Syed Sibte Raza Abidi a,, Selvakumar Manickam b

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1 International Journal of Medical Informatics 68 (2002) 187/203 Leveraging XML-based electronic medical records to extract experiential clinical knowledge An automated approach to generate cases for medical case-based reasoning systems Syed Sibte Raza Abidi a,, Selvakumar Manickam b a Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada B3S 1J3 b School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia Abstract Case-based reasoning (CBR)-driven medical diagnostic systems demand a critical mass of up-to-date diagnosticquality cases that depict the problem-solving methodology of medical experts. In practical terms, procurement of CBRcompliant cases is quite challenging, as this requires medical experts to map their experiential knowledge to an unfamiliar computational formalism. In this paper, we propose a novel medical knowledge acquisition approach that leverages routinely generated electronic medical records (EMRs) as an alternate source for CBR-compliant cases. We present a methodology to autonomously transform XML-based EMR to specialized CBR-compliant cases for CBRdriven medical diagnostic systems. Our multi-stage methodology features: (a) collection of heterogeneous EMR from Internet-accessible EMR repositories via intelligent agents, (b) automated transformation of both the structure and content of generic EMR to specialized CBR-compliant cases, and (c) inductive estimation of the weight of each casedefining attribute. The computational implementation of our methodology is presented as case acquisition and transcription info-structure (CATI). # 2002 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Case-based reasoning; Electronic medical records; Knowledge acquisition; XML 1. Introduction Case-based reasoning (CBR) [1,2] provides an analogy-based reasoning technique that is Corresponding author. Tel.: / ; fax: / addresses: (S.S.R. Abidi). quite prevalent towards addressing healthcare-related decision-support problems, in particular clinical diagnostic support, WWW-based patient-centric consultation, health planning and so on [3/6]. The problem-solving strategy of clinical practitioners routinely takes into account solutions to typical past problems*/they consider the differences between the current patient and /02/$ - see front matter # 2002 Elsevier Science Ireland Ltd. All rights reserved. PII: S ( 0 2 ) X

2 188 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/203 past treated patients (or cases) and accordingly exploit their subjective experience, gained whilst treating similar patients, to treat the current patient. In a similar manner, CBR*/an experience-based reasoning paradigm*/exploits the underlying experiential knowledge acquired from previously experienced problem situations (referred to as cases) to solve new problems. Problem solving via CBR involves a comparison of the current problem with a set of solved past problems, finding the most similar past problem(s) and finally applying the solution(s) of the similar past problem(s) to the current problem [1,2] Problem statement In practical terms, a CBR-based medical diagnostic system requires a critical mass of up-to-date diagnostic-quality operable clinical cases (OCCs), i.e. real-life cases depicting both a clinical situation expressed via a set of (problem) situation-defining attributes and an associated solution derived by a medical expert. In practice, the sustained and ubiquitous availability of high-quality OCC is deemed as a bottleneck towards the incorporation of medical CBR systems in any reallife medical diagnostic environment. This is because OCC procurement requires:. Medical experts to manually transcribe their real-life clinical experiences to a CBR-system-compliant case structure, i.e. a OCC. Note the operational constraints in this scenario: (a) medical experts need to be engaged on regular basis, which is not only expensive but also resource-intensive and (b) medical experts are required to map their experiential knowledge, which may be organized with respect to their cognitive models, to the OCC formalism which may be different from their perspective [7].. Medical knowledge engineers to routinely source for and collect a large volume of upto-date cases from multiple medical experts, who may be distributed over various sites.. Medical knowledge engineers and medical experts to standardize and validate the collected OCC, because the OCC might be procured from different medical experts and sites [8].. Medical experts to judge the relative importance of each OCC-attribute towards the solution. The expert s subjective judgment reflects as the OCC-attribute s weight */a value that modulates the confidence of the inferred solution [9]. We argue that despite the natural propensity of CBR technology to provide effective medical diagnostic support, the need to satisfy the above constraints tend to compromise the overall acceptance and deployment of CBRbased systems within adaptive medical environments Proposed problem solution: the method Given the above problem description, one possible solution to OCC procurement is to leverage alternate situation-solution information resources. From an information-content perspective, routinely collected electronic medical records (EMRs) contain both the situation (observed symptoms and findings) and the solution (diagnosis/prognosis/treatment-plans prescribed by a medical expert) information, more so the EMR information representation formalism depicts a causal relationship between the observation and the reasoning by a medical expert. Henceforth, there is a case to investigate the possible transformation of EMR to reasoning constructs akin to OCC [10,11]. In this paper, we will discuss the methodological and functional issues to facilitate OCC

