Healthcare Causal AI
Use mathematical AI, Bayesian networks, and causal reasoning to support healthcare decisions under uncertainty.
Healthcare decision intelligence
Healthcare teams make decisions with incomplete information, competing risks, and consequences that matter.
Bayes Server provides a mathematical SDK and user interface for diagnostics, guided questioning, risk assessment, health monitoring, causal analysis, and decision support.
Use artificial intelligence guided Q&A, to iteratively collect information on many possible symptoms and reduce uncertainty about many possible diseases. Systems can be built using expertise from multiple experts and also incorporate data. Bayes Server can automatically determine the best questions to ask.
Make rational, cost-based decisions when there is uncertainty and incomplete information. Support prescriptive decisions in complex domains containing many symptoms, diseases, and treatments with associated costs.
Use causal models, causal inference & causal optimization, to quantify cause and effect relationships and make optimal causal aware decisions based on real world data. Causal Artificial Intelligence is used by policy and decision makers such as governments and healthcare executives.
Use advanced anomaly detection techniques to monitor complex sensor data from multiple sources. Trace the root cause, and the impact of different sensors
Move beyond Fault Trees, Event Trees, Bow-Tie models & FMECA toward sophisticated risk analysis approaches with Bayes Server.