Deep Learning and IoT to Assist Multimorbidity Home Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare in the presence of multimorbidity chronic patients. These environments unite the chronicle patient clinical history sematic representation ICP (Individual Care Process) with the ability of monitoring the living conditions using IoT technologies and events recurring to a fully managed Semantic Web of Things (SWoT) and Machine Learning Algorithms in order to activate the LDC (Less Differentiated Caregiver) for a specific care need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

[1]  Pablo García Del Valle,et al.  Accurate Human Tissue Characterization for Energy-Efficient Wireless On-Body Communications , 2013, Sensors.

[2]  Tsipi Heart,et al.  Older adults: Are they ready to adopt health-related ICT? , 2013, Int. J. Medical Informatics.

[3]  Irene Pimenta Rodrigues,et al.  Extended Clinical Discourse Representation Structure for Controlled Natural Language Clinical Decision Support Systems , 2015 .

[4]  Elena Vildjiounaite,et al.  Five-point acceleration sensing wireless body area network - design and practical experiences , 2004, Eighth International Symposium on Wearable Computers.

[5]  Alex Mihailidis,et al.  A Survey on Ambient-Assisted Living Tools for Older Adults , 2013, IEEE Journal of Biomedical and Health Informatics.

[6]  Brian D. Loader,et al.  Health informatics for older people: a review of ICT facilitated integrated care for older people , 2008 .

[7]  Markus Krötzsch,et al.  The Incredible ELK , 2013, Journal of Automated Reasoning.

[8]  Angus Roberts,et al.  Building a semantically annotated corpus of clinical texts , 2009, J. Biomed. Informatics.

[9]  M. Nies,et al.  Successful aging among assisted living community older adults. , 2013, Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing.

[10]  Mihail Cocosila,et al.  Adoption of mobile ICT for health promotion: an empirical investigation , 2010, Electron. Mark..

[11]  Steve Gardner,et al.  Towards ambient intelligence in assisted living: The creation of an Intelligent Home Care , 2013, 2013 Science and Information Conference.

[12]  Hoi-Jun Yoo,et al.  Low energy wireless body area network systems , 2013, 2013 IEEE International Wireless Symposium (IWS).

[13]  Mukhtiar Memon,et al.  Ambient Assisted Living Healthcare Frameworks, Platforms, Standards, and Quality Attributes , 2014, Sensors.

[14]  Mohammed Bennamoun,et al.  Ontology learning from text: A look back and into the future , 2012, CSUR.

[15]  M. Marschollek,et al.  Health-enabling technologies for pervasive health care: on services and ICT architecture paradigms , 2008, Informatics for health & social care.

[16]  Hoi-Jun Yoo,et al.  The Signal Transmission Mechanism on the Surface of Human Body for Body Channel Communication , 2012, IEEE Transactions on Microwave Theory and Techniques.

[17]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[18]  Marc Simon Wegmüller,et al.  Intra-body communication for biomedical sensor networks , 2007 .

[19]  C. Jackson,et al.  Health care savings with the patient-centered medical home: Community Care of North Carolina's experience. , 2014, Population health management.

[20]  Ruijiao Li,et al.  Cognitive assisted living ambient system: a survey , 2015, Digit. Commun. Networks.