3 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/ procurement via the automatic transformation of situation-solution information contained in generic EMR to specialized OCC. We present a case-base enrichment methodology, together with its computational implementation (as shown in Fig. 1), to autonomously transform extensible Markup Language (XML)-based EMR*/originating from heterogeneous EMR repositories accessible over the Internet/WWW [12]*/to specialized OCC that can subsequently be incorporated within CBR-based medical diagnostic systems. The work presented here accounts for:. Pro-active and continuous scouting and procurement of OCC-compliant information from heterogeneous Internet-accessible EMR repositories via an intelligent agent [12].. Automatic transformation of generic EMR (focusing on sections containing causal information objects) to specialized OCC. This requires both inductive and deductive techniques*/featuring the use of meta-data constructs for structural equivalence, metathesaurus for terminological equivalence, and domain-specific ontologies for conceptual equivalence [13].. Automatic estimation of the weight of each OCC-attribute [14,15]. This is achieved by applying a suite of neural network (NN)- based feature sensitivity analysis techniques applied to a cohort of OCC. Fig. 1. The functional architecture of our CATI, illustrating three task-specific layers.

4 190 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/ EMRs as causal information objects: a justification An EMR can be viewed as a manifestation of the physician s problem-solving strategy since it depicts a problem situation and a corresponding physician-specified solution/action [11,16,17]. Typically, information in an EMR can be divided into three major parts:. Problem/situation description. The state of the problem documented by the physician in terms of the patient and problem description, i.e. longitudinal patient history, illness-related signs and symptoms, pathological finding, and so on.. Solution. The physician s inferred solution, given in terms of diagnosis and/or a treatment plan.. Outcome. the resulting state of the world when the solution will be carried out. In our case, physician-specified follow-up or rehabilitation-related information. From the above information content, one can regard an EMR to: (i) represent the cognitive framework of the processing and the constraints which arise in the context of each given clinical situation; (ii) express through a set of attributes and corresponding values a clinical situation (either new or a follow-up) comprising both observations and solutions; (iii) represent codified medical guidelines; and (iv) contain clinical reasoning that is inherent in the medical expert s actions. We believe that the above properties of EMR are quite similar to that of a typical case as per the CBR formalism [1]. Hence, we regard an EMR as a valid high-level abstraction of clinical problem-solving knowledge systematically, yet implicitly, compiled by physicians during episodic visits by patients. Fig. 2 illustrates high-level structural similarities between an EMR and a typical OCC. 3. Methodology: automated EMR/OCC transformation EMRs are specifically designed to operate within a controlled information processing environment defined by the information standards adopted by the parent health information system (HIS). The information structure and representation format of EMR varies across different HIS; in fact there are outstanding issues regarding the interoperability of EMR across heterogeneous HIS. Likewise, the information structure and content of an OCC is compliant to a specific CBR system. Here, we are looking at two information objects, with potential functional similarities, yet each is designed for a different purpose and is used by a different kind of system. Both, an EMR and an OCC comprise a set of attribute-value pairs, whereby the list of attributes determines the information structure and the attribute s values represent the stored information content. Despite the functional similarities between EMR and OCC, the inevitable terminological and ontological variations used by both EMR and OCC to represent the same concepts*/at both the attribute and value levels*/requires a methodology to map EMR to OCC, whilst maintaining conceptual equivalence. The problem of EMR/OCC transformation can thus be described as the automated mapping of both the structure and the content of heterogeneous EMR (sourced from multiple HIS) to some pre-defined OCC structure and content vocabulary. In this way, the EMR is the source and the OCC is the target of the transformation*/note that OCC are generated to populate the case-base of a target CBR system. We have devised an EMR/OCC transformation methodology that facilitates the mapping of the source EMR to the target OCC based on establishing conceptual equivalence

