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Exploiting Semantic Structure for Mapping
                                     Clinician‐specified Form Terms to SNOMED CT Concepts
                                            Ritu Khare1,3, Yuan An3, Jiexun Li3, Il‐Yeol Song3, Xiaohua Hu3, Michele Follen1,2
                                                       , Yuan An Jiexun          Il‐Yeol        Xiaohua      Michele Follen ,
                                                 College of Medicine Center for Women’s Health Research 1, and Obstetrics and Gynecology2 , College of Information Science and Technology3


                          Motivation, Problem, and Challenges                                                                                                                                                 Structure‐based SNOMED‐CT Mapping Framework

The elements of clinical databases are usually named after the clinical terms
used in various design artifacts. These terms are instinctively supplied by the                                                    Form                      Semantic                    Semantic
users, and hence, different users often use different terms to describe the same                                                   X                       Information 
                                                                                                                                                                                                                           Training Data
                                                                                                                                   Y
                                                                                                                                                                                         Form Tree
clinical concept. This term diversity makes future database integration and                                                                                 Extraction
analysis a huge challenge.
                                                                                                                                                                                                                                                          Semantic                 SNOMED CT
                                                                                                                                                                                        Semantic                    Structure –based                                                 Category
            Terms                                                                                                                                                                                                                                         Category                                                   SNOMED CT 
                                                                              SNOMED                                                          Form Term                                 Structure                     Classification                                                 Specific
         (in Clinical                                                                                                                                                                                                                                      Picker                                                      Concept
                                     Mapping/                                    CT                                                                                                     Analyzer                         Model                                                     Mapping (API)
           Forms)                                                                                                                                                                                                                                       (configurable)
                                  Standardization                             Concepts

