Descrizione del progetto
Sfruttare al massimo i dati delle cartelle cliniche elettroniche
Sebbene si possano estrarre informazioni estremamente preziose dalle cartelle cliniche elettroniche, queste ultime rimangono inutilizzate a causa della loro mancanza di struttura e del fatto che sono scritte in un linguaggio naturale. Il progetto SAVANA, finanziato dall’UE, consente ai professionisti sanitari di generare prove reali, effettuare nuove scoperte, creare medicina personalizzata e valutare gli esiti sanitari. A tal fine, creerà uno strumento che impiega l’elaborazione del linguaggio naturale per estrarre dati da ingenti quantità di narrazioni cliniche provenienti dalle cartelle elettroniche. Il nuovo strumento soddisferà i requisiti dei comitati etici ospedalieri, le norme dei servizi sanitari nazionali e le politiche dell’industria farmaceutica ed è rivolto a dirigenti, ospedali e ricercatori.
Obiettivo
In the last twenty years, the average return on R&D expenditure in the pharma industry has dropped from almost 18% to 3.7%. Moreover, annual funding for biomedical research has more than doubled while new drugs approvals have declined by one third. There is a wide consensus that the main cause of this problem is the exhaustion of a model intended to develop ‘broad indications’ and the need for a new ‘precision medicine’ model. We simply do not know enough about the underlying disease mechanisms involved, and more research is required to develop better disease classifications, which will enable a more targeted development approach for drugs and therapies.
Electronic Health Records (EHRs) has been used for more than ten years in most developed countries, and they gather now exhaustive clinical information of millions of patients. Leveraging EHRs could accelerate clinical research, and improve healthcare quality.
However, in order to uncover unknown disease models from EHRs, precision medicine requires massive research studies on thousands of patients (often in several countries). Currently there is no tool capable of: 1) automating the extraction of data from EHRs, and also, solving the privacy concerns raised by EHRs.
SAVANA RESEARCH uses Natural Language Processing to extract data from massive amounts of EHRs’ clinical narratives. It has the following advantages intended to make a leap in clinical research efficiency: 1) It uses only de-identified clinical records and ensures state of the art technologies to protect data privacy; 2) It is capable of decoding ten times more EHRs in half of the time; 3) It is capable of identifying 100 times more variables from EHRs; 4) And it costs 40% less.
The application of NLP to healthcare is a fast-growing market that is expected to reach 2.65 billion by 2021, by growing at a CAGR of 20.8%. SAVANA RESEARCH’s target markets are primary Europe and North America, which together comprises 75% of all clinical trials worldwide.
Campo scientifico
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesdata sciencenatural language processing
- natural sciencescomputer and information sciencessoftwaresoftware development
- medical and health scienceshealth sciencespersonalized medicine
- medical and health sciencesclinical medicinehepatology
Programma(i)
Argomento(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-SMEInst-2018-2020-2
Meccanismo di finanziamento
SME-2 - SME instrument phase 2Coordinatore
28016 Madrid
Spagna
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.