5 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/ Fig. 2. General similarities between a typical EMR and a case. at both the structural and content levels. Our EMR/OCC transformation methodology involves two phases. Phase I attempts to establish structural mapping, i.e. to determine for each OCC-attribute a conceptually equivalent EMR-attribute; and Phase II attempts to establish content mapping, i.e. to standardize the content (the value) of each mapped EMRattribute with respect to the pre-defined content vocabulary of the corresponding OCCattribute Phase I: structural equivalence The objective of the structural equivalence phase is to map pre-defined OCC-attributes (that represent the structure of the case) to conceptually equivalent EMR-attributes, to facilitate the subsequent transfer of the values of the EMR-attributes to matching OCCattributes. To ensure structural equivalence, it is imperative that each OCC-attribute is corelated with at least one EMR-attribute; however, we envisage and deal with situations when an OCC-attribute may map to more than one EMR-attribute. The structure of OCC is defined as an OCC meta-data map that details the name, conceptual description, conceptually equivalent synonyms, data structure, and type of each OCC-attribute [12]. In our work, we leverage XML-based EMR that are deemed as structured documents*/ the structure being either externally defined in terms of a document type definition (DTD) or internally defined in terms of attribute-defining tags. The availability of the EMR s DTD directly renders the EMR meta-data map, else we need to parse the XML-based EMR to derive the EMR meta-data map. Structural equivalence is achieved via a two-pass strategy that takes as input an OCC-attribute (from the OCC meta-data map), matches it with the EMR meta-data map, and outputs a conceptually equivalent EMR-attribute(s), as shown

6 192 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/203 Fig. 3. Flowchart illustrating the automated process of EMR/OCC structural equivalence. in Fig. 3. Functional details of the two passes are: Pass 1. For each OCC-attribute, we attempt to find a similar named EMRattribute to establish a basic name match, i.e. an OCC-attribute is directly mapped to an EMR-attribute(s). Pass 2. For non-matched OCC-attributes (from pass 1), we attempt structural equivalence by taking into account possible terminological variations between the target OCC and the source EMR-attributes. For each OCC-attribute, we have compiled a list of conceptually equivalent synonyms (including abbreviations and acronyms) from a number of different EMR definitions. In pass 2, we use the synonyms of each OCC-attribute to establish a synonymous match with an EMR-attribute. If all OCC-attributes are successfully mapped to corresponding EMR-attributes, then EMR/OCC structural equivalence is achieved. However, in case a few OCCattributes are not mapped to EMR-attributes, a variety of options are possible ranging from discarding the EMR source to manual assistance by domain experts. Table 1 illustrates the transformation of a DTD of an exemplar EMR to a pre-defined OCC structure Phase 2: content equivalence Successful structural equivalence allows for the values*/depicting situation-action content*/of the matched EMR-attributes to be

7 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/ Table 1 EMR/OCC structural equivalence DTD of EMR EMR structure OCC structure B/!ELEMENT EMR (DATE, VISIT_TYPE, PATIENT_INFO, VITAL_- DOB: AGE: SIGNS, OBSERVATION/, FINDING/, TREATMENT/, PATH TEST/, MEDICAL HISTORY, FAMILY HISTORY)/ B/!ELEMENT PATIENT_INFO (DOB, SEX)/ SEX: GENDER: B/!ELEMENT DOB (#PCDATA)/ VITAL SIGNS VITALS B/!ELEMENT SEX (#PCDATA)/ Temperature Temperature B/!ELEMENT VITAL_SIGNS (TEMPERATURE, HEART_RATE, Heart Rate Heart Rate BLOOD_PRESSURE, PULSE)/ B/!ELEMENT TEMPERATURE (#PCDATA)/ BP BP B/!ELEMENT HEART_RATE (#PCDATA)/ Systolic Systolic B/!ELEMENT BLOOD_PRESSURE (#PCDATA)/ Diastolic Diastolic B/!ELEMENT PULSE (#PCDATA)/ Pulse Pulse B/!ELEMENT OBSERVATION (ID, TYPE, NOTES)/ OBSERVATION {ID, Type, Notes} SYMPTOM {Specific Type} B/!ELEMENT ID (#PCDATA)/ B/!ELEMENT TYPE (#PCDATA)/ B/!ELEMENT NOTES (#PCDATA)/ B/!ELEMENT FINDING (ID, TYPE)/ FINDINGS {ID Type} DIAGNOSIS {ID Type} B/!ELEMENT ID (#PCDATA)/ PATH TEST {Type, Value, Unit} LAB TEST {Type, Value, Unit} B/!ELEMENT TYPE (#PCDATA)/ B/!ELEMENT TREATMENT (DRUG, THERAPY, DOSAGE, RE- COMMENDATION)/ MEDICAL HISTORY {} MEDICAL HISTORY {}... FAMILY HISTORY {} FAMILY HISTORY {} The center column shows the EMR meta-data map derived from the DTD. The EMR-attributes shown in italics were mapped to the OCC-attributes during pass 2. used to populate the corresponding OCCattributes. In the CBR formalism, inter-case similarity is established by comparing the values of the current case with those of the past cases on an attribute-by-attribute basis, such that cases with similar values for the corresponding attributes are deemed to be similar. The use of different terms, representing the same medical concept, adversely affects the value-specific inter-case matching strategy. For instance, given two cases that use different terms to represent conceptually equivalent values for corresponding attributes, the inter-case matching algorithm may nonetheless regard the two cases as being different. For our purposes, the heterogeneous origin of the EMR leads to the inevitable usage of functionally synonymous term to denote the same concept. Hence, to ensure inter-case matching accuracy it is extremely important to (a) define both a numerical (for numeric values) and vocabulary (for textual values) domain for each OCC-attribute-value; and (b) to standardize the EMR values with respect to the pre-defined OCC content. Phase 2 of our EMR/OCC transformation methodology specifically deals with establishing EMR/OCC content equivalence for both textual and numerical EMR-attribute-values, as shown in Fig Textual content equivalence Textual content equivalence (TCE) involves both terminological and ontological mapping between EMR- and OCC-attribute-values. Functionally, we generate a To-Map_Textual

