ML and Ontology Based Situation Awareness System
199
2 Background and Related Works
Adoption of EHR
1 has increased almost 9 times since 2008 [12]. This huge
amount of data circling in clinical information systems has formed new challenges as: semantic interoperability, standardization, automatic medical discovery, knowledge reuse, preventive personalized health services, etc. Aligned with
this list of challenges, our work is based on three key concepts: Ontologies,
Semantic Rules and ML; presented in the sequel:
Ontologies Based Semantic Healthcare Modelling: The conceptual model
ontology is encouraging knowledge reuse and simplify problem solving in various fields. Healthcare applications are one of the systems that benefit from
using ontologies: drug recommendations discovery [8], clinical support decisions
[31], home personalized care to chronic patients [20], healthcare monitoring [33],
etc. In Ontologies engineering, integration of ontologies is a useful process that
consists on the combination of two or more standard validate ontologies from
different disciplines in the aim to create a new multi-disciplinary ontology [23].
Semantic Rules Healthcare Reasoning: Semantic web and its technologies
are providing efficient solutions in the information and system integration in
any distributed information system environments including eHealth systems for
which information integration and knowledge discovery are highly recommended
[7]. The combination of Semantic Web Rules with Ontology are becoming a
mature technology [14]. It use widespread in healthcare and clinical systems.
A semantic rules are used in reasoning based approach for dieting and exercising management for diabetics [9]. OWL ontologies and SWRL are combined to
integrate reasoning for decision support in alerting system [21].
ML Techniques for Healthcare: The high dimensional features and the availability of high quality software made the ML techniques widely used in all fields
[4]. It refers to a set of algorithms used to extract useful knowledge or to learn by
searching for interesting patterns in a large volumes of previously collected data.
The use of ML algorithms in medicine is a hot research topic: disease progression
[36,37], diagnosis prediction [5,19,35], and so on. However, those technologies
are not mature enough and researchers are still working in the different possibilities and manners to integrate ML algorithms in healthcare systems [29]. One
of the combination that appears successful and promising is the combination of
ML techniques and ontologies [18,26,28].
3 Proposed Methodology
Our main goal is the integration of the ML Techniques in a combined ontology
semantic modelling and semantic rules based reasoning healthcare framework for
1 Electronic Health Records.
199
2 Background and Related Works
Adoption of EHR
1 has increased almost 9 times since 2008 [12]. This huge
amount of data circling in clinical information systems has formed new challenges as: semantic interoperability, standardization, automatic medical discovery, knowledge reuse, preventive personalized health services, etc. Aligned with
this list of challenges, our work is based on three key concepts: Ontologies,
Semantic Rules and ML; presented in the sequel:
Ontologies Based Semantic Healthcare Modelling: The conceptual model
ontology is encouraging knowledge reuse and simplify problem solving in various fields. Healthcare applications are one of the systems that benefit from
using ontologies: drug recommendations discovery [8], clinical support decisions
[31], home personalized care to chronic patients [20], healthcare monitoring [33],
etc. In Ontologies engineering, integration of ontologies is a useful process that
consists on the combination of two or more standard validate ontologies from
different disciplines in the aim to create a new multi-disciplinary ontology [23].
Semantic Rules Healthcare Reasoning: Semantic web and its technologies
are providing efficient solutions in the information and system integration in
any distributed information system environments including eHealth systems for
which information integration and knowledge discovery are highly recommended
[7]. The combination of Semantic Web Rules with Ontology are becoming a
mature technology [14]. It use widespread in healthcare and clinical systems.
A semantic rules are used in reasoning based approach for dieting and exercising management for diabetics [9]. OWL ontologies and SWRL are combined to
integrate reasoning for decision support in alerting system [21].
ML Techniques for Healthcare: The high dimensional features and the availability of high quality software made the ML techniques widely used in all fields
[4]. It refers to a set of algorithms used to extract useful knowledge or to learn by
searching for interesting patterns in a large volumes of previously collected data.
The use of ML algorithms in medicine is a hot research topic: disease progression
[36,37], diagnosis prediction [5,19,35], and so on. However, those technologies
are not mature enough and researchers are still working in the different possibilities and manners to integrate ML algorithms in healthcare systems [29]. One
of the combination that appears successful and promising is the combination of
ML techniques and ontologies [18,26,28].
3 Proposed Methodology
Our main goal is the integration of the ML Techniques in a combined ontology
semantic modelling and semantic rules based reasoning healthcare framework for
1 Electronic Health Records.