                                                                                                                                                                            Fig. 3. Overall Mapping Framework: (1) The form tree structure is analyzed to derive the form context, (2) The
  Patient History Form
                                                Diversity Challenge                 Context Challenge                                                                       classification model (Naïve Bayes) ranks the SNOMED CT semantic categories suitable for the form context, (3) A
   PATIENT                                       (Well Addressed)                    (Less Explored)                                                                        category is picked, (4) The most linguistically matching concept in this category is selected as the winner concept.
    Name:
    Gender: M        F
                                                Different clinicians           The same form
                                                                                                                                                                                     Exploit the local semantic structure of form tree                         Select a winner semantic category , and map the
     DOB:                MRN:                   specify different              term when used in                                                                                                                                                               term to the linguistically matching concept within
                                                                                                                                                                                     to determine the term context, and candidate                 Key Ideas
    HISTORY                                     form terms to                  different contexts,                                                                                   SNOMED CT semantic categories.                                            the determined semantic category.
    Chief                                       specify the same               may      map    to
    Complaints
                                                 l     l
                                                clinical concept.              d ff
                                                                               different SNOMED                            How
                                                                                                                           H         can     we
   Review of Systems:
                                       e.g.,                                   CT concepts.                                leverage          the                                                                                            Results and Contributions
          Eyes                         MRN, or Med.Rec.#. e.g.,    the    term                                             semantic structure                                               Empirical Study with Clinician‐designed Forms
         ENMT                          VitalSigns,        Respiratory in Fig. 1                                                                                                                                                                                                                     Future Work
         Respiratory                   Constitutional, or and 2.                                                           of clinical forms to                       About the Data                                                   About the Methods                                           Leverage other relationships of
                                       Physical status                                                                     map the form terms                         The data includes 26 forms collected from 5                      BASELINE: Linguistic comparison                             SNOMED CT and test with other
     Fig 1. A Sample Clinician Designed Form                                                                               into         standard                      healthcare institutions. The forms contain                       HYBRID: Linguistic as well as
                                                                                                                                                                                                                                                                                                   vocabularies from the UMLS.
                                                                                                                           SNOMED             CT                      over 1500 terms, out of which 954 (63%) are                      Structural          (Contextual)                            Test within larger frameworks
                                                                                                                                                                      mappable to SNOMED CT concepts.                                  comparison (See Fig. 3)                                     of health information systems.
               Preliminaries: SNOMED CT and Semantic Form Trees
               Preliminaries: SNOMED CT and Semantic Form Trees                                                            concepts?
                                                                                                                                                    Mapping Precision                                                                  HYBRID++: Linguistic as well as                             Apply     other    classification
The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is a                                                                 0.89         0.92
                                                                                                                                                                     0.87
                                                                                                                                                                                                                                       advanced structural comparison                              techniques      and      employ
                                                                                                                                                    0.89                                    0.84
widely used medical terminology. It comprises 360,000 clinical CONCEPTS                                                                                            0.76     0.73
                                                                                                                                                                                  0.78
                                                                                                                                                                                          0.72
                                                                                                                                                                                                                                                                                                   sophisticated          linguistic
                                                                                                                                     0.69
belonging to various SEMANTIC CATEGORIES. Each concept is represented using                                                                      0.63         0.64             0.65    0.66                                                                                                        techniques.
a CONCEPT ID and a FULLY SPECIFIED NAME. A simple search for the term Eyes                                                         0.51
                                                                                                                                                                                                                                                 Findings                      Implications
across the UMLS SNOMED CT browser leads to the following top results:                                                                                                                                                                            Improvement due to
                                                                                                                                                                                                   90                                                                           Structural Knowledge
                 Concept Id     Fully‐specified Name               Semantic Category                                                                                                                                               Precision     structure (Fig 4)              has the ability to
                                                                                                                                                                                                   80
                 63342001       Sunsetting eyes                    Finding                                                                                                                                                                        (R = recall, P=Precision)     address the context             Conclusion
                                                                                                                                                                                                                                   Recall
                 371110006      Immature eyes                      Disorder                                                         Set1            Set2          Set3        Set4       Set5
                                                                                                                                                                                                   70                                            Hybrid over Baseline:          challenge,       and
                                                                                                                                                                                                                                                 18% (P); 2%(R)                                                 It is desirable to
                 362508001      Both eyes, entire                  Body Structure                                                                  Baseline          Hybrid      Hybrid++          60                              Precision                                    improve the overall             develop         hybrid
                                                                                                       Observable
                                                                                                                                                                                                                                   with Term     Hybrid++ over Hybrid:          mapping
                                                     Person                       Procedure            Entity                                                                                      50
                                                                                                                                                                                                                                   Processing
                                                                                                                                                                                                                                   Recall with   16% (P); 23%(R)                                                approaches that can
  Patient Examination Form                                            root                                                                                 0.74    Mapping Recall                                                  Term                                         performance.                    address    both    the
                                                                                                                                                        0.69                                       40                              Processing
   PATIENT
                                                                                                              Observable                    0.57
                                                                                                                                                                                                        Baseline Hybrid Hybrid++
                                                                                                                                                                                                                                                 Improvement due          to    Linguistic Techniques           challenges & lead to a
    Name:                          Observable          Patient                 Examination
                                   Entity                                                                     Entity                               0.52
                                                                                                                                                                  0.49 0.51
                                                                                                                                                                                            0.52
                                                                                                                                                                                                                                                 Linguistics (Fig 5)            can improve the recall          superior performance
                                                                                                                                       0.43                          0.43   0.43 0.43
                                                                                                                                                                                      0.45
                                                                                                                                                                                         0.43      Fig 5. Change in Results with 
    Gender: M F                                                               T               Respiratory                           0.37                                                                                                         2‐3% (P), >30%(R)              and      address     the
                                            Name                Gender                                                                                                         0.31                the term processing, 
   EXAMINATION                                                                                                                                                                                                                                                                  diversity challenge to a
                                                                                                                                                                                                   advanced linguistic technique
   T                                                                                                                                                                                                                                                                            large extent.
    Respiratory                    Observable
      Symmetric chest              Entity                                             symm.           nl perc.                                                                                                     Acknowledgements
                                                              M           F                                                            Set1          Set2          Set3        Set4      Set5
      expansion                          Qualifier                                    expan.
     Normal Percussion                   Value                Qualifier                                                                   Fig 4. Mapping Results for 3 Methods                                      National Cancer Institute (National Biomedical Imaging Branch): Grant #P01‐CA‐82710‐09
                                                              Value                   Finding               Finding                                                                                                 National Science Foundation Grants: NSF CCF 0905291, NSF CCF 1049864, and NSFC 90920005

             Fig. 2. A clinical form and its equivalent Semantic Form Tree. Each 
             node in the tree is tagged with SNOMED CT semantic categories.

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Exploiting Semantic Structure for Mapping Clinician-specified Form Terms to SNOMED CT Concepts