8 194 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/203 Fig. 4. Flowchart illustrating the automated process of EMR/OCC content equivalence. The EMR content is distinguished as being textual and numerical. list comprising the following tuples: {EMRattribute, EMR-attribute-value, OCC-attribute, OCC-attribute-vocabulary} that determine the source EMR-attribute-value and the target OCC-attribute s pre-defined vocabulary. Next, we attempt to map the EMRattribute-value to an acceptable OCC-attribute-value; upon success, the element is removed from the To-Map_Textual list and placed in the Mapped_Textual list as the following tuple: {EMR-attribute, EMR-attribute-value, OCC-attribute, OCC-attributevalue, mapping_type}. TCE is successfully completed when the To-Map_Textual list is empty. TCE is pursued via a three pass strategy as detailed below Pass 1: term equivalence. In pass 1, we attempt to establish a direct term match for all EMR-attribute-values with their corresponding OCC-attribute-value vocabulary. All successfully mapped EMR_attribute-values are removed from the To-Map_Textual list and placed in the Mapped_Textual list, with the value term match for the mapping-type attribute Pass 2: terminological equivalence via meta-thesaurus. In pass 2, we attempt to establish terminology-level equivalence between the EMR-attribute-values with the pre-defined OCC-attribute-value vocabulary via a medical meta-thesaurus that may define the domain vocabulary space. We use the UMLS meta-thesaurus [18/20] to check whether any conceptually equivalent synonym of the EMR-attribute-value matches with the corresponding OCC-attribute-value vocabulary. All terminologically equivalent EMR_attribute-values are removed from the To- Map_Textual list and placed in the Mapped_- Textual list, with the value terminological match for the mapping-type attribute. We argue that meta-thesaurus-mediated terminological equivalence will effectuate cases having a common terminology for the same concept which in turn will facilitate inter-case similarity measurement. Table 2 shows terminological mapping of textual EMR content to standard OCC-attribute-values Pass 3: ontological equivalence via medical ontology. For the non-matched EMR-attribute-values (as shown in the right column of Table 2) from pass 2, we attempt ontological equivalence by mapping the conceptual specialization/generalization of the EMR-attribute s value to the corresponding

