| KEYWORD |
Extraction and identification of information from Mass Spectra of the breath of patients infected with SARS-CoV-2
Tesi esterna in azienda
Parole chiave ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, SARS/COV2
Riferimenti GIOVANNI SQUILLERO
Riferimenti esterni Raffaele Correale (NANOTECH Srl), Nicolò Bellarmino (Politecnico)
Gruppi di ricerca DAUIN - GR-05 - ELECTRONIC CAD & RELIABILITY GROUP - CAD
Tipo tesi RESEARCH THESIS WITH A COMPANY
Descrizione Nowdays, the entire world is still facing the COVID-19 pandemic. Get the virus under control is the first goal in order to return to a normal life. Epidemiological studies show that the spreading of the virus is possible from an infected person’s mouth or nose liquid. It is important to identify COVID-19 clusters (a group of people positive to COVID-19 test) and isolate, in orther to avoid the virus spread. Mass screening is useful to test many people in the shortest time, to identify positive patients. Different kind of tests are available on the market, mostly known as invasive. Invasive tests can be difficult to manage due to the necessity of medichal assistants and, in addition to that, if a person needs to be testes many times it can be stressful or dangerous too. The solution, proposed by NanoTech Analysis S.r.l. (NTA), is a non-invasive breath study. The object of the thesis is the analysis of COVID patients' exhalations, in order to identify appropriate information in the complex mass spectra and correlate the readings to the different cases and specific situations. Preliminary studies are encouraging, and the goal is to build a ML model with a reliability grade (probability of correct identification and data classification) that is acceptable for commercial applications. The candidate will be supported (as needed) by electronic experts, physicians and physicists.
Conoscenze richieste Required: Python, ML
Optional (useful): Scikit-learn, NN, PyTorch
Scadenza validita proposta 31/08/2022
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