  • 1. Exploiting Semantic Structure for Mapping Clinician‐specified Form Terms to SNOMED CT Concepts Ritu Khare1,3, Yuan An3, Jiexun Li3, Il‐Yeol Song3, Xiaohua Hu3, Michele Follen1,2 , Yuan An Jiexun Il‐Yeol Xiaohua Michele Follen , College of Medicine Center for Women’s Health Research 1, and Obstetrics and Gynecology2 , College of Information Science and Technology3 Motivation, Problem, and Challenges Structure‐based SNOMED‐CT Mapping Framework The elements of clinical databases are usually named after the clinical terms used in various design artifacts. These terms are instinctively supplied by the Form Semantic  Semantic users, and hence, different users often use different terms to describe the same X Information  Training Data Y Form Tree clinical concept. This term diversity makes future database integration and Extraction analysis a huge challenge. Semantic SNOMED CT Semantic Structure –based Category Terms Category SNOMED CT  SNOMED  Form Term Structure Classification Specific (in Clinical  Picker Concept Mapping/ CT  Analyzer Model Mapping (API) Forms) (configurable) Standardization Concepts Fig. 3. Overall Mapping Framework: (1) The form tree structure is analyzed to derive the form context, (2) The Patient History Form Diversity Challenge Context Challenge classification model (Naïve Bayes) ranks the SNOMED CT semantic categories suitable for the form context, (3) A PATIENT (Well Addressed) (Less Explored) category is picked, (4) The most linguistically matching concept in this category is selected as the winner concept. Name: Gender: M F Different clinicians The same form Exploit the local semantic structure of form tree Select a winner semantic category , and map the DOB: MRN: specify different term when used in term to the linguistically matching concept within to determine the term context, and candidate Key Ideas HISTORY form terms to different contexts, SNOMED CT semantic categories. the determined semantic category. Chief specify the same may map to Complaints l l clinical concept. d ff different SNOMED How H can we Review of Systems: e.g., CT concepts. leverage the Results and Contributions Eyes MRN, or Med.Rec.#. e.g., the term semantic structure Empirical Study with Clinician‐designed Forms ENMT VitalSigns, Respiratory in Fig. 1 Future Work Respiratory Constitutional, or and 2. of clinical forms to About the Data About the Methods Leverage other relationships of Physical status map the form terms The data includes 26 forms collected from 5 BASELINE: Linguistic comparison SNOMED CT and test with other Fig 1. A Sample Clinician Designed Form into standard healthcare institutions. The forms contain HYBRID: Linguistic as well as vocabularies from the UMLS. SNOMED CT over 1500 terms, out of which 954 (63%) are Structural (Contextual) Test within larger frameworks mappable to SNOMED CT concepts. comparison (See Fig. 3) of health information systems. Preliminaries: SNOMED CT and Semantic Form Trees Preliminaries: SNOMED CT and Semantic Form Trees concepts? Mapping Precision HYBRID++: Linguistic as well as Apply other classification The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is a 0.89 0.92 0.87 advanced structural comparison techniques and employ 0.89 0.84 widely used medical terminology. It comprises 360,000 clinical CONCEPTS 0.76 0.73 0.78 0.72 sophisticated linguistic 0.69 belonging to various SEMANTIC CATEGORIES. Each concept is represented using 0.63 0.64 0.65 0.66 techniques. a CONCEPT ID and a FULLY SPECIFIED NAME. A simple search for the term Eyes 0.51 Findings Implications across the UMLS SNOMED CT browser leads to the following top results: Improvement due to 90 Structural Knowledge Concept Id Fully‐specified Name Semantic Category Precision structure (Fig 4) has the ability to 80 63342001 Sunsetting eyes Finding (R = recall, P=Precision) address the context Conclusion Recall 371110006 Immature eyes Disorder Set1 Set2 Set3 Set4 Set5 70 Hybrid over Baseline: challenge, and 18% (P); 2%(R) It is desirable to 362508001 Both eyes, entire Body Structure Baseline Hybrid Hybrid++ 60 Precision improve the overall develop hybrid Observable with Term Hybrid++ over Hybrid: mapping Person Procedure Entity 50 Processing Recall with 16% (P); 23%(R) approaches that can Patient Examination Form root 0.74 Mapping Recall Term performance. address both the 0.69 40 Processing PATIENT Observable 0.57 Baseline Hybrid Hybrid++ Improvement due to Linguistic Techniques challenges & lead to a Name: Observable Patient Examination Entity Entity 0.52 0.49 0.51 0.52 Linguistics (Fig 5) can improve the recall superior performance 0.43 0.43 0.43 0.43 0.45 0.43 Fig 5. Change in Results with  Gender: M F T Respiratory 0.37 2‐3% (P), >30%(R) and address the Name Gender 0.31 the term processing,  EXAMINATION diversity challenge to a advanced linguistic technique T large extent. Respiratory Observable Symmetric chest Entity symm.  nl perc. Acknowledgements M F Set1 Set2 Set3 Set4 Set5 expansion Qualifier expan. Normal Percussion Value Qualifier Fig 4. Mapping Results for 3 Methods National Cancer Institute (National Biomedical Imaging Branch): Grant #P01‐CA‐82710‐09 Value Finding Finding National Science Foundation Grants: NSF CCF 0905291, NSF CCF 1049864, and NSFC 90920005 Fig. 2. A clinical form and its equivalent Semantic Form Tree. Each  node in the tree is tagged with SNOMED CT semantic categories.