9 Table 2 Exemplar terminological equivalence EMR content Terminological mapping OCC content (after terminological mapping) SYMPTOM {Specific: (General, General, Musculosketal, Cardio, Cardio, Cardio, Digestive) Type: (Wooziness, Fever, Lack of Coordination, Rapid Heartbeat, Orthopnea, Mediastinitis, Anorexia)} Wooziness/Dizziness, Rapid Heartbeat/Tachycardia SYMPTOM {Specific: (General, General, Musculosketal, Cardio, Cardio, Cardio, Digestive) Type: (Dizziness, Fever, Lack of Coordination, Tachycardia, Orthopnea, Mediastinitis, Anorexia)} DIAGNOSIS {ID: (Lyme disease)} DIAGNOSIS {ID: (Lyme disease)} LAB TEST {Type: (Titer-ELISA, Western Blot, Polymerase Chain Reaction) Value: (1.45, True, 2.31)} Polymerase Chain Reaction/ PCR LAB TEST {Type: (Titer-ELISA, Western Blot, PCR) Value: (1.45, True, 2.31)} MEDICAL HISTORY {Myocardial Infarction, Angina} MEDICAL HISTORY {Myocardial Infarction, Angina} FAMILY HISTORY {Heart disease, Colon tumor, Asthmatic} Asthmatic/Asthma FAMILY HISTORY {Heart disease, Colon tumor, Asthma} The left column shows the EMR-attribute-values; the values in bold typeface do not conform to standard OCC-attribute-values. The center column shows the terminological mapping of EMR-attribute-values to standard OCC-attribute-values. The right column shows the resultant OCC-attribute-values; the values in italic typeface are achieved via a terminological match, whilst the values in bold typeface are yet to be standardized. S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/

10 196 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/203 OCC-attribute-value vocabulary. We argue that a medical ontology cannot only serve as a global conceptual schema, capturing the structure and semantics of the knowledge space, but also as a vehicle for medical knowledge transformation and standardization [13]. A medical ontology*/defining a taxonomy of medical concepts*/depicts a semantic parent/child relationship between concepts which can form the basis for matching two concepts. If the two given concepts are not deemed terminologically equivalent, the use of a taxonomy allows us to determine whether the parent/child of concept A has a relationship with concept B, or vice versa. For example, given a medical ontology the two concepts orthopnea and hypercapnia can be deemed conceptually equivalent since, orthopnea is the parent of dysepnea which is the parent of hypercapnia (represented as orthopnea0/dysepnea0/hypercapnia). In pass 3, we use an UMLS-derived ontology [20,21] (as shown in Fig. 5) to establish ontological equivalence via a three-step process: (1) Determine the source of the ontological equivalence, which can be either the EMR- or the OCC-attribute-value (as shown in Fig. 5). Typically, we use the EMRattribute-value as the source concept; (2) determine the position of the source concept in the ontology; (3) establish ontological equivalence by transversing the ontology*/ upward transversal of the ontology yields a generalization whereas downward transversal yields a specialization of the concept. All ontologically equivalent EMR_attribute-values are removed from the To-Map_Textual list and placed in the Mapped_Textual list, with the value ontological match for the mapping-type attribute. Table 3 shows ontological mapping applied to the non-matched OCC-attribute-values of Table Numerical content equivalence EMR contain raw continuous-valued numeric data that need to be made amenable to inter-case matching strategies. For each OCCattribute-value with a numeric data type, we have a discretization map that determines the numerical content equivalence (NCE) strategy being used. We use two NCE strategies: Strategy I. To discretize the range of continuous values into meaningful intervals, where each interval is assigned a unique textual label. For example, normal Fig. 5. A segment of a medical ontology derived from the MSH99 coding scheme.

11 Table 3 Exemplar ontological equivalence applied to the remaining non-standard EMR-attribute-vales shown in Table 2 OCC content (after terminological mapping) Ontological mapping OCC content (after ontological mapping) SYMPTOM {Specific: (General, General, Musculosketal, Cardio, Cardio, Cardio, Digestive); Type: (Dizziness, Fever, Lack of Coordination, Tachycardia, Orthopnea, Mediastinitis, Anorexia)} Child: Lack of Coordination0/ Muscular Incoordination SYMPTOM {Specific: (General, General, Musculosketal, Cardio, Cardio, Cardio, Digestive); Type: (Dizziness, Fever, Muscular Incoordination, Tachycardia, Hypercapnia, Myocarditides, Anorexia)} DIAGNOSIS {ID: (Lyme disease)} Child: Orthopnea0/Dyspnea0/ DIAGNOSIS {ID: (Lyme disease)} Hypercapnia LAB TEST {Type: (Titer-ELISA, Western Blot, PCR); Value: (1.45, True, 2.31)} Child: Mediastinitis0/Rheomatoid Carditis0/Myocarditides LAB TEST {Type: (Titer-ELISA, Western Blot, PCR); Value: (1.45, True, 2.31)} MEDICAL HISTORY {Myocardial Infarction, Angina} Parent: Angina0/Myocardial Ischemia MEDICAL HISTORY {Myocardial Infarction, Myocardial Ischemia } FAMILY HISTORY {Heart disease, Colon tumor, Asthma} Parent: Heart disease0/coronary disease FAMILY HISTORY {Coronary disease, Colon tumor, Asthma} The left column shows the EMR-attribute-values; the values in bold typeface do not conform to standard OCC-attribute-values. The center column shows the ontological mapping of EMR-attribute-values to standard OCC-attribute-values. The right column shows the resultant OCC-attribute-values; the values in italic typeface are achieved via an ontological match. S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/

