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CSE5810: Intro to Biomedical Informatics

CSE5810: Intro to Biomedical Informatics. Mobile Computing to Impact Patient Health and Data Availability for Diseases Monitoring Xian Shao xian.shao@engr.uconn.edu Advisor: Prof. Steven A. Demurjian , Sr. steve@engr.uconn.edu. What is Mobile Computing. Mobile computing is:

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CSE5810: Intro to Biomedical Informatics

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  1. CSE5810: Intro to Biomedical Informatics Mobile Computing to Impact Patient Health and Data Availability for Diseases Monitoring Xian Shao xian.shao@engr.uconn.edu Advisor: Prof. Steven A. Demurjian, Sr. steve@engr.uconn.edu

  2. What is Mobile Computing • Mobile computing is: • Human-computer interaction • Computing enabled by presence of wireless enabled portable devices (PDAs, cell phones, tablet etc.) • Involves mobile communication, mobile hardware, and mobile software • Many other names/overlapping computing paradigms: • Pervasive Computing • Ubiquitous Computing • Wireless Computing • Embedded Computing • Nomadic Computing • Wireless Sensor Networks • Ad-Hoc Networks • Mesh Networks • Vehicular Networks • ...

  3. Mobile Computing • Applications • Location-awareness • Mobility Support • Security • Resource Management • Network Protocol • Broadcast • Technologies • Standards • Wireless Medium

  4. Mobile Computing • Wireless communication: • Cellular data service: GSM, CDMA, GPRS, 3G, 4G, etc. • Wi-Fi connection • Satellite Internet access • Mobile devices: • Personal digital assistant/enterprise digital assistant • Smartphone • Tablet computer • Ultra-Mobile PC • Wearable computer

  5. Mobile Computing • Revolution: • Mobile is global • Cost effectively, Convenient • Anytime and anywhere • Contextual • Limitation: • Range & Bandwidth • Security standards • Power consumption • Transmission interferences • Potential health hazards • Human interface with device

  6. Mobile Applications for Diseases Monitoring • Background: • Mobile devices are becoming more and more ubiquitous in our daily life. • Chronic patients must carry out a rigorous control of diverse factors in their lives. • As the technologies rapidly evolving, more and more mobile device applications related to healthcare are being developed . • Why use Mobile devices: • Mobile devices are much cheaper than the desktop nowadays • People carry mobile device with them everywhere • People stored their information in their mobile devices include the health condition • The technical capabilities of mobile devices increased significant

  7. Mobile Applications for Diseases Monitoring • Mobile Technology Capabilities for Monitoring Patients: • Text message (SMS) • Camera • Native applications • Automated sensing（pedometer, blood pressure monitors, glucose meters, and fitness equipment） • Internet Access

  8. Healthcare Applications • Application types: • Data collection • Information entered manually • gIUCModel • Personal Health Assistant (PHA) • Collect information automatically • A mobile monitoring application for chronic diseases (Vladimir Villarreal 2013) • Mobile access to health information (Lena Mamykina 2006) • etc. • Data collection & analysis • One application (Maarten van der Heijden 2013 ) • Another application (Mark Beattie 2014) • Etc…

  9. Healthcare Applications • gIUCModel • gIUCModl is an application that use mobile technology to help to monitor chronic diseases, especially for diabetes. Using this application, patients can collect their daily information and upload through mobile phones or other platforms. And physicians can review each patient’s information and return suggestions to patients. • General Structure:

  10. Healthcare Applications • Five modules of the structure: • Data Interface • Automatically: importing data from a XML file generated by glucometers. • Manually • Database • A cloud database (Microsoft Health Vault) • A local database • gIUCModel recommender system • system automatically evaluate patient data and provide suggestions, according to the information which patients provided. • E-learning module • it offers a fully virtualized education space with recommendations from recommendation system.

  11. Healthcare Applications • Glucose model module • It obtains a customized model each patient’s glucose blood levels with the information provided by patients through evolutionary computation. gIUCModel recommender system, E-Learning module and Glucose model module are three new models provided by this application. • User profiles: • Administrator • Create accounts within the application. • Physician • Introduce new patients and communicate with them, observe their data and their evolution. • Patient • Upload their data and receive the recommendations related to his condition.

  12. Healthcare Applications • A mobile monitoring application for chronic diseases (Vladimir Villarreal 2013) • This application contains three components that enable the semiautomatic development of software, independent of the target disease and adaptable to the particular needs. • The first component is ontologies that classify medical elements such as disease, recommendations, preventions, food, mobile devices and diet suggestions. • The second component is the distribution of the devices in layers, allowing the generation of final applications distributed in a medical context. These layers are defined to develop and maintain the set of applications. • The third and most important element is developing patterns known as MobiPatterns. • A MobiPattern defines the schema of each control module that is a part of the final application.

  13. Healthcare Applications • Relationship between the distributed application and multiple devices: • Elements: biometric devices, mobile devices, medical server (use MySQL as local database). • Developed application (Android): • One corresponding to the needs of physician • One to the needs of patient

  14. Healthcare Applications • MobiPatterns: • This application defined and developed a set of patterns known as MoiPatterns to integrate the modules into the mobile device. These MobiPatterns define the schema of each screen of the final application and the functional structure. And one MobiPattern always depends on another MobiPattern. • Distribution and relationship:

  15. Healthcare Applications • Structure of the ontology for the application: • Ontology classification: • Patient’s Profile: defines each patient’s data (Common Profile and Individual Profile).

