Abstract:
Some known medical terms may function as non-medical terms depending on their particular context. Accordingly, the present inventors devised systems, methods, and software that facilitate determining whether a term that is found in a medical corpus is likely to be a medical term when found in another corpus. An exemplary embodiment receives a term and computes an ambiguity score based on language models for a medical and a non-medical corpus.

Description:
CROSS-REFERENCE TO RELATED APPLICATION  
       [0001]     This application claims priority to U.S. provisional application 60/723,483 filed on Oct. 4, 2005. The provisional application is incorporated herein by reference. 
     
    
     COPYRIGHT NOTICE AND PERMISSION  
       [0002]     A portion of this patent document contains material subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyrights whatsoever. The following notice applies to this document: Copyright © 2005-2006, Thomson Global Resources.  
       TECHNICAL FIELD  
       [0003]     Various embodiments of the present invention concern systems, methods, and software for identifying medical content in documents and linking those documents to other documents based on the medical content.  
       BACKGROUND  
       [0004]     The fantastic growth of the Internet and other computer networks has fueled an equally fantastic growth in the data accessible via these networks. One of the seminal modes for interacting with this data is through the use of hyperlinks within electronic documents.  
         [0005]     Hyperlinks are user-selectable elements, such as highlighted text or icons, that link one portion of an electronic document to another portion of the same document or to other documents in a database or computer network. With proper computer equipment and network access, a user can select or invoke a hyperlink and almost instantaneously view the other document, which can be located on virtually any computer system in the world.  
         [0006]     Although many hyperlinks are created and inserted into documents manually, recent years have seen development of automated techniques for identifying specific types of document text and linking the identified text using hyperlinks to other related documents. For example, to facilitate legal research, the Westlaw legal research system automatically identifies legal citations and attorney names in text and links the citations to corresponding legal documents in a database and the attorney names to biographical entries in an online directory. For further details, see U.S. Pat. No. 7,003,719 and U.S. Published Patent Application US2003/0135826A1, both of which are incorporated herein by reference.  
         [0007]     Although the automated linking technology in the Westlaw system is highly effective for legal citations and names, the present inventors have identified that this technology is not well suited for other types of content, such as medical terms. For example, the inventors recognize that identifying legal citations and entity names within a text is generally simpler than identifying medical terms because terms may function as medical terms in one context and as non-medical terms in another. Legal citations and person names, on the other hand, generally function as legal citations and person names regardless of context.  
         [0008]     Accordingly, the present inventors have identified a need for automated methods identifying whether terms are medical terms or non-medical terms.  
       SUMMARY  
       [0009]     To address this and/or other needs, the inventors devised, among other things, systems, methods, and software that facilitate determining whether a term is a medical term or a non-medical term. 
     
    
     BRIEF DESCRIPTION OF DRAWINGS  
       [0010]      FIG. 1  is a block diagram of an exemplary system  100  which corresponds to one or more embodiment of the present invention.  
         [0011]      FIG. 2  is a flow chart of an exemplary method of operating system  100  which corresponds to one or more embodiments of the invention. 
     
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS  
       [0012]     The following detailed description, which references and incorporates  FIGS. 1 and 2 , describes and illustrates one or more exemplary embodiments of the invention. These embodiments, offered not to limit but only to exemplify and teach the invention, are shown and described in sufficient detail to enable those skilled in the art to make and use the invention. Thus, where appropriate to avoid obscuring the invention, the description may omit certain information known to those of skill in the art.  
       Exemplary Computer System Embodying the Invention  
       [0013]      FIG. 1  shows a diagram of an exemplary computer system  100  incorporating a system, method, and software for assessing the ambiguity of terms, such as medical terms. Though the exemplary system is presented as an interconnected ensemble of separate components, some other embodiments implement their functionality using a greater or lesser number of components. Moreover, some embodiments intercouple one or more the components through wired or wireless local- or wide-area networks. Some embodiments implement one or more portions of system  100  using one or more mainframe computers or servers.) Thus, the present invention is not limited to any particular functional partition.  
         [0014]     Generally, system  100  includes input terms  110 , term-ambiguity calculator  120 , and ambiguity scores output  130 .  
         [0015]     Input terms  110  includes one or more terms, such as a set of terms from a medical database. In the exemplary embodiment, input terms  110  includes terms from the Unified Medical Language System (UMLS). The table below shows that UMLS includes a great number of terms in disease, injury, medical procedure, body part, and drug categories.  
                                                                         Category   Terms   Concepts                                        Disease   189,712   69,948           Injury   42,141   28,997           Medical   134,179   72,918           procedure           Body part   38,041   22,260           Drugs   244,752   129,959                      
 
 In some embodiment, input terms  110  are terms extracted from one or more input documents, such as an electronic judicial opinion. or other type legal document. 
 
