STORING STRUCTURED AND UNSTRUCTURED CLINICAL INFORMATION FOR INFORMATION RETRIEVAL

• US 20160371375A1
• Filed: 06/14/2016
• Published: 12/22/2016
• Est. Priority Date: 12/12/2011
• Status: Active Grant

• Current Assignee: Cleveland Clinic Foundation
• Original Assignee: Cleveland Clinic Foundation
• Inventors: Sasidhar, Madhu
• Granted Patent: US 10,445,378 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/319 Inverted lists
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/63 for local operation

Abstract

A computer-implemented method can include acquiring data from at least one data source, the acquired data including health data for a patient. The acquired data can be transformed into episode model data according to a context-specific data model and the episode model data can be stored in a database. The method also includes generating at least one inverted index document for at least a portion of an episode for the patient based on the episode model data.

Claims

1-20. -20. (canceled)

21. A computer-implemented method, comprising:

acquiring data from at least one data source, the acquired data including health data comprising a plurality of data objects for at least one patient;

transforming the acquired data into episode model data according to a context-specific data model and storing the episode model data in a database;

generating at least one inverted index document for at least a portion of an episode for the patient based on the episode model data, the generating comprising;

determining a respective qualified name for selected data objects in the episode model data according to the context-specific data model; and

adding the respective qualified name to each of the selected data objects in the inverted index document;

searching the at least one inverted index document based on a query that includes at least a portion of a corresponding qualified name; and

storing results data that includes search results in response to the searching.

22. The method of claim 21, wherein the at least one inverted index is generated at a scheduled time interval.

23. The method of claim 21, wherein the searching further comprising executing one or more queries periodically at a predetermined time interval or in response to an event trigger.

24. The method of claim 23, further comprising

evaluating the results data;

and

providing an alert based on the evaluation indicating the occurrence of a predetermined alert condition.

25. The method of claim 21, wherein the transforming further comprises:

segmenting the acquired data into temporal segments of the episode relative to a predefined base time, each of the temporal segments spanning a time period; and

adding a temporal identifier to selected data objects in each inverted index document to indicate which of the temporal segments each of the selected data objects belongs.

26. The method of claim 25, wherein the temporal identifier comprises a temporal prefix of a qualified name that is pre-pended to each of the selected data objects, according to a predefined schema, to identify a time segment for the selected data objects relative to a time when the inverted index document is generated.

27. The method of claim 21, wherein the qualified name comprises at least two of a temporal identifier specifying one of a plurality of time segments for the episode of care in which each of the selected data objects has been determined to reside, a field name providing a descriptor to represent information content for each of the selected data objects, and a data type indicating a predefined characteristic for data values stored in each of the selected data objects.

28. The method of claim 21, wherein the generating at least one inverted index document further comprises removing stop words from predetermined types of data objects in the episode model data and semantically expanding the predetermined types of the data objects in the episode model data to provide modified versions thereof in the inverted index document.

29. The method of claim 21, further comprising:

computing a score for the search results in the results data, and

ranking the search results in an order based on the computed score for each of the search results.

30. The method of claim 29, wherein the query comprises a plurality of search terms selected according to the data model, the method further comprising assigning a relative weight to each of the plurality of search terms such that the order of the search results varies depending on the relative weighting of the search terms.

31. The method of claim 30, wherein the relative weight is assigned in response to a user input.

33. A non-transitory machine readable medium having instructions executable by a processing resource, the instructions comprising:

a data converter programmed to access health data comprising a plurality of data objects from at least one data source and transform the accessed data to episode data for a given patient based on a data model that defines at least one of structure and content for storing the episode data in a database for an episode of care for the given patient; and

an index generator programmed to;

generate an inverted index document based on the episode data;

determine a name for each of a plurality of selected data objects in the episode data according to the data model; and

add the name to each of the plurality of selected data objects in the inverted index document;

a search engine programmed to;

query the inverted index document to search the at least one inverted index document, the query comprising at least one search term corresponding to at least a portion of the name associated with at least one of the plurality of selected data objects; and

provide search results in response to query.

34. The medium of claim 33, wherein the data converter is programmed to determine the name that is assigned to each of the plurality of data objects as a qualified name, the qualified name being added to each of the selected data objects in the inverted index document according to a schema.

35. The medium of claim 34, wherein the data converter further comprises:

a temporal segmenting function to segment the accessed data into time segments relative to an event; and

a prefix generator to generate a temporal prefix, corresponding to a portion of the qualified name, to specify one of the time segments for each of the selected data objects.

36. The medium of claim 35, wherein the event is a time when the inverted index document is generated.

37. The medium of claim 35, wherein the data converter further comprises:

a field naming function to determine a field name for each of the selected data objects according to the data model; and

a field typing function to determine a data type for each of the selected data objects according to the data model,

wherein the index generator adds the qualified name to each of the selected data objects in the inverted index document based on the temporal prefix, the field name and the data type determined for each respective data object.

38. The medium of claim 33, wherein the search engine is programmed to execute one or more queries periodically at a predetermined time interval or in response to an event trigger.

39. The medium of claim 33, further comprising an alert generator to evaluate the results data and provide an alert based on the evaluation indicating the occurrence of a predetermined alert condition.

40. The medium of claim 33, wherein the index generator further comprises a semantic expansion function to expand to include an expanded set of values in the inverted index document that is generated, and a stop word removal function to remove predetermined stop words from of data objects that store a predetermined type of data in the database such that the predetermined stop words are omitted from the inverted index document that is generated.