AI-based medical intelligence system, of surveillance, alert and analysis for CBRNE hybrid warfare. EMCAS (Azure AI/Palantir Foundry PaaS)
EMCAS, It arose seven years ago (2015), as a strategic reflexion of experimental design to expose to the civil and military organizations responsible for the global protection of citizens health, although I don´t have a crystal ball, at that time I considered and today for obvious reasons, that the main threats to the global population were invisible weapons (CBRNE Weapons), the fundamental idea is to put into play all the elements for the immediate and global deployment on the ground of socio-technical infrastructures for early warning, surveillance and intelligence of plagues and pandemics, using a common evidence-based medical intelligence.
The global objective is to create a set of mobile and interconnected units that allow to be strategically located and to be able to form a network of early warning, surveillance and epidemiological analysis adapted to the type of pandemic (type of infectious agent, expansion model, phase of the pandemic, etc).
Since then EMCAS has been undergoing a global review and we are currently designing the final model with military and civil specifications, especially aimed at integrating into the infrastructure of a smart city network. In this sense, I am evolving the system to a PaaS model, based at the moment on the capabilities of Azure AI/Palantir Foundry services.
Regarding innovations related to EMCAS, I would mainly highlight:
-The socio-technical conception of the AI-based PaaS infrastructure (Azure AI/Palantir Foundry services).
-The advanced evidence based analysis platform.
-The evidence-based case management methodology.
-The system for capturing and automatically weighing the reliability of the information.
-The AI system of automatic generation and treatment of plausible lines of research (automated reasoning secuences based on learned cases that I call "Thought Chains").
-The multidimensional medical pattern detection and analysis model.
The system incorporates a methodological, operational and technological model that from the functional point of view performs the functions described in the following figure:
Have an early warning and as well as an evaluation of the risk and the tendencies of climbing in real time.
The ability to make decisions based on real-time situational awareness provided by the capabilities to capture and manage the medical evidence obtained.
Detect and recognize multidimensional patterns from the analysis of the complex evidence network.
The generation & treatment of plausible research lines is focused on the available evidence and the automatic reasoning processes based on cases stored in a knowledge base.
The ability to estimate and control new ways of expansion of the health emergency, control of the infested population and adapt the monitoring system to the structure and size of the threat.
EMCAS proposes a general operational methodology of medical intelligence cases based on evidence.
Figure- Evidence Lab-V2 Methodology.
An approach to the Evidence-Lab-V2 is the following:
Define the problem:
•Who need to know what is happening?
•What do they need to know and argue with precision?
•Where do they need to know?
•When do they need to know in order to chronologically plan the project properly?
•Why do they need to know? and what actions are possible to carry out?
•How do they need to know, that is, the format? A dossier, a design, forensics, executive summary, visual diagram, report face to face.
•What levels and procedures of confidentiality are going to be applied?
Determine the requirements:
•What is happening? What is the problem?
•Who or what are the elements (factors, objects, etc.) to investigate?
•Where are we going to develop the investigation? and where are we going to get the information from?
•When and how does the phenomenon begin, develop and manifest?
•Why is this phenomenon occurring? That is, what are factors, internal or external to the customer, that cause the phenomenon?
Define the cases:
•Role and importance of the evidences (physical, documentary, statistical and analytical).
•Role and importance of the analysis and interpretation.
•Rol and importance of the strategy and decisions.
•Methodological approach.
•Information-gathering tasks involved.
•Analytical tasks involved.
•Information-gathering methods to use.
•Analytical methods to use.
•Strategic techniques to use.
•Information-gathering methods to use.
•Analysis tools to use.
•Simulation and decision-making tools to use.
•Case management tools to use.
•Case timelines.
If you are interested and want more information about this experimental design work, do not hesitate to contact me.
https://www.linkedin.com/pulse/ai-based-cbrne-intelligence-system-early-warning-paas-luis/
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