12 198 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/203 Table 4 Exemplar numerical content equivalence OCC content (after terminological mapping) Numerical mapping OCC content (after numerical mapping) AGE: 43 years AGE: 43 years/ AGE: Middle Age Middle Age GENDER: M GENDER: M VITALS VITALS Temperature: 39.2 Temperature: 39.2/High Temperature: High Heart Rate: 110 Heart Rate: 110/High Heart Rate: High BP BP Systolic: 117 Systolic: 117/ Normal Systolic: Normal Diastolic: 78 Diastolic: 78/ Normal Diastolic: Normal SYMPTOM { SYMPTOM { The center column shows the mapping of EMR-attributevalues to standard OCC textual labels. The right column shows the resultant OCC-attribute-values. readings of hemoglobin blood count can be discretized into five meaningful intervals, each interval identified by a textual label, i.e. very low, low, normal, high and very high. Table 4 shows NCE using the discretization strategy. Strategy II. To assign a distance threshold that defines an acceptable difference between two numeric values; if the difference between two continuous values is below the distance threshold they are regarded as being similar. 4. Method: phases of automated EMR/OCC transformation Functionally, EMR/OCC transformation is carried out as a sequential process, spanning over five major phases (shown in Fig. 6). We briefly explain the functionality of the salient phases Phase A: Internet-mediated EMR procurement This phase is responsible for the following activities: 1) EMR donor site set-up whereby permission is granted by the institutions owning the EMR allow for remote access by our data procurement agent to access the EMR repository to procure EMR. Due to the nature of the information being released, a number of serious ethical and legal issues need to be considered by donor institutions. Site set-up also involves agreeing on an EMR blinding strategy so as to ensure that the released EMR does not contain any personal identification-related information. Fig. 6. Functional stages of case-base enrichment via EMR/OCC transformation.

13 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/ Fig. 7. A screenshot of the data procurement agent, depicting a list of donor data procurement sites and certain information pertaining to data procurement. 2) EMR procurement from heterogeneous, Internet-enabled donor EMR repositories. This is achieved by a data procurement agent (shown in Fig. 7) that periodically monitors the selected EMR repositories for the addition of new EMR with respect to the last EMR procurement schedule. EMRs recently added to the EMR repository are automatically procured. 3) EMR cleansing involves the removal of all information that may lead to ethical and legal claims, for instance, patient name, address, next-of-kin, etc. 4) EMR storage in intermediate repositories at the application server side Phase B: EMR/OCC transformation This phase is responsible for the automatic transformation of generic XML-based EMR to specialized OCC as per the methodology described in the earlier section (see Figs. 3 and 4). Table 5 shows the transformation of an EMR to an OCC via the various stages of our EMR/OCC transformation methodology. We have put in place an OCC-fitness matrix that associates a fitness value to each OCC, which in turn determines the influence of the case in the reasoning process. The fitness value associated with a case is a composite of the marks awarded by the content equivalence stage to each OCC-attribute. Highest marks are given for a term match whereas a relatively lower mark is awarded for a weak ontological match. In an automated OCCvalidation scheme, all transformed OCC with a fitness value above the pre-defined threshold are selected for inclusion to the CB. However, in a semi-automated scheme, manual validation by domain experts is also a viable option

