WILLEM: AI to Reduce Cardiovascular Diseases
Sector: Hospital • Location: Spain
Source: EU Funding & Tenders Portal
WILLEM is the first 100% automated cloud platform for electrocardiogram (ECG) analysis, designed to comprehensively identify and diagnose all types of arrhythmias and predict Cardiovascular Diseases (CVDs) behaviour at 6 months since its detection. WILLEM communicates users and hospitals-in real time through a unique Cloud Platform, integrable with any other eHealth platform as part
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | ended |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | WILLEM is the first 100% automated cloud platform for electrocardiogram (ECG) analysis, designed to comprehensively identify and diagnose all types of arrhythmias and predict Cardiovascular Diseases (CVDs) behaviour at 6 months since its detection. WILLEM communicates users and hospitals-in real time through a unique Cloud Platform, integrable with any other eHealth platform as part of the clinical workflow. WILLEM provides the best prospective and labelled ECG database and, as a hardware-agnostic platform, it uses breakthrough Artificial Intelligence (AI) models to transform raw ECG signals from any monitoring device into a medical grade ECG report. Today, WILLEM’s AI classifies 73 arrhythmias of the 288 known cardiac patterns, more than 90% of the cases, and it is the only solution that predicts Atrial Fibrillation. The goal is to classify every arrythmia present in human biology and to predict the 6 most prevalent heart diseases in an automatic and non-supervised way to reduce CVDs. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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Article Published Date | Obfuscated Data |
