Combined Machine Learning
and Semantic Modelling for Situation
Awareness and Healthcare
Decision Support
Amira Henaien
1(B) , Hadda Ben Elhadj
2(B) , and Lamia Chaari Fourati
2
1 King Khalid University, Abha, Kingdom of Saudi Arabia
aheniaen@kku.edu.sa
2 Laboratory of Technology and Smart Systems (LT2S), LR16CRNS01, Digital
Research Center of Sfax, Sfax, Tunisia
Hadda.Ibnelhadj@ESTI.rnu.tn, lamia.chaari@enis.rnu.tn
Abstract. The average of global life expectancy at birth was 72 years
in 2016 [1], however, the global healthy life expectancy at birth was only
63.3 years in the same year, 2016 [2]. Living a long life is not any more as
challenging as assuring active and associated life [25]. We propose in this
paper an IoT based holistic remote health monitoring system for chronically ill and elderly patients. It supports smart clinical decision help and
prediction. The patient heterogeneous vital signs and contexts gathered
from wore and surrounding sensors are semantically simplified and modeled via a validated ontology composed by FOAF (Friend of a Friend),
SSN (Semantic Sensors Network)/SOSA (Sensor, Observation, Sample
and Actuator) and ICNP (International Classification Nursing Practices)
ontologies. The reasoner engine is based on a scalable set of inference rules
cohesively integrated with a ML (Machine Learning) algorithm to ensure
predictive analytic and preventive personalized health services. Experimental results prove the efficiency of the proposed system.
Keywords: Active and assisted living · Ontologies · ML · Health
monitoring · Preventive personalized health services
1 Introduction
Information revolution and wireless mobile technology growth have made a considerable contribution to the expansion and empowering of E-health services. In
fact, smart remote and mobile healthcare applications are making an enormous
shift in the health and social care workforce efficiency as well as patients’ wellbeing. The main target of such applications is leveraging IoT, ML and Semantic
Web technologies to ensure opportunities that enable people to be and do what
they value throughout their lives despite illness. The headline goal of E-health
is promoting elderly independence and sustaining cognitive and physical capability via multidisciplinary and user-friendly technology. [6] is one of the earliest
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 197–209, 2020.
https://doi.org/10.1007/978-3-030-51517-1_16
and Semantic Modelling for Situation
Awareness and Healthcare
Decision Support
Amira Henaien
1(B) , Hadda Ben Elhadj
2(B) , and Lamia Chaari Fourati
2
1 King Khalid University, Abha, Kingdom of Saudi Arabia
aheniaen@kku.edu.sa
2 Laboratory of Technology and Smart Systems (LT2S), LR16CRNS01, Digital
Research Center of Sfax, Sfax, Tunisia
Hadda.Ibnelhadj@ESTI.rnu.tn, lamia.chaari@enis.rnu.tn
Abstract. The average of global life expectancy at birth was 72 years
in 2016 [1], however, the global healthy life expectancy at birth was only
63.3 years in the same year, 2016 [2]. Living a long life is not any more as
challenging as assuring active and associated life [25]. We propose in this
paper an IoT based holistic remote health monitoring system for chronically ill and elderly patients. It supports smart clinical decision help and
prediction. The patient heterogeneous vital signs and contexts gathered
from wore and surrounding sensors are semantically simplified and modeled via a validated ontology composed by FOAF (Friend of a Friend),
SSN (Semantic Sensors Network)/SOSA (Sensor, Observation, Sample
and Actuator) and ICNP (International Classification Nursing Practices)
ontologies. The reasoner engine is based on a scalable set of inference rules
cohesively integrated with a ML (Machine Learning) algorithm to ensure
predictive analytic and preventive personalized health services. Experimental results prove the efficiency of the proposed system.
Keywords: Active and assisted living · Ontologies · ML · Health
monitoring · Preventive personalized health services
1 Introduction
Information revolution and wireless mobile technology growth have made a considerable contribution to the expansion and empowering of E-health services. In
fact, smart remote and mobile healthcare applications are making an enormous
shift in the health and social care workforce efficiency as well as patients’ wellbeing. The main target of such applications is leveraging IoT, ML and Semantic
Web technologies to ensure opportunities that enable people to be and do what
they value throughout their lives despite illness. The headline goal of E-health
is promoting elderly independence and sustaining cognitive and physical capability via multidisciplinary and user-friendly technology. [6] is one of the earliest
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 197–209, 2020.
https://doi.org/10.1007/978-3-030-51517-1_16