Data pre-processing (pattern recognition):
This step involves preparing the input data for analysis by applying various pre-processing techniques. Pattern recognition algorithms may be used to identify and extract relevant patterns or features from the raw data
Extract physiotypes (multivariate time series analysis):
Once the data is pre-processed, multivariate time series analysis techniques are applied to extract physiotypes. Physiotypes refer to specific physiological patterns or characteristics obtained from the collected data. These physiotypes could include vital signs, biomarkers, or other relevant physiological parameters.
Real-time explainable risk analysis (Risk assessment using edge AI):
Using the extracted physiotypes, real-time risk analysis is performed. This involves assessing the risk level or probability of certain outcomes or events based on the patient’s physiological data. The AI model analyzes the data and provides insights into the patient’s condition, such as predicting the likelihood of developing complications or identifying high-risk situations.
Tackle bias and practicality (Risk assessment using edge AI):
In this step, efforts are made to address bias and ensure the practicality of the AI model. Bias can arise due to imbalanced datasets or inherent biases in the data collection process. By carefully considering and addressing these biases, the model aims to provide fair and accurate risk assessments. Additionally, the use of edge AI refers to deploying the AI model directly on edge devices or within the clinical setting, enabling real-time analysis and decision-making without relying solely on centralised systems.
Implement on edge and centre (clinical expert system):
Finally, the AI model is implemented both at the edge (on devices or within local infrastructure) and at the centre (cloud-based system). This implementation allows for the deployment of the AI model in clinical environments, where it can serve as a clinical expert system. The system provides insights and recommendations based on real-time analysis of patient data, assisting healthcare professionals in making informed decisions and improving patient care.