technologies for data acquisition by built-in ad hocsensors and data sharing

! lack of well-established models, theories, and algorithms for dataelaboration



Our Proposal in Context

From the available literature. . .

. . . many algorithms and theories from computer science forelaborating data [ZNM+10]

complex event processing, data mining tools, big data technologies,machine learning, and decision support systems

. . . to our proposal

We here advocate the adoption of modelling and simulation techniques,and agent-based modelling and simulation (ABMS) in particular

to model a complex system – such as the human body

to simulate how it evolves over time

to obtain predictions of the health conditions of the patient

to identify a set of corrective actions



Why ABMS?

ABMS makes it possible to directly capture and represent main aspects ofcomplex systems—such as the human body

[Bon02]

The benefits of ABM over other modelling techniques can becaptured in three statements:

ABM captures emergent phenomena;ABM provides a natural description of a system;ABM is flexible.

Emergent phenomena result from the interactions of individualentities. By definition, they cannot be reduced to the systemsparts: the whole is more than the sum of its parts because of theinteractions between the parts. An emergent phenomenon canhave properties that are decoupled from the properties of thepart.


Background on Type 1 Diabetes Mellitus Physiopathology

Outline


2 IoMT and m-Health






Physiology of the Metabolic System

Glucose

main source of energy for cells

obtained from the food

released into the blood

delivered to all cells of our body

used as an energy source or stored as glycogen

Plasma glucose levels are normally maintained within a narrow range(70− 100 mg/dl) as a result of the antagonistic action of two pancreatichormones

insulin enables the uptake of glucose, produced by β-cells

glucagon promotes the release of glucose, produced by α-cells



Type 1 Diabetes Mellitus (DM) I

Cause & pathophysiology

loss of β-cells (autoimmune process)

the insulin-dependent uptake of glucose in cells is no more possible

→ high level of blood glucose and low level of fuel for body cells

Complications

cardiovascular disease

stroke

chronic kidney failure

foot ulcers

damage to the eyes



Type 1 Diabetes Mellitus (DM) II

Prevention & treatment. . .

. . . are possible

firstly managed with insulin injections

benefit from a healthy diet, sport activity, strict control of glycaemia,and no use of tobacco


Type 1 DM self-management

Outline


2 IoMT and m-Health





Type 1 DM self-management

Type 1 DM

Goal

To find solutions for identifying personalised therapies and lifestylesuggestions for patients to improve their outcome

Proposal

We present an ABMS of Type 1 DM self-management

from an initial state that reproduces thehealth condition of a patient. . .

. . . a low-level simulation of themetabolic system is performed

from the simulation results, feedbacksare provided to the patient


Type 1 DM self-management Type 1 DM Model

Two-Levels Model

High-level: self-management model

Patients are agents that behavesaccording to feedbacks received by theirPDAs

Low-level: disease model

Metabolic system as a set of interactingagents

each agent is a set of cellsconducting the same activities

agents interact via an interactionmedium, the environment, thatmodels the bloodstream whereagents release and retrieve molecules


Type 1 DM self-management Type 1 DM Model

Metabolic System and Type 1 DM Agents

intestine-cells agentabsorption of food substancesrelease of the glucose derived by digestion in the bloodstream

β-cells agentglucose level in the blood < 75 mg/dl → secretion of insulinthe model of Type 1 DM is obtained by simply stopping this agent

α-cells agentglucose level in the blood < 70 mg/dl → secretion of glucagon

liver-cells agentglucagon available → glycogenolysis and release of glucoseglucose level in the blood > 75 mg/dl→ uptake of glucose and glycogen synthesis

muscle-cells agentinsulin available → uptake of glucose and glycogen synthesisphysical exercises→ usage of glycogen

brain-cells agentcontinuous absorption of glucose


Type 1 DM self-management Early Simulation Results

Healthy Patient Simulation Results

The model is implemented on top of the MASON infrastructureGlycaemia Plot

Glycaemia

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

Time (min)

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Insulin PlotInsulin

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

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Glucagon PlotGlucagon

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

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dynamic of the metabolic system over 3 days in the case of healthypatient

glycaemia value varies during the day following meals→ breakfast, morning snack, lunch, afternoon snack, and dinner

insulin and glucagon follow the following dynamics

insulin increases as a response of glucose increase, whileglucagon is secreted mainly during the night when patient does not eat


