Taking the generic architecture described earlier in the "Methodology for Creating a DSS", the architecture of the BD2DECIDE Project would be as follows:
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As with the general architecture, there are users, who are the decision makers. They can be either clinicians or researchers involved in HNC patients. On the one hand, the system supports clinicians in the event of making a decision and, on the other, provides researchers with the means to analyze data and to move forward and progress in the knowledge of this pathology.
Thus, the users interact with the DSS through the LS, by a request. Additionally, the user gets a response of the system through the PS and, as explained before, these two systems are in parallel architecture …show more content…
The Visual Analytics module, facilitates the work of clinical researchers through the visualization of the information obtained from vast and heterogeneous sources and its interpretation and representation. To carry out this task, clinical cases of patients retrieved from cancer registries will be used, along with population data such as alcoholism, smoking, pollution, etc. The main activities within this task are:
- The Knowledge Representation: through data presentation, visualization for EHR exploration and smart visualization techniques will be used to handle big data sets and heterogeneous information from individual or populations of patients.
- Knowledge-based contouring: from the use of Data Abstraction and Data Mining techniques such as machine learning, pattern recognition or statistics, the BD2Decide system will extract from repositories, general rules and statistics regarding cancer patient health condition, progress in relation to treatment, diagnosis and potentially prognosis of the disease course, similar cases, recommendations, …show more content…
It also includes data related to risk factors for the development of Head and Neck Cancer.
- Diagnostics and prognosis. This would represent the new knowledge acquired, modifying the content of the Knowledge Base.
In addition, the system is linked to external data sources and experimental research that provide information related to the population. This information makes it able to determine decisions regarding the treatment of a particular patient as they provide information about lifestyle and habits that could affect the progress and evolution of the disease.
Returning to the before mentioned ontologies, nowadays, there are several ontologies involved in the domain of health, such as SNOMED CT [35], and in particular, in cancer, such as pMedicine [36], or NEOMARK [37], for oral cancer, which represents clinical data, MR imaging, genomic and PCR data. These have a lot of value as they are useful for the use of logic and semantics that allow the handling of massive data. With ontologies, it is allowed the interoperability, standardization and integration of data, obtaining a homogeneous and coherent
cambiar la imagen
As with the general architecture, there are users, who are the decision makers. They can be either clinicians or researchers involved in HNC patients. On the one hand, the system supports clinicians in the event of making a decision and, on the other, provides researchers with the means to analyze data and to move forward and progress in the knowledge of this pathology.
Thus, the users interact with the DSS through the LS, by a request. Additionally, the user gets a response of the system through the PS and, as explained before, these two systems are in parallel architecture …show more content…
The Visual Analytics module, facilitates the work of clinical researchers through the visualization of the information obtained from vast and heterogeneous sources and its interpretation and representation. To carry out this task, clinical cases of patients retrieved from cancer registries will be used, along with population data such as alcoholism, smoking, pollution, etc. The main activities within this task are:
- The Knowledge Representation: through data presentation, visualization for EHR exploration and smart visualization techniques will be used to handle big data sets and heterogeneous information from individual or populations of patients.
- Knowledge-based contouring: from the use of Data Abstraction and Data Mining techniques such as machine learning, pattern recognition or statistics, the BD2Decide system will extract from repositories, general rules and statistics regarding cancer patient health condition, progress in relation to treatment, diagnosis and potentially prognosis of the disease course, similar cases, recommendations, …show more content…
It also includes data related to risk factors for the development of Head and Neck Cancer.
- Diagnostics and prognosis. This would represent the new knowledge acquired, modifying the content of the Knowledge Base.
In addition, the system is linked to external data sources and experimental research that provide information related to the population. This information makes it able to determine decisions regarding the treatment of a particular patient as they provide information about lifestyle and habits that could affect the progress and evolution of the disease.
Returning to the before mentioned ontologies, nowadays, there are several ontologies involved in the domain of health, such as SNOMED CT [35], and in particular, in cancer, such as pMedicine [36], or NEOMARK [37], for oral cancer, which represents clinical data, MR imaging, genomic and PCR data. These have a lot of value as they are useful for the use of logic and semantics that allow the handling of massive data. With ontologies, it is allowed the interoperability, standardization and integration of data, obtaining a homogeneous and coherent