         [0016]     Coupled to database  110  is term-ambiguity calculator  120 . Calculator  120  includes one or more conventional processors  121 , display device  122 , interface devices  123 , network-communications devices  124 , and memory  125 . Memory  125 , which can take a variety of forms, such as coded instructions or data on an electrical, magnetic, and/or optical carrier medium, includes term-ambiguity software  126 . Term-ambiguity software  126  includes various software and data components, for determining or calculating for each input term t and ambiguity score, Score(term) defined as  
         Score   ⁢           ⁢     (   term   )       =         λ   1     ⁢           ⁢       log   ⁡     (     P   ⁡     (     t   |   News_lang     )       )         log   ⁡     (     P   ⁡     (     t   |   UMLS_lang     )       )           +       λ   2     ⁢       log   ⁡     (     P   ⁡     (     t   |   Legal_lang     )       )         log   ⁡     (     P   ⁡     (     t   |   UMLS_lang     )       )                 
     where     
         log   ⁡     (     P   ⁡     (     t   |   lang     )       )       =       ∑     i   =   1     n     ⁢     log   ⁡     (     P   ⁡     (     ngram   |   lang     )       )             
 
 and lambda 1  and lambda 2  are constants, which in some embodiments are used to normalize or smooth the scoring function. In some embodiments, lambda 1  and lambda 2  are set to 0.5. The exemplary embodiment uses ngram backoff with Witten Bell smoothing to smooth the language models. 
 
         [0017]     The exemplary scoring function is based on the intuition that medical ngrams, such as “hepatic,” occur relatively more often in UMLS than in news or legal and that ngrams such as “drinki” will occur relatively more often in news or legal than in UMLS. Terms having ngrams that are more highly predicted by UMLS than news or legal tend to yield a larger score and thus indicate that the given term is more likely a medical term than not a medical term when found in a news or legal document.  
         [0018]     Term-ambiguity calculator  120  outputs a set  130  of one or more ambiguity scores based on the input terms. ( FIG. 1  shows that the input terms  110  and output scores  130  are also retained in memory  130 .) In the exemplary embodiment, the scores are output as a ranked list, with each score associated with corresponding terms. (Note that term may include one or more words.)  
         [0019]     The ambiguity scores can be used for a variety of purposes, including for example determining whether it is appropriate to insert a link in a document including a given term back to a ULMS document associated with the term. For example, in the output terms shown the terms having an ambiguity score greater than 1.5 may be considered as clearly being medical terms and thus linked with high confidence back to related ULMS documents. On the other hand, terms such as “word salad” or “anticipatory vomiting” that have lower scores should not generally be linked back to a related ULMS document without contextual corroboration.  
         [0020]     Exemplary Operation of System  100   
         [0021]      FIG. 2  shows a flowchart  200  illustrating an exemplary method of operating system  100 . Flow chart  200  includes process blocks  210 - 230 . Though these blocks (and those of other flow charts in this document) are arranged serially in the exemplary embodiment, other embodiments may reorder the blocks, omit one or more blocks, and/or execute two or more blocks in parallel using multiple processors or a single processor organized as two or more virtual machines or subprocessors. Moreover, still other embodiments implement the blocks as one or more specific interconnected hardware or integrated-circuit modules with related control and data signals communicated between and through the modules. Thus, this and other exemplary process flows in this document are applicable to software, firmware, hardware, and other types of implementations.  
         [0022]     Block  210  entails receiving a set of terms. In the exemplary embodiment, this entails receiving a set of terms from ULMS or an input news or legal document into memory  126  of term-ambiguity calculator  120 . Execution continues at block  220 .  
         [0023]     Block  220  entails determining one or more ambiguity scores for one or more of the input terms. In the exemplary embodiment this entails computing ambiguity scores according to the definition set forth above for Score(term) in equation above, which provides a sum of two conditional probability ratios. Each conditional probability is based on language model of set or corpus of documents. In some embodiments, one of the conditional probability ratios is omitted from the scoring function. Also, in some embodiments, the conditional probability ratios are inverted.  
         [0024]     Block  230  entails outputting one or more of the determined ambiguity scores. In the exemplary embodiment, this entails outputting in printed or other human readable form; however, in other embodiments, the output may also be used by another machine, component, or software module, or simply retained in memory.  
       CONCLUSION  
       [0025]     The embodiments described above are intended only to illustrate and teach one or more ways of practicing or implementing the present invention, not to restrict its breadth or scope. The actual scope of the invention, which embraces all ways of practicing or implementing the teachings of the invention, is defined only by the following claims and their equivalents.