The final version of Big Data Platform was deployed, including new services allowing the feature of executing AI based modules, and new capabilities providing an easier way to query data stored in the platform.
Multilingual Conversation System was completed and a data model to deliver speech-enabled services and collection of patient experiences (mood, PREMs and PROMs) supported by conversational intelligence. MRAST Framework was delivered as a framework of new wellbeing software sensors to recognize, classify and track symptoms and patient risk factors by exploring PGHD, including digital biomarkers, CTCs, and patient-reported experiences (diaries).
CTC chip was successfully manufactured in COP, and priming and blood processing protocols were optimised taking the PDMS prototype as a reference. The chips were used during the clinical trial. A web application for CTC enumeration and classification results was developed. Acquired CTC images were preprocessed and annotated in accordance with COCO standard, to train an AI algorithm able to classify new CTC images using ML.
The trajectories and cohorts were updated and made available through a new designed web-interface. Additionally, the overall improvement of AI algorithms and tools is reflected in the obtained ENRICHED sets of EHR data, which were iteratively improved to support filling gaps in structured data.
The mClinician web app provides a user-friendly interface for clinicians to enter patient data in a structured format, saving these data as FHIR resources. Clinicians can view and modify existing records. It also enables clinicians to manage and monitor patient activity with the data collected through mHealth app.
The mHealth app was developed as an Android mobile application to gather data from the patients and present that data to both the user and the clinician. Data is gathered using a bluetooth smart-band and in-app features such as questionnaires, video diary and emotion dialog.
In the INFERENCE ENGINE of the clinical decision support system (CDSS) a scoring algorithm was developed for assessing the risk of breast and colon cancer recurrence according to patient’s retrospective data, questionnaire responses and patient habits and lifestyle based on the determined risk factors in the knowledge base and their hazard ratios from the researches in the literature.
Also Full clinical validation was done. Second set of patient workshops in each hospital was organised for feedback gathering. Co-creation activities happened in workshops and individual consultations with clinicians.
Legal and ethical aspects correlated to the activities of the Consortium were taken into consideration. The legal qualification of PERSIST Solution under the Medical Device Regulation 2017/745, along with its relevant KERs, was another perspective taken under close observation.
PERSIST consortium presented the project on several events and seminars, as well as to organise project-specific events. Besides, relevant materials were produced like newsletters, press releases, videos etc. Furthermore, several electronic and web dissemination channels kept their dissemination activities, including the project website, its collaboration portal, social media accounts and channels.
Lastly, the “Business Case” activity provided the guidelines for making a reliable business model. It also provided the market test template where applicable.