14 200 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/203 Table 5 Exemplar EMR/OCC transformation EMR content OCC content (after mapping) AGE: 43 years AGE: Middle Age GENDER: M GENDER: M VITALS VITALS Temperature: 39.2 Temperature: High Heart Rate: 110 Heart Rate: High BP BP Systolic: 117 Systolic: Normal Diastolic: 78 Diastolic: Normal SYMPTOM {Specific: (General, General, Musculosketal, Cardio, Cardio, Cardio, Digestive); Type: (Wooziness, Fever, Lack of Coordination, Rapid Heartbeat, Orthopnea, Mediastinitis, Anorexia)} SYMPTOM {Specific: (General, General, Musculosketal, Cardio, Cardio, Cardio, Digestive); Type: (Dizziness, Fever, Muscular Incoordination, Tachycardia, Hypercapnia, Myocarditides, Anorexia)} DIAGNOSIS {ID: (Lyme disease)} DIAGNOSIS {ID: (Lyme disease)} LAB TEST {Type: (Titer-ELISA, Western Blot, Polymerase Chain Reaction); Value: (1.45, True, 2.31)} LAB TEST {Type: (Titer-ELISA, Western Blot, PCR); Value: (1.45, True, 2.31)} MEDICAL HISTORY {Myocardial Infarction, Angina} MEDICAL HISTORY {Myocardial Infarction, Myocardial Ischemia } FAMILY HISTORY {Heart disease, Colon tumor, Asthmatic} FAMILY HISTORY {Coronary disease, Colon tumor, Asthma} In the right column, the values in bold typeface are subject to transformation. In the right column, the values in italic typeface are a result of the transformation. as it also allows for domain experts to tweak the transformed OCC to make it more accurate and relevant Phase C: case-base maintenance via inductive OCC-attribute ranking This phase entails CB maintenance, which amongst other activities includes an inductive assessment of each OCC-attribute s significance towards the eventual CBR-based solution. In our work, we apply three different attribute sensitivity analysis techniques [22,23] to back-propagation neural network that have learnt an abstract representation of the inherent relationship between the input stimuli and the associated output response. Our results are interesting and show quite an agreement with attribute weights assigned by domain experts in a subjective manner. 5. Implementation details The CBR system is implemented using the Java 2.0 programming language, and is hosted on a server running Windows NT. The casebase and the intermediate database(s) are implemented using the Microsoft SQL Server 7.0 (MSSQL) database. The UMLS Metathesaurus and domain ontologies are converted from native text files into indexed segments of MSSQL database for faster queries. Data exchange with donor EMR repositories is achieved using JDBC (Java Data Base Connectivity), which ensures dynamic connections with a wide range of database platforms. EMR (given as XML documents)-based information extraction visa-vis XML document parsing is achieved using the Microsoft XML Parser. The webbased GUI is implemented using Java servlets in conjunction with Microsoft IIS web server.

15 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/ Fig. 8. Technical architecture of CATI illustrating the various development platforms. Fig. 8 shows the confluence of technologies used to implement our methodology. 6. Concluding remarks In our work, we have managed to leverage information-rich EMR, accessible over the Internet, to enhance the (medical) knowledge of traditional medical CBR systems. We have presented a novel facet and utility of routinely collected EMR, whereby they can be transformed from mere information resource to a diagnostic decision-support resource. Transformation of generic EMR to specialized OCC is an interesting yet challenging research problem in a real-life setting; nevertheless, our initial results, though based on certain realistic premises are quite encouraging. The premises of our work are:. EMRs are represented as XML documents, hence the structure of the EMR can be readily determined. On the other hand, determining the structure of non-xmlbased EMR is a challenging problem and will require improvements to the structural equivalence strategy.. EMR-attribute-values use medical terms and standard codes. At this stage, we do not perform any natural language processing to determine the semantics of the values to effectuate content transformation. However, in a real-life setting there is the need to transform notes written in natural language by medical practitioners. This will require improvements to the textual content equivalence strategy.

