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Brief overview of the System
Motivation
- To use technologies to provide personalized healthcare services based on activity, emotion recognition and context-awareness.
- To deal with diverse datasets such as clinical, social and sensory data for constructing intelligent clinical and personalized Knowledge-Base (KB).
- To develop state-of-the-art, standard-based Lifecare Decision Support System that provides recommendations for chronic disease patients.
Goals
- Clinical Analytics: Abnormal observations patterns identification and its effect and relationship with other observations.
- Personalized Analytics: Abnormal behavior identification in activities monitored between different encounters of the patient.
Features
- Construct intelligent knowledge-base using social interaction, sensory and clinical information.
- Utilize cloud infrastructure to reduce healthcare cost.
- Facilitate interoperability among different health care standards.
- Provide clinical, personalized and feedback recommendations for chronic disease patients.
- Support security and privacy of the user's personalized data.
- Utilize big data services for handling large scale data from different sources.
- Verify and validate information provision to patients for evolutionary KB using feedback analysis.
- Track other patients by trusted network building for discovering the best answer to cure a disease.
Uniqueness
- Platform providing clinical, persoanlized and feedback services to chronic disease patients.
- Managing heterogneous datasets from clinical and persoanlized data sources using big data services.
- Standard based clinical knowledge based creation to manipulate information for clinical informatics.
- Use of hybird machine learning approaches for providing persoanlized analytics.
- System evaluation using feedback services and trusted network building based on user's feedback.
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