Systems and methods for event stream platforms which enable applications
Systems and methods to generate a final event stream are provided. The system collects information from a wide variety of sources, and then parses, normalizes, and indexes the information. This generates an initial event stream that can be tagged and then iteratively processed to generate a final event stream. The processing includes first order logic querying and knowledge extraction to infer additional events which is added to the event stream. The final event stream is used by a knowledge exchange for consumption by applications. These applications may be internal applications and/or third party applications. This system may be particularly useful in use with medical information, or any other big data enterprise system.
1. In a Medical Information Navigation Engine, a computerized method for generating a final event stream, the method comprising:
collecting medical information from a plurality of data sources;
parsing the medical information;
normalizing the medical information;
indexing the medical information to generate an initial event stream, wherein an event includes a fact that is either true or false, occurs in a specific time interval for a specific subject, attributed to a source and episode grouping, and the event stream is a collection of such events;
semantic meta-tagging the event stream; and
iteratively processing the event stream to generate a final event stream, wherein the processing includes at least one of first order logic querying and knowledge extraction to infer at least one additional event which is added to the event stream by agents to provide a rich and expanded event stream for deriving clinical knowledge and downstream applications.
2. The method of claim 1 further comprising providing the final event stream to a clinical knowledge exchange.
3. The method of claim 2 wherein the clinical knowledge exchange transforms the final event stream for consumption by a plurality of applications.
4. The method of claim 3 wherein the applications include internal applications and third party applications.
5. The method of claim 1 further comprising performing optical character recognition on the data sources which are not machine readable.
6. The method of claim 1 wherein the processing is performed by at least one agent.
7. The method of claim 6 wherein the at least one agent includes an internal agent and a third party agent.
8. The method of claim 1 wherein the event comprises a subject, evidence used to infer the event, a fact that was inferred from the evidence, a start date of the event, an end date of the event, an episode level grouping, the source of the event, type of the event, named values, a snippet of the source, an event classification, and expiry information.
9. The method of claim 1 wherein the final event stream is one which does not change after additional iterative processing.
10. A computerized method of generating an event stream comprising:
extracting knowledge from a data source, using a processor, to generate a set of events, wherein each event includes a subject, evidence used to infer the event, a fact that was inferred from the evidence, a start date of the event, an end date of the event, an episode level grouping, the source of the event, type of the event, named values, a snippet of the source, an event classification, and expiry information; and
iteratively processing the plurality of events by agents to expand the set of events including inferred events that is added to event stream for deriving clinical knowledge and downstream applications.
11. A medical information system for generating a final event stream comprising:
a data pipeline, including a processor, configured to collect medical information from a plurality of data sources, parse the medical information, normalize the medical information and index the medical information to generate an initial event stream, wherein an event includes a fact that is either true or false, occurs in a specific time interval for a specific subject, attributed to a source and episode grouping, and the event stream is a collection of such events; and
a knowledge extractor configured to tag the event stream, and iteratively process the event stream to generate a final event stream, wherein the processing includes at least one of first order logic querying and knowledge extraction to infer at least one additional event which is added to the event stream by agents to provide a rich and expanded event stream for deriving clinical knowledge and downstream applications.
12. The system of claim 11 further comprising a clinical knowledge exchange configured to transform the final event stream to a format consumable by a plurality of applications.
13. The system of claim 12 wherein the applications include internal applications and third party applications.
14. The system of claim 13 further comprising internal applications.
15. The system of claim 11 wherein the data pipeline is further configured to perform optical character recognition on the data sources which are not machine readable.
16. The system of claim 11 wherein the processing is performed by at least one agent.
17. The system of claim 16 wherein the at least one agent includes an internal agent and a third party agent.
18. The system of claim 11 wherein the event comprises a subject, evidence used to infer the event, a fact that was inferred from the evidence, a start date of the event, an end date of the event, an episode level grouping, the source of the event, type of the event, named values, a snippet of the source, an event classification, and expiry information.
19. The system of claim 11 wherein the final event stream is one which does not change after additional iterative processing.