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ABMS in m-Health Self-Management

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The impact of mobile technologies on healthcare is particularly evident in the case of self-management of chronic diseases, where they can decrease spending and improve the patient quality of life. In this talk we propose the adoption of agent-based modelling and simulation techniques as built-in tools to dynamically monitor patient health state and provide feedbacks for self-management. To demonstrate the feasibility of our proposal we focus on Type 1 Diabetes Mellitus as our case study, and provide some preliminary simulation results.

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ABMS in m-Health Self-Management

  1. 1. Towards the Adoption of Agent-Based Modelling and 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 – Universit`a 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
  2. 2. 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 2 / 29
  3. 3. Motivation & Goals 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 3 / 29
  4. 4. Motivation & Goals Context & Motivation Context The emergence of mobile things connected to the Internet even while moving around – the Internet of Mobile Things (IoMT) – is opening new frontiers 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 of effectively supporting disease self-management Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 4 / 29
  5. 5. Motivation & Goals Goal To demonstrate feasibility of our proposal by simple experiments on Type 1 DM disease Our proposal to adopt modelling and simulation tools for supporting disease self-management, since they provide both short-term and long-term clinical predictions of patient health predictions provide information for making the most informed choices between available treatments and interventions agent-based modelling and simulation (ABMS) tools in particular Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 5 / 29
  6. 6. IoMT and m-Health 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 6 / 29
  7. 7. IoMT and m-Health IoMT: A New Era in Medicine the introduction of ICT into healthcare systems is revolutionising medicine and opening new frontiers in healthcare crucial role of mobile devices for data collection, access, sharing, and elaboration IoMT applied to health systems changes the way health-services will be provided → mobile Health (m-Health) & pervasive healthcare [Var09] “Pervasive healthcare is the conceptual system of providing healthcare to anyone, at anytime, and anywhere by removing restraints of time and location while increasing both the coverage and the quality of healthcare”. Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 7 / 29
  8. 8. IoMT and m-Health Main Expected Improvements A number of improvements in healthcare are expected to come by the introduction 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 8 / 29
  9. 9. Self-Management of Chronic Diseases 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 9 / 29
  10. 10. Self-Management of Chronic Diseases Definition and Main Facts Self-management of chronic diseases [NSM04, ENK+ 13] . . . is defined as the active involvement of patients in their treatment with day-to-day decisions about different actions to be taken: control of symptoms, take medicines, make lifestyle changes, undertake preventive actions 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 hoc sensors and data sharing ! lack of well-established models, theories, and algorithms for data elaboration Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 10 / 29
  11. 11. Self-Management of Chronic Diseases Our Proposal in Context From the available literature. . . . . . many algorithms and theories from computer science for elaborating 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 11 / 29
  12. 12. Self-Management of Chronic Diseases Why ABMS? ABMS makes it possible to directly capture and represent main aspects of complex systems—such as the human body [Bon02] The benefits of ABM over other modelling techniques can be captured 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 individual entities. By definition, they cannot be reduced to the systems parts: the whole is more than the sum of its parts because of the interactions between the parts. An emergent phenomenon can have properties that are decoupled from the properties of the part. Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 12 / 29
  13. 13. Background on Type 1 Diabetes Mellitus Physiopathology 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 13 / 29
  14. 14. Background on Type 1 Diabetes Mellitus Physiopathology Metabolic System c 2001 Benjamin Cummings Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 14 / 29
  15. 15. Background on Type 1 Diabetes Mellitus Physiopathology 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 pancreatic hormones insulin enables the uptake of glucose, produced by β-cells glucagon promotes the release of glucose, produced by α-cells Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 15 / 29
  16. 16. Background on Type 1 Diabetes Mellitus Physiopathology 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 16 / 29
  17. 17. Background on Type 1 Diabetes Mellitus Physiopathology 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 17 / 29
