Processing text with domain-specific spreading activation methods

• US 8,930,178 B2
• Filed: 01/04/2008
• Issued: 01/06/2015
• Est. Priority Date: 01/04/2007
• Status: Active Grant

• Current Assignee: Children's Hospital & Medical Center, Cincinnati Children's Hospital Medical Center (University of Cincinnati)
• Original Assignee: Children's Hospital & Medical Center
• Inventors: Pestian, John, Matykiewicz, Pawel, Duch, Wlodzislaw, Glauser, Tracy, Kowatch, Robert A., Grupp-Phelan, Jacqueline
• Primary Examiner(s): Godbold, Douglas
• Assistant Examiner(s): ORTIZ SANCHEZ, MICHAEL
• Application Number:
  US12/006,813
• Publication Number:
  US 20080270120A1
• Time in Patent Office: 2,559 Days
• Field of Search: 704/1, 704/9
• US Class Current: 704/9
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 40/117 Tagging; Marking up details of markup languages G06F40/143; Designating a block; Setting of attributes style sheets, e.g. eXtensible Stylesheet Language Transformation [XSLT], G06F40/154
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/232 Orthographic correction, e.g. spell checking or vowelisation
• CPC Class Codes: G06F 40/237 Lexical tools
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Claims

1. One or more non-transitory electronic memory devices including computer instructions for performing a method for processing natural language, the computer instructions being configured to perform the steps of:

providing access to a clinical text, the clinical text including a plurality of groups of characters;

providing access to a first database and a second database, the first database including associations between a plurality of known words and a plurality of semantic concepts, and the second database including associations between a plurality of episodic concepts and at least one of the plurality of known words and the plurality of semantic concepts, the plurality of episodic concepts being separate from the plurality of semantic concepts;

identifying one or more of the plurality of groups of characters as corresponding to at least one of the plurality of known words;

creating a list of the identified known words;

querying the first database to obtain a set of one or more semantic concepts associated with each of the identified known words;

annotating the list of identified known words with the set of semantic concepts associated with each identified known word;

querying the second database to obtain a set of one or more episodic concepts associated with the set of semantic concepts;

creating a semantic network having a plurality of nodes corresponding to the sets of semantic and episodic concepts and weighted links between the sets of semantic and episodic concepts;

utilizing spreading activation algorithms to refine the weighted links in the semantic network; and

selecting at least one of the concepts from the sets of semantic and episodic concepts based upon an associated weight for the at least one node derived from the step of utilizing spreading activation.

2. The one or more non-transitory memory devices of claim 1, wherein the computer instructions are further configured to perform the step of preparing the clinical text prior to the identifying step, the step of preparing the clinical text including at least one of tagging parts of speech, replacing abbreviations with words, and correcting misspelled words.

3. The one or more non-transitory memory devices of claim 1, wherein the computer instructions are further configured to perform the step of providing an output including the selected at least one of the concepts.

4. The one or more non-transitory memory devices of claim 1, wherein the clinical text comprises clinical free text.

5. The one or more non-transitory memory devices of claim 4, wherein the clinical free text comprises pediatric clinical free text.

6. The one or more non-transitory memory devices of claim 1, wherein the clinical text comprises a plurality of documents and the computer instructions are further configured to perform the step of identifying a subset of the plurality of documents by identifying at least two documents having associations with the selected at least one of the concepts.

7. The one or more non-transitory memory devices of claim 6, wherein the computer instructions are further configured to perform the step of producing an output, the output including identification of one or more portions of each of the at least two documents having associations with the selected at least one of the concepts.

8. The one or more non-transitory memory devices of claim 1, wherein the clinical text comprises at least one suicide note and the method further comprises the step of evaluating the suicide note for concepts indicative of suicidal intent.

9. One or more non-transitory electronic memory devices including computer instructions for performing a method for processing natural language, the computer instructions being configured to perform the steps of:

providing access to a clinical text containing natural language;

tagging parts of speech in the clinical text;

recognizing known words in the clinical text;

creating a semantic network, the semantic network including at least one of the recognized known words and at least one relationship with at least one semantic concept associated with at least one of the recognized known words; and

supplementing the semantic network by iteratively adding additional concepts and additional relationships to the semantic network until a termination requirement is met, each additional concept being associated with at least a prior one of the concepts and additional concepts in the semantic network by a respective additional relationship, at least one of the additional concepts being an episodic concept separate from the at least one semantic concept.

10. The one or more non-transitory memory devices of claim 9, wherein the computer instructions are further configured to perform the steps of

weighting each of the at least one relationships and each of the additional relationships with a weighting value reflecting the strength of each relationship and additional relationship;

determining a minimum threshold weighting value; and

terminating the iterative growth of any network node in which the weighting between the relationships and the additional relationships do not satisfy the minimum threshold weighting value.

11. The one or more non-transitory memory devices of claim 10, wherein the computer instructions are further configured to perform the steps of

comparing the at least one semantic concept and the additional concepts to a list of known relevant concepts to generate a list of identified relevant concepts;

and

providing an output based on at least one of a number and a significance of the identified relevant concepts.

12. The one or more non-transitory memory devices of claim 11, wherein the output pertains to a probability of a particular occurrence.

13. The one or more non-transitory memory devices of claim 12, wherein the clinical text includes at least one suicide note and the particular occurrence is a suicide attempt.

14. The one or more non-transitory memory devices of claim 10, wherein the clinical text includes a plurality of documents and the computer instructions are further configured to perform the steps of

receiving a query including a search concept;

and

displaying a list of documents including one or more of the plurality of documents that is associated with the at least one semantic concept and the additional concepts that matches the search concept.

15. The one or more non-transitory memory devices of claim 14, wherein the list of documents is sorted by the weighting value pertaining to at least one relationship or additional relationship between the search concept and the corresponding recognized known word.

16. The one or more non-transitory memory devices of claim 9, wherein the one or more episodic concepts are uniquely associated with a patient'"'"'s prior clinical history.

17. One or more non-transitory electronic memory devices including computer instructions for performing a method for processing natural language, the computer instructions being configured to perform the steps of:

providing access to a clinical text, the clinical text including a plurality of groups of characters;

providing access to one or more databases, the one or more databases including associations between a plurality of known words and a plurality of concepts, and including quantitative values representative of a strength of a relationship between the plurality of concepts;

identifying one or more of the plurality of groups of characters as corresponding to at least one of the plurality of known words;

creating a list of the identified known words;

querying the one or more databases to obtain a first set of semantic concepts associated with each of the identified known words;

annotating the list of identified known words with the first set of semantic concepts associated with each identified known word;

creating a semantic network having a plurality of nodes corresponding to the first set of semantic concepts;

iteratively expanding the semantic network with additional concepts taken from the one or more databases and linked to respective nodes in the semantic network to iteratively add new nodes to the semantic network for such additional concepts, each new node including a weighted link with an existing node, the additional concepts being separate from the first set of semantic concepts and including at least one episodic concept; and

selecting at least one of the concepts from the combination of the first set of concepts and the additional concepts based upon a value of the weighted link included with the node associated with the at least one selected concept.

18. The one or more non-transitory memory devices of claim 17, wherein the step of iteratively expanding will continue until a termination requirement is met.

19. The one or more non-transitory memory devices of claim 18, wherein the termination requirement is a value of a weighted link falling below a predefined threshold.