20240038083. PUBLICITY-EDUCATION PUSHING METHOD AND SYSTEM BASED ON MULTI-SOURCE INFORMATION FUSION simplified abstract (ZHEJIANG LAB)
PUBLICITY-EDUCATION PUSHING METHOD AND SYSTEM BASED ON MULTI-SOURCE INFORMATION FUSION
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PUBLICITY-EDUCATION PUSHING METHOD AND SYSTEM BASED ON MULTI-SOURCE INFORMATION FUSION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240038083 titled 'PUBLICITY-EDUCATION PUSHING METHOD AND SYSTEM BASED ON MULTI-SOURCE INFORMATION FUSION
Simplified Explanation
The present disclosure describes a method and system for publicity-education pushing based on multi-source information fusion. The method involves several steps:
- Constructing a patient publicity-education knowledge graph and pushing it to the patient through a publicity-education applet.
- Fusing and correcting patient basic information, patient diagnosis-treatment information, patient eye movement information, and a patient personality inventory to obtain patient multi-source information.
- Constructing a compliance prediction model using a neural network, using the patient multi-source information and collected patient medication taking behavior data.
- Building a system rule base and searching for a corresponding disease and treatment in the patient publicity-education knowledge graph using information returned by the system rule base. The disease and treatment are then pushed to the patient through the publicity-education applet.
Potential applications of this technology:
- Personalized patient education: The system can provide tailored education materials to patients based on their individual characteristics and medical history.
- Medication compliance improvement: By predicting patient compliance with medication taking behavior, the system can help healthcare providers intervene and improve patient adherence to prescribed treatments.
- Disease awareness and treatment promotion: The system can push relevant disease and treatment information to patients, increasing their knowledge and promoting appropriate healthcare actions.
Problems solved by this technology:
- Lack of personalized patient education: Traditional patient education methods often provide generic information that may not be relevant or engaging for individual patients. This technology addresses this issue by tailoring education materials to each patient's specific needs.
- Poor medication compliance: Non-adherence to prescribed treatments is a significant problem in healthcare. This technology aims to improve medication compliance by predicting patient behavior and providing targeted interventions.
- Limited disease awareness: Patients may lack knowledge about certain diseases and available treatments. This technology helps address this issue by pushing relevant information to patients, increasing their awareness and understanding.
Benefits of this technology:
- Personalized and targeted education: Patients receive education materials that are specifically tailored to their individual characteristics and medical history, enhancing their understanding and engagement.
- Improved medication adherence: By predicting patient compliance and providing interventions, this technology can help improve medication adherence rates, leading to better health outcomes.
- Enhanced disease awareness: Patients gain access to relevant disease and treatment information, empowering them to make informed healthcare decisions and take appropriate actions.
Original Abstract Submitted
the present disclosure discloses a publicity-education pushing method and system based on a multi-source information fusion. the method includes: step s constructing a patient publicity-education knowledge graph, and pushing the patient publicity-education knowledge graph to a patient through a publicity-education applet; step s fusing and correcting patient basic information, patient diagnosis-treatment information, patient eye movement information and a patient personality inventory to obtain patient multi-source information; step s constructing a compliance prediction model through a neural network by using the patient multi-source information and collected patient medication taking behavior data; and step s building a system rule base, and after searching for a corresponding disease and treatment in the patient publicity-education knowledge graph through information returned by the system rule base, pushing the disease and the treatment to the patient through the publicity-education applet.
