Healthentia Smart Services
A large number of innovation features are evaluated for their feasibility and assessed for integration into future versions of Healthentia. In this section you can find descriptions of features that are currently under investigation; most of them under the framework of R&D initiatives. These features are not medical device modules and their inclusion into the SaMD is subject to the D&D procedure, by considering patients' safety and performance.
Data Structure & Normalization
Data sources need to be structured and get normalized in order to be uniform.
Physical activity sensing of patients, extraction of their habits and monitoring of deviations.
Behavioral Change Models
Different behavioral change models are used for coaching and goal setting to assess motivation and capacity.
The discovered biomarkers have predictive power in what patients will report about themselves.
Unsupervised, automatic biomarker discovery in data and grouping of validated biomarkers into phenotypes
Our flow for biomarker discovery utilizes Healthentia for data collection and Healthentia Smart Services to establish the important attributes that indicate any clinical outcome, as well as the way these attributes should be combined into the biomarker via learning predictive models. It facilitates data-driven and clinical validation of these models into digital composite biomarkers.
Multi-dimensional vectors create a visual patient phenotype model with characteristic behavioral habits
Pattern recognition algorithms cluster patients of similar phenotypes that can be then addressed by the system/investigator in a similar way.
Any deviation from the model is enumerated and monitored