MedicalAI-based platform for anticipating potential biological threats and mitigating the COVID-19 crisis
Medical Intelligence Platform (MIP) provides information analysts with a complete solution to acquire, analyze, search and explore billions of data points related to biological risks.
MIP leverages Artificial Intelligence based on full Natural Language Understanding to quickly identify new emerging crises like COVID-19 and other biological threats (Digital Disease Detection), mitigate risks and support decision making through case monitoring and analysis of medical discoveries, social and business impacts and citizen emotions.
2020欧冠比赛时间MIP performs deep and wide analysis of unstructured data sets, extracting and highlighting real-time events linked to the crisis. It organizes results by category based on medical ontologies and taxonomies (MeSH, Medical Subject Headings, SNOMED CT, Media Topics and other taxonomies) for exploring large datasets using medical data scientist perspectives, and monitors open sources and public opinion to understand sentiment and emotions around the crisis.
Understand, categorize, aggregate
2020欧冠比赛时间Thanks to Natural Language Understanding, analysts can read and identify information contained in any document and understand context, concepts, entities, relationships and automatically categorize across any taxonomy. MIP provides access to the most up-to-date information about the situation and makes it more findable, in order to mitigate risks and discover more information, faster, with greater accuracy.
Real-time monitoring and horizon scanning
2020欧冠比赛时间Receive proactive alerts triggered by relevant news from sources on the analyst-defined target. Facilitate scanning for emerging threats and issues, detect weak signals related to over-the-horizon biological threats and monitor public opinion
Searching and exploring
MIP discovers significant clusters (a combination of symptoms/clinical analyses/temporal and territorial correlations), combining the data that emerges from open sources and social networks (Digital Disease Detection), from health system channels (patient medical records, 911 calls, etc.) and from scientific and academic literature.