ML and Ontology Based Situation Awareness System
207
of this work, we are aiming to develop a prototype of the proposed system and
test it on a larger samples of data collected from an elderly population with
chronic diseases.
References
1. World Health Organization. https://www.who.int/gho/mortality burden disease/
life tables/situation trends/en/
2. World Health Organization. https://www.who.int/gho/mortality burden disease/
life tables/hale/en/
3. Alamri, A.: Ontology middleware for integration of IoT healthcare information
systems in EHR systems. Computers 7(4), 51 (2018). https://doi.org/10.3390/
computers7040051
4. Baltruˇ saitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey
and taxonomy. arXiv preprint arXiv:1705.09406 (2017)
5. Choi, E., et al.: Multi-layer representation learning for medical concepts. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1495–1504 (2016)
6. Detmer, D.E.: Building the national health information infrastructure for personal
health, health care services, public health, and research. BMC Med. Inform. Decis.
Mak. 3(1), 1 (2003)
7. Dogdu, E.: Semantic web in ehealth, January 2009. https://doi.org/10.1145/
1566445.1566542
8. Doulaverakis, C., Nikolaidis, G., Kleontas, A., Kompatsiaris, I.: GalenOWL:
ontology-based drug recommendations discovery. Biomed. Semant. 3, 1–9 (2012)
9. Faiz, I., Mukhtar, H., Qamar, A., Khan, S.: A semantic rules & reasoning based
approach for diet and exercise management for diabetics, pp. 94–99, January 2015.
https://doi.org/10.1109/ICET.2014.7021023
10. F´ elix, N.D.d.C., Ramos, N.d.M., Nascimento, M.N.R., Moreira, T.M.M., de
Oliveira, C.J.: Nursing diagnoses from ICNP R
for people with metabolic syndrome. Rev. Bras. Enferm. 71(suppl 1), 467–474 (2018). https://doi.org/10.1590/
0034-7167-2017-0125
11. Haluza, D., Jungwirth, D.: ICT and the future of healthcare: aspects of pervasive
health monitoring. Inform. Health Soc. Care 43(1), 1–11 (2018)
12. Henry, J., Pylypchuk, Y., Searcy, T., Patel, V.: Adoption of electronic health record
systems among us non-federal acute care hospitals: 2008–2015. ONC Data Brief
35, 1–9 (2016)
13. Holmes, G., Donkin, A., Witten, I.H.: WEKA: a machine learning workbench.
In: Proceedings of ANZIIS 1994-Australian New Zealand Intelligent Information
Systems Conference, pp. 357–361. IEEE (1994)
14. Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.:
SWRL. https://www.w3.org/Submission/SWRL/
15. Iroju, O., Soriyan, A., Gambo, I., Olaleke, J.: Interoperability in healthcare: benefits, challenges and resolutions. Int. J. Innov. Appl. Stud. 3(1), 262–270 (2013)
16. Janowicz, K., Haller, A., Cox, S.J.D., Le, D.: Web semantics : science, services and
agents on the world wide web SOSA: a lightweight ontology for sensors, observations, samples, and actuators. Web Semant. Sci. Serv. Agents World Wide Web
56, 1–10 (2019). https://doi.org/10.1016/j.websem.2018.06.003
207
of this work, we are aiming to develop a prototype of the proposed system and
test it on a larger samples of data collected from an elderly population with
chronic diseases.
References
1. World Health Organization. https://www.who.int/gho/mortality burden disease/
life tables/situation trends/en/
2. World Health Organization. https://www.who.int/gho/mortality burden disease/
life tables/hale/en/
3. Alamri, A.: Ontology middleware for integration of IoT healthcare information
systems in EHR systems. Computers 7(4), 51 (2018). https://doi.org/10.3390/
computers7040051
4. Baltruˇ saitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey
and taxonomy. arXiv preprint arXiv:1705.09406 (2017)
5. Choi, E., et al.: Multi-layer representation learning for medical concepts. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1495–1504 (2016)
6. Detmer, D.E.: Building the national health information infrastructure for personal
health, health care services, public health, and research. BMC Med. Inform. Decis.
Mak. 3(1), 1 (2003)
7. Dogdu, E.: Semantic web in ehealth, January 2009. https://doi.org/10.1145/
1566445.1566542
8. Doulaverakis, C., Nikolaidis, G., Kleontas, A., Kompatsiaris, I.: GalenOWL:
ontology-based drug recommendations discovery. Biomed. Semant. 3, 1–9 (2012)
9. Faiz, I., Mukhtar, H., Qamar, A., Khan, S.: A semantic rules & reasoning based
approach for diet and exercise management for diabetics, pp. 94–99, January 2015.
https://doi.org/10.1109/ICET.2014.7021023
10. F´ elix, N.D.d.C., Ramos, N.d.M., Nascimento, M.N.R., Moreira, T.M.M., de
Oliveira, C.J.: Nursing diagnoses from ICNP R
for people with metabolic syndrome. Rev. Bras. Enferm. 71(suppl 1), 467–474 (2018). https://doi.org/10.1590/
0034-7167-2017-0125
11. Haluza, D., Jungwirth, D.: ICT and the future of healthcare: aspects of pervasive
health monitoring. Inform. Health Soc. Care 43(1), 1–11 (2018)
12. Henry, J., Pylypchuk, Y., Searcy, T., Patel, V.: Adoption of electronic health record
systems among us non-federal acute care hospitals: 2008–2015. ONC Data Brief
35, 1–9 (2016)
13. Holmes, G., Donkin, A., Witten, I.H.: WEKA: a machine learning workbench.
In: Proceedings of ANZIIS 1994-Australian New Zealand Intelligent Information
Systems Conference, pp. 357–361. IEEE (1994)
14. Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.:
SWRL. https://www.w3.org/Submission/SWRL/
15. Iroju, O., Soriyan, A., Gambo, I., Olaleke, J.: Interoperability in healthcare: benefits, challenges and resolutions. Int. J. Innov. Appl. Stud. 3(1), 262–270 (2013)
16. Janowicz, K., Haller, A., Cox, S.J.D., Le, D.: Web semantics : science, services and
agents on the world wide web SOSA: a lightweight ontology for sensors, observations, samples, and actuators. Web Semant. Sci. Serv. Agents World Wide Web
56, 1–10 (2019). https://doi.org/10.1016/j.websem.2018.06.003