  18. 18. Type 1 DM self-management 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 18 / 29
  19. 19. Type 1 DM self-management Type 1 DM Goal To find solutions for identifying personalised therapies and lifestyle suggestions for patients to improve their outcome Proposal We present an ABMS of Type 1 DM self- management from an initial state that reproduces the health condition of a patient. . . . . . a low-level simulation of the metabolic system is performed from the simulation results, feedbacks are provided to the patient Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 19 / 29
  20. 20. Type 1 DM self-management Type 1 DM Model Two-Levels Model High-level: self-management model Patients are agents that behaves according to feedbacks received by their PDAs Low-level: disease model Metabolic system as a set of interacting agents each agent is a set of cells conducting the same activities agents interact via an interaction medium, the environment, that models the bloodstream where agents release and retrieve molecules Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 20 / 29
  21. 21. Type 1 DM self-management Type 1 DM Model Metabolic System and Type 1 DM Agents intestine-cells agent absorption of food substances release of the glucose derived by digestion in the bloodstream β-cells agent glucose level in the blood < 75 mg/dl → secretion of insulin the model of Type 1 DM is obtained by simply stopping this agent α-cells agent glucose level in the blood < 70 mg/dl → secretion of glucagon liver-cells agent glucagon available → glycogenolysis and release of glucose glucose level in the blood > 75 mg/dl → uptake of glucose and glycogen synthesis muscle-cells agent insulin available → uptake of glucose and glycogen synthesis physical exercises→ usage of glycogen brain-cells agent continuous absorption of glucose Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 21 / 29
  22. 22. Type 1 DM self-management Early Simulation Results Healthy Patient Simulation Results The model is implemented on top of the MASON infrastructure Glycaemia Plot Glycaemia 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 Time (min) 0 10 20 30 40 50 60 70 80 90 100 110 mg/dl Insulin Plot Insulin 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 Time (min) 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 microUI/ml Glucagon Plot Glucagon 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 Time (min) 0 5 10 15 20 25 30 35 40 45 50 55 pg/ml dynamic of the metabolic system over 3 days in the case of healthy patient 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, while glucagon is secreted mainly during the night when patient does not eat Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 22 / 29
  23. 23. Type 1 DM self-management Early Simulation Results Type 1 DM Patient Simulation Results Glycaemia Plot Glycaemia 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 Time (min) 0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400 425 mg/dl Insulin Plot Insulin 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 Time (min) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 microUI/ml Glucagon Plot Glucagon 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 Time (min) 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 pg/ml dynamic over 3 days of glucose, insulin, and glucagon concentration in blood in a Type 1 DM affected patient. the glycaemia value is no longer under control, and the patient enters soon into a hyperglycaemia state insulin is no longer produced glucagon also is no longer secreted, since glucose concentration is over the threshold Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 23 / 29
  24. 24. Conclusion & Future Work Conclusion & Future Work Conclusion we explored some future directions in the field of self-management of chronic diseases ABMS as a built-in tool – within a IoMT infrastructure to automatically characterise health state to 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 24 / 29
  25. 25. 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 Type 1 DM Model Early Simulation Results Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 25 / 29
  26. 26. 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, C´ecile Knai, Matthias Brunn, Karine Chevreul, Annalijn Conklin, Isabelle Durand-Zaleski, Antje Erler, Maria Flamm, Anne Frølich, Birgit Fullerton, Ramune Jacobsen, Zuleika Saz-Parkinson, Antonio Sarria-Santamera, Andreas S¨onnichsen, and Hubertus Vrijhoef. Is Europe putting theory into practice? A qualitative study of the level of self-management support 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. Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 26 / 29
  27. 27. 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, Mark Hawley, and Gail Mountain. Smart self management: assistive technology to support people with chronic disease. Journal of Telemedicine and Telecare, 16(4):224–227, 2010. Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 27 / 29
  28. 28. 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 Montagna, Omicini, Degli Angeli, Donati (UniBo)ABMS in m-Health Self-Management WOA 2016, 30/07/2016 28 / 29
  29. 29. Towards the Adoption of Agent-Based Modelling and 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 – Universit`a 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 29 / 29

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