  16. Healthcare Applications • Common profile: this profile stores the information about the patient’s diseases. • Diseases: defines a classification of diseases. • ModuleDefinition: elements generated according to each patient’s profile. • Food: defines a classification of the different types of food to be consumed by the patient.

  17. Mobile access to health information (Lena Mamykna 2006) • It presents three approached to investigating health management on diabetes. • Face-to-face interview • Observation of Diabetes Support Group • Cognitive Probe • Cognitive probe • It has dual nature: • it was meant to heighten behavior and engage them in reflective analysis. • it served as an early prototype of a health monitoring solution.The application, Continuous Health Awareness Program (CHAP).

  18. Healthcare Applications • The CHAP application • It utilized sensing and self-report techniques to capture individuals’ actions and daily blood sugar trends. • Components: • GlucoWatch G2 Biographer: a commercially available glucose monitoring device worn as a wrist-watch that non-invasively samples blood sugar every 10 minutes. • X10 motion detection sensors: which positioned in places of usual activity, it’s unique for each household. • A computer-based diary application: it allowing individuals to report on their activities, composition of meals or medications as well as their emotional state. The diary application was available from a laptop screen augmented with touch-sensitive MagicTouch cover to simplify user interaction. • A webcam: it used for free-form comments or notes for the research team. The main purpose of the motion detection sensors was to provide an additional reference for the research team and help assess accuracy of self-reports.

  19. Healthcare Applications • A system for chronic obstructive pulmonary disease (COPD) exacerbation management. (Maarten 2013) • It has the novel feature of including automatic data interpretation by a probabilistic risk model, enabling autonomous operation to support patient self-management. • General architecture:

  20. Healthcare Applications • The systems components: • A smartphone: communication and computation • Sensor: obtain objective information on the patient’s health status, transmitted wirelessly to the smartphone • A web-based systems: scheduling tasks and collecting patient data • Hardware components: • Smartphone (Android OS) • Sensor interface: the phone communicates with the sensors via a Mobi, a Bluetoothcapable multichannel sensor-interface • Pulse-oximeter • Spirometer • The Aerial application • This application is based on Android system • It provides following functionality: • A time alarm o signal the registration • A touch-screen interface for the questionnaire

  21. Healthcare Applications • Processing of the spirometer data to compute the forced expiratory volume in 1s. • Computation of the probability of an exacerbation based on the observed data • Asynchronously transmission of the observed data to the server over a secured data connection • Risk model • The main component of this system • Based on the data that is gathered, the model can compute the probability of an exacerbation. The model that this system use is a Bayesian network. • For COPD models, the main outcome variable is exacerbation, focusing on a symptom based definition. But the nature of a Bayesian network allows us to easily inspect probabilities for any variable. • TBD

  22. Healthcare Applications • Web-centre • It is the administration web-application that was built using the workflow management system iTask. • The workflow system implements advanced feature to generate and coordinate tasks and provides a generic (web) interface. • The other data collection and analysis applications (TBD)

  23. Conclusion • In this review, I evaluated five eHealth applications, they are gIUCModel, PHA, A mobile monitoring application for chronic diseases (Vladimir Villarreal 2013), Mobile access to health information (Lena Mamykna 2006)and A system for chronic obstructive pulmonary disease (COPD) exacerbation management. (Maarten 2013). • In this applications, gIUCModel and PHA only support manually enter, Vladimir Villarreal 2013 and Lena Mamykna 2006, these two application support automated data collection, all these four applications only can collect data. Maarten 2013, this application can collect data, but analysis data as well. • More applications will be determined.

  24. Reference • Hidalgo JI, Maqueda E, Risco – Martin JL, Cuesta- Infante A, Colmenar JM, Nobel J, “glUCModel - A monitoring and modeling system for chronic diseases,”, J Biomed Inform. 2014 Jan 7. pii: S1532-0464(13)00206-2. doi: 10.1016/j.jbi.2013.12.015J Biomed Inform. 2014 Jan 7. pii: S1532-0464(13)00206-2. doi: 10.1016/j.jbi.2013.12.015: http://www.ncbi.nlm.nih.gov/pubmed/24407050 • PHA-course project • Vladimir Villarreal, Jesus Fontecha, Ramon Hervas, Jose Bravo, “Mobile and ubiquitous architecture for the medical control of chronic diseases through the use of intelligent devices: Using the architecture for patients with diabetes”, Future Generation Computer Systems, Volume 34, May 2014, Pages 161–175: http://www.sciencedirect.com/science/article/pii/S0167739X1300277X • L. Mamykina, E.D. Mynatt, D.R. Kaufman, Investigating health management practices of individuals with diabetes, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, Montreal, Quebec, Canada, 2006. • Van der Heijden, Maarten, et al. "An autonomous mobile system for the management of COPD." Journal of biomedical informatics 46.3 (2013): 458-469.

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