Type 1 DM self-management Early Simulation Results

Type 1 DM Patient Simulation ResultsGlycaemia Plot

Glycaemia

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

Time (min)

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Insulin PlotInsulin

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

Time (min)

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Glucagon PlotGlucagon

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

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dynamic over 3 days of glucose, insulin, and glucagon concentrationin blood in a Type 1 DM affected patient.

the glycaemia value is no longer under control, and the patient enterssoon into a hyperglycaemia state

insulin is no longer produced

glucagon also is no longer secreted, since glucose concentration isover the threshold


Conclusion & Future Work

Conclusion & Future Work

Conclusion

we explored some future directions in the field of self-management ofchronic diseases

ABMS as a built-in tool – within a IoMT infrastructure

to automatically characterise health stateto provide feedbacks for daily life

preliminar experiments on Type 1 DM

Future work

implementation and verification of the whole self-management system

move to other diseases

work with real patients and data


Outline


2 IoMT and m-Health



5 Type 1 DM self-managementType 1 DM ModelEarly Simulation Results


References

References I

Eric Bonabeau.Agent-based modeling: Methods and techniques for simulating human systems.Proceedings of the National Academy of Sciences of the United States of America, 99(s.3):7280–7287, May 2002.

Arianne Elissen, Ellen Nolte, Cecile Knai, Matthias Brunn, Karine Chevreul, AnnalijnConklin, Isabelle Durand-Zaleski, Antje Erler, Maria Flamm, Anne Frølich, Birgit Fullerton,Ramune Jacobsen, Zuleika Saz-Parkinson, Antonio Sarria-Santamera, Andreas Sonnichsen,and Hubertus Vrijhoef.Is Europe putting theory into practice? A qualitative study of the level of self-managementsupport in chronic care management approaches.BMC Health Services Research, 13(1):1–9, 2013.

Stanton Newman, Liz Steed, and Kathleen Mulligan.Self-management interventions for chronic illness.The Lancet, 364(9444):1523 – 1537, 2004.

Upkar Varshney.Pervasive Healthcare Computing. EMR/EHR, Wireless and Health Monitoring.Springer, 1st edition, 2009.


References

References II

Huiru Zheng, Chris Nugent, Paul McCullagh, Yan Huang, Shumei Zhang, William Burns,Richard Davies, Norman Black, Peter Wright, Sue Mawson, Christopher Eccleston, MarkHawley, and Gail Mountain.Smart self management: assistive technology to support people with chronic disease.Journal of Telemedicine and Telecare, 16(4):224–227, 2010.


Extras

URL

Slides

on APICe

→ http://apice.unibo.it/xwiki/bin/view/Talks/MhealthselfmanWoa2016

on SlideShare→ http://www.slideshare.net/andreaomicini/

abms-in-mhealth-selfmanagement

Paper

on APICe

→ http://apice.unibo.it/xwiki/bin/view/Publications/MhealthselfmanWoa2016

on CEUR

→ http://ceur-ws.org/Vol-????/paper ?.pdf


http://apice.unibo.it/xwiki/bin/view/Talks/MhealthselfmanWoa2016

http://www.slideshare.net/andreaomicini/abms-in-mhealth-selfmanagement

http://www.slideshare.net/andreaomicini/abms-in-mhealth-selfmanagement

http://apice.unibo.it/xwiki/bin/view/Publications/MhealthselfmanWoa2016

http://ceur-ws.org/Vol-????/paper_?.pdf

Towards the Adoption of Agent-Based Modellingand Simulation in Mobile Health Systems

for the Self-Management of Chronic Diseases

Sara Montagna Andrea Omicini{sara.montagna, andrea.omicini}@unibo.it

Francesco Degli Angeli Michele Donati

Dipartimento di Informatica – Scienza e Ingegneria (DISI)Alma Mater Studiorum – Universita di Bologna

XVII Workshop “From Objects to Agents” (WOA 2016)Catania, Italy, 30 July 2016


ABMS in m-Health Self-Management

Science

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