The Smart House Based System for the Collection and Analysis of Medical Data

The analysis of personal patient information is one of critical factors for well-been paradigm. The collection and processing system of the medical data based on cloud computing, IoT in medicine and anticipation system for the deterioration of the patient's state on the basis of AI systems were constructed. The time series are used for future state prediction.

[1]  Andy Gray,et al.  Are new models needed to optimize the utilization of new medicines to sustain healthcare systems? , 2015, Expert review of clinical pharmacology.

[2]  Natalia Melnykova.  Semantic Search Personalized Data as Special Method of Processing Medical Information , 2017 .

[3]  Yuriy Syerov,et al.  Protection of University Information Image from Focused Aggressive Actions , 2016, ICONS 2016.

[4]  B. B. Zaidan,et al.  Meeting the Security Requirements of Electronic Medical Records in the ERA of High-Speed Computing , 2014, Journal of Medical Systems.

[5]  Kamran Sartipi,et al.  HL7 FHIR: An Agile and RESTful approach to healthcare information exchange , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[6]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.

[7]  Wilson D. Pace,et al.  Electronic health record functionality needed to better support primary care , 2014, J. Am. Medical Informatics Assoc..

[8]  Olena Vovk,et al.  UNCERTAINTY REDUCTION IN BIG DATA CATALOGUE FOR INFORMATION PRODUCT QUALITY EVALUATION , 2018 .

[9]  Wei Ma,et al.  RxNorm: prescription for electronic drug information exchange , 2005, IT Professional.

[10]  Mauro Giacomini,et al.  A Web Based Tool to Enhance Monitoring and Retention in Care for Tuberculosis Affected Patients , 2017, pHealth.

[11]  Vasileios Megalooikonomou,et al.  A Graph Framework for Multimodal Medical Information Processing , 2016, eTELEMED 2016.

[12]  Christopher Pearce,et al.  A personally controlled electronic health record for Australia , 2014, J. Am. Medical Informatics Assoc..

[13]  LeMai Nguyen,et al.  Electronic health records implementation: An evaluation of information system impact and contingency factors , 2014, Int. J. Medical Informatics.

[14]  Natalya Shakhovska.  Consolidated processing for differential information products , 2011, Perspective Technologies and Methods in MEMS Design.

[15]  Cloann Schultz,et al.  Evaluation of the use of clinical decision support and online resources for pharmacogenomics education. , 2015, Pharmacogenomics.

[16]  O. Ben-Assuli,et al.  Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments. , 2015, Health policy.

[17]  Rainu Kaushal,et al.  Electronic health records and health care quality over time in a federally qualified health center , 2015, J. Am. Medical Informatics Assoc..

[18]  Liva Ralaivola,et al.  Dynamical Modeling with Kernels for Nonlinear Time Series Prediction , 2003, NIPS.

[19]  Li Li,et al.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records , 2016, Scientific Reports.

[20]  L. Smeeth,et al.  Use of electronic healthcare records in large‐scale simple randomized trials at the point of care for the documentation of value‐based medicine , 2014, Journal of internal medicine.

[21]  Yuriy Syerov,et al.  Method of the Data Adequacy Determination of Personal Medical Profiles , 2018 .

[22]  L. Lorenzoni,et al.  Health-care expenditure and health policy in the USA versus other high-spending OECD countries , 2014, The Lancet.

[23]  Natalya Shakhovska,et al.  The Identification of the Operator's Systems Images Using the Method of the Phase Portrait , 2017, CSIT.

[24]  Massimiliano Giacomin,et al.  ArgMed: A Support System for Medical Decision Making Based on the Analysis of Clinical Discussions , 2016 .