Medical Entity Extraction From Patient Data

• US 20080228769A1
• Filed: 03/13/2008
• Published: 09/18/2008
• Est. Priority Date: 03/15/2007
• Status: Abandoned Application

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Niculescu, Radu Stefan, Rao, R. Bharat, Lita, Lucian Vlad, Raileanu, Ciprian Dan
• Application Number:
  US12/047,416
• Publication Number:
  US 20080228769A1
• Time in Patent Office: Days
• US Class Current: 707/6
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Members of a medical entity class are extracted from patient data. A semi-supervised approach uses one or more initial medical terms such as terms from an ontology, for a given category or medical canonical entity. A larger set of medical terms is extracted from the medical information. In one example, the extraction is performed using lexical surface form features, rather than syntactical parsing.

Claims

1. A system for extracting members of a medical entity class from patient data, the system comprising:

an input operable to receive identification of at least a first member of the medical entity class;

a processor operable to extract at least a second member of the medical entity class from the patient data, the extraction being a function of the first member, the extraction being a semi-supervised process operable to identify the second member from the patient data comprising data for a plurality of patients, at least some of the data subjected to the semi-supervised process being free text with medical information related to symptoms, medication, test result, condition, disease, or combinations thereof; and

a display operable to output a listing of members of the medical entity class, the members comprising the at least first member and the at least second member extracted by the processor as a function of the first member.

2. The system of claim 1 wherein the free text comprises natural language information from a medical professional, the information including a misspelling, non-grammatical format, different formats, or combinations thereof.

3. The system of claim 1 wherein the processor or another processor is operable to learn from the patient data a model for determining a patient state, the learning being a function of the members, and wherein the display or another display is operable to output the patient state for at least one patient.

4. The system of claim 1 wherein the semi-supervised process uses lexical surface form features.

5. The system of claim 4 wherein the semi-supervised process identifies the second member as being in a list with the first member.

6. The system of claim 4 wherein the semi-supervised process identifies the second member as being in a similar contextual pattern as the first member.

7. The system of claim 5 wherein the semi-supervised process identifies a third member as being in a similar contextual pattern as the first member.

8. The system of claim 1 wherein the processor is operable to extract at least a third member as a function of the second member in an iteration of the semi-supervised process performed after extracting the second member, and wherein the processor is operable to deselect at least one of the second and third members from the listing as a function of a heuristic.

9. The system of claim 1 wherein the semi-supervised process is free of syntactical parsing.

10. The system of claim 1 wherein the second member comprises a rephrasing of the first member, the medical entity class comprises a canonical entity, and the listing of members is different for different datasets from respective different medical institutions, the different datasets associated with different numbers of patients.

11. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for identifying a set of words or phrases for a canonical entity, the instructions comprising:

receiving at least one initial word or phrase;

identifying the set with lexical surface form features from free text without syntactical parsing of the free text, the identifying being a function of the at least one initial word or phrase; and

outputting the set.

12. The computer readable storage medium of claim 11, wherein the at least one initial word or phrase comprises a first plurality of medical terms, and wherein the identifying comprises identifying a second plurality of medical terms with similar context as the medical terms of the first plurality in the free text, the free text comprising medical transcripts.

13. The computer readable storage medium of claim 11 wherein identifying with lexical surface form features comprises identifying a list including the at least one initial word or phrase as a function of commas and a conjunction term, the set being populated with the at least one initial word or phrase and other words or phrases in the list.

14. The computer readable storage medium of claim 11 wherein identifying with lexical surface form features comprises:

identifying a prefix phrase, a suffix phrase, or both in a clause delimited by punctuation and including the at least one initial word or phrase, and

identifying other words or phrases with a same or similar prefix phrase, suffix phrase or both in a clause delimitated by punctuation, the other words or phrases being added to the set.

15. The computer readable medium of claim 11 further comprising:

iteratively performing the identifying with each iteration using the set from a previous iteration as the at least one initial word or phrase; and

selecting a subset of words or phrases identified by the identifying as words or phrases of the set, the selecting being a function of a frequency ratio.

16. The computer readable medium of claim 11 wherein the identifying is a semi-supervised operation.

17. A method for extracting members of a medical canonical entity from patient data including free text, the method comprising:

receiving the free text as natural language information from medical professionals for a plurality of patients, the information including a misspelling, non-grammatical format, different formats, or combinations thereof;

receiving one or more seed medical terms, the one or more seed medical terms comprising one or more members of the medical canonical entity;

determining context for the one or more seed medical terms in the free text, the determining being free of syntactical parsing;

identifying additional medical terms as a function of the context in the free text; and

generating a list of the members of the medical canonical entity as at least some of the additional medical terms and the seed medical terms.

18. The method of claim 17 wherein determining the context comprises identifying a string of terms including at least one of the one or more seed medical terms as a function of commas and a conjunction term, and wherein identifying the additional medical terms comprises identifying other ones of the terms of the string.

19. The method of claim 17 wherein determining comprises identifying a prefix phrase, a suffix phrase, or both in a clause delimited by punctuation and including at least one of the one or more seed medical terms, and wherein identifying comprises identifying the additional medical terms as having a same or similar prefix phrase, suffix phrase or both in a clause delimitated by punctuation.

20. The method of claim 17 further comprising:

iteratively performing the determining and identifying with each iteration using the additional medical terms from a previous iteration as the seed medical terms; and

selecting a subset of the additional medical terms identified in each iteration as a function of frequency ratios of the additional medical terms.

21. The method of claim 17 wherein generating the list comprises generating the list with a precision of at least about 0.90 through five iterations.