16 202 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/203. A single case is an episodic (snapshot) encounter with a medical practitioner*/ there is a set of problem-defining symptoms and a corresponding action. This understanding discounts the temporal aspects associated with the longitudinal medical history of an individual, which usually is the case with EMR. Henceforth, we regard each clinical visit to be recorded as a new page in the EMR, and each page of the EMR is to be transformed to an OCC. We need to add that due to the confidential nature of EMR, it has been extremely difficult to find Internet-accessible EMR repositories, even for research purposes. Nevertheless, from a medical informatics research perspective we find the possibility of transforming EMR to decision-support resources quite interesting and technically challenging, notwithstanding our present controlled research environment, and anticipate that with increasing demand for decision-support services together with improved EMR security and privacy protocols our work can be useful towards enriching CB for value-added diagnostic support services. References [1] A. Aamodt, E. Plaza, Relating case-based reasoning: foundational issues, methodological variations and system approaches, AI Communications 7 (1) (1994) 39/59. [2] J.L. Kolodner, Case-based Reasoning, Morgan Kaufmann, San Mateo, [3] K. Althoff, R. Bergmann, Case-based reasoning for medical decision support tasks: the INRECA approach, Artif. Intelligence Med. J. 12 (1998) 25/41. [4] C. Bradburn, J. Zeleznikow, The application of case-based reasoning to the tasks of health care planning, in: S. Wess, K.D. Althoff, M. Richter (Eds.), Topics in Case-based Reasoning: First European Workshop, Springer, Berlin, 1994, pp. 365/378. [5] G. Mariuzzi, A. Nombello, L. Mariuzzi, P.W. Hamilton, J.E. Weber, D. Thompson, P.H. Bartels, Quantitative study of ductal breast cancer*/patient targeted prognosis: an exploration of case-based reasoning, Pathology Research and Practice, vol. 193, 1997, pp. 535/542. [6] M. Jaulent, C. Le Bozec, Case-based diagnosis in histopathology of breast tumours, in: B. Cesnik, A. McCray, J. Scherrer (Eds.), Proceedings of MedInfo 98, IOS Press, Amsterdam, [7] D.W. Aha, Tolerating noisy, irrelevant, and novel attributes in instance-based learning algorithms, Int. J. Man- Mach. Stud. 36 (1992) 267/287. [8] D.W. Aha, Feature weighting for lazy learning algorithms, in: H. Liu, H. Motoda (Eds.), Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer, Norwell, MA, [9] J.J. Cimino, Vocabulary and health care information technology: state of the art, J. Am. Soc. Inform. Sci. 46 (1995) 777/782. [10] D.W. Aha, L. Breslow, Refining conversational case libraries, in: Proceedings of the Second International Conference on Case-based Reasoning, RI, USA, [11] E.H. Shortliffe, The Evolution of healthcare records in the era of the Internet, in: B. Cesnik, A. McCray, J. Scherrer (Eds.), Proceedings of MedInfo 98, IOS Press, Amsterdam, [12] I.S. Kohane, P. Greenspun, J. Fackler, C. Cimino, P. Szolovits, Building national electronic medical record systems via the world wide web, J. Am. Med. Inform. Assoc. 3 (3) (1996) 191/207. [13] G. Leroy, H. Chen, Meeting medical terminology needs*/ the ontology-enhanced medical concept mapper, IEEE Trans. Inform. Technol. Biomed. 5 (4) (2001) 261/270. [14] W. Wilke, R. Bergmann, Considering decision costs while learning of feature weights, in: Proceedings of the Third European Workshop on Case-based Reasoning, Lausanne, Switzerland, Springer, Berlin, 1996, pp. 460/472. [15] N. Howe, C. Cardie, Examining locally varying weights for nearest neighbor algorithms, in: Proceedings of the Second International Conference on Case-based Reasoning, Springer, Providence, RI, 1997, pp. 455/466. [16] R.S. Dick, E.B. Steen, The Computer-based Patient Record: An Essential Technology for Health Care, National Academy Press, Washington, DC, [17] G.F. Murphy, Electronic Health Records: Changing the Vision, Harcourt Brace & Co, Philadelphia, [18] J.J. Robert, M. Joubert, L. Nal, M. Fieschi, A computational model of information retrieval with UMLS, in: Proceedings of the 17th Annual Computer Applications in Medical Care Symposium, 1994, pp. 167/171. [19] G. Carenini, J.D. Moore, Using the UMLS semantic network as a basis for constructing a terminological knowledge base: a preliminary report, in: Proceedings of the 17th Annual Computer Applications in Medical Care Symposium, [20] H. Chen, B.R. Schatz, T. Yim, D. Fye, Automatic thesaurus generation for an electronic community system, J. Am. Soc. Inform. Sci. 46 (1995) 175/193. [21] H. Chen, J. Martinez, D.T. Ng, B.R. Schatz, A concept space approach to addressing the vocabulary problem in

17 S.S.R. Abidi, S. Manickam / International Journal of Medical Informatics 68 (2002) 187/ scientific information retrieval: an experiment on the Worm Community System, J. Am. Soc. Inform. Sci. 48 (1997) 17/31. [22] S. Hashem, Sensitivity analysis for feedforward artificial neural network with differentiable activation functions, in: Proceedings of the International Joint Conference on Neural Networks, Vol. 1, [23] H.L. Poh, J.T. Yao, C.L. Tan, Forecasting and analysis of marketing data using neural networks, J. Inform. Sci. Eng. 14 (1998) 843/862.

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