Multi-source heterogeneous data integration for incident likelihood analysis

Integrates unstructured incident information (via NLP) with structured operational data to estimate incident likelihood and identify sensitive management/organizational drivers. For Management of Change, this is relevant because changes can shift operating envelopes, maintenance patterns, or organizational interfaces in ways that affect incident likelihood. A multi-source model that fuses historical incident narratives with current operating context can provide additional evidence for MOC risk reviews, especially for recurring change types (setpoint changes, equipment substitutions, staffing/role changes) where the risk impact is subtle or distributed.