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Massive reutilization of Electronic Health Records (EHRs) through AI to enhance clinical research and precision medicine

Descripción del proyecto

Aprovechar al máximo los datos de las historias clínicas electrónicas

Aunque se puede extraer información muy valiosa de las historias clínicas electrónicas (HCE), siguen sin poder aprovecharse porque no están estructuradas ni escritas con un lenguaje natural. El proyecto SAVANA, financiado con fondos europeos, permitirá a los profesionales sanitarios generar datos de la vida real, hacer nuevos descubrimientos, crear medicina personalizada y evaluar los desenlaces clínicos. Para ello, se creará una herramienta que utilizará el procesamiento del lenguaje natural para extraer datos de enormes cantidades de anotaciones de las HCE. Esta herramienta nueva cumplirá los requisitos de los comités de ética de los hospitales, las normativas de los servicios sanitarios nacionales y las políticas de la industria farmacéutica, y está destinada a gestores, hospitales e investigadores.

Objetivo

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.

Palabras clave

Convocatoria de propuestas

H2020-EIC-SMEInst-2018-2020

Consulte otros proyectos de esta convocatoria

Convocatoria de subcontratación

H2020-SMEInst-2018-2020-2

Régimen de financiación

SME-2 - SME instrument phase 2

Coordinador

MEDSAVANA SL
Aportación neta de la UEn
€ 943 358,07
Dirección
C/ JILOCA 4 PLANTA 5 DERECHA
28016 Madrid
España

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Pyme

Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.

Región
Comunidad de Madrid Comunidad de Madrid Madrid
Tipo de actividad
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Enlaces
Coste total
€ 2 408 995,78

Participantes (3)