Hackathon for the COVID-19

There is a pressing need by healthcare professionals to access information relevant to clinical practice in a more effective way. Over 80% of clinically relevant data is essentially unstructured, mainly images like MRI and clinical texts.

One of the challenges faced by doctors is finding patients and clinical cases that show particular similarities to a given case (similar symptoms, diagnosis, treatments, or other characteristics) amongst the rapidly growing amount of clinical records and medical publications and the complexity of the data. Detection of similarities among patients or groups of patients is key for evidence-based clinical practice, the selection of patients for clinical trials, prioritizing patients for vaccination and for understanding the variability in clinical outcomes.

From a COVID-19 point of view, AI tools should distinguish between patients with and with no risk of a severe outcome, so that clinicians could intervene promptly. Specifically, this task aims to promote the development of systems able to detect similarities among a collection of clinical case texts.

Proposing and collaborating: BSC.

Can we reduce the number of children (paediatric population) to whom the microbiological study, associated with additional health costs and usually annoying, should be performed? Defining a predictive value, grouped or individually, of the clinical signs and symptoms associated to the clinical suspicion of COVID-19 can help! Can you find the best mechanism (screening strategies and algorithms) to predict paediatric COVID-19? That is the challenge!

All children suspected to be infected by SARS-CoV-2 visited in any of the participating Primary Health-Care centres and Emergency Wards of the COPEDI-CAT project in Catalonia are included in the database. We collect demographic, epidemiological, clinical and microbiological diagnosis data from all these children.

The collection and analysis of this information are carried out from the already available COPEDI-CAT database on the REDCap © digital platform.

Proposing and collaborating: COPEDI-CAT Project, Vall d’Hebron, BIOCOM-SC

Cough is another feature in the symptoms of COVID-19. The sound of coughing can be unique when you want to distinguish it from another infectious or other process. At the UPC we want to help people find out what kind of cough they have and whether it is compatible with a SARS-cov-2 type infection. Do you accept the challenge?

Proposing and collaborating: CREB and CCD.

1. Could we automatise when contacting the close contacts of a COVID-19 case once the COVID manager enters the contact details of a Positive? In other words, could we somehow free our health system from informing close contacts that they are close contacts when necessary - due to overburdening triggered by a large amount of cases? Could we inform them that they have to be confined and that they have to be tested in a CAP as well as to let them know which CAP is the closest based on their address, and schedule a visit for them? Currently, a specific person makes the first contact, but it is not always possible if the system becomes overburdened.
2. Could we automatise the monitoring of an asymptomatic case, after having a positive PCR test/test in order to know if it is still well? Can we automatise the fact that an asymptomatic case has already done the days of confinement required and so we can discharge them if they have not symptoms? Generally, we could coordinate all these efforts and free our CAPs so that they can focus mainly on COVID cases with symptoms or ... non-COVID patients! Only when there is a case with symptoms an alarm system could alert the doctors/nurses. Because all these things, the challenge is ... could we create a system that automatises the entire process or part of the process depending on the overburdening of the system, from the first contact entered by the COVID manager to the discharge of the asymptomatic cases? That could save energy for everyone!