Incorporating Data Mining Applications into Clinical Guidelines

1
Incorporating Data Mining Applications into
Clinical Guidelines

• Presented by
• Reza Sherafat
• sherafr_at_mcmaster.ca
• March 28, 2006
• McMaster University

2
Agenda

• Current trends in Healthcare
• Clinical decision support systems
• Data Interoperability problem
• Data mining results as the source of knowledge
• Knowledge interoperability
• Integration of data mining results with clinical
  guidelines (plus some case studies)
• Summary
• References

3
Current trends in Healthcare

• The Healthcare professionals are overwhelmed with
  information.
• Preventable medical errors cause thousands of
  deaths each year and loss of billions of dollars.
• Healthcare information systems are deployed for
  various purposes, telemedicine, patient care,
  Electronic Health Records and decision support.
• A good start by many standards organizations to
  define and maintain healthcare standards.
• HL-7 (most popular healthcare data standard) 4

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Clinical decision support systems (CDSS)

• Effectiveness of clinical decision support
  systems (a question to be answered)
• Even a recommendation system should NOT to flood
  the practitioner with so many irrelevant cases
  and also should NOT ignore possible important
  cases.
• Many different approaches to provide decision
  making support.
• We focus on guideline-based CDSS that try to
  support clinical best practices at the point of
  care decision making.
• Arden Syntax by HL-7 5
• Guideline Interchange Format (GLIF) 3

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Arden Syntax 5

• The idea behind Arden Syntax is to have a simple,
  yet powerful enough procedural language that can
  encode the necessary logic for deciding upon a
  single problem.
• Decision making knowledge is encoded as IF-THEN
  rules in separate Medical Logic Modules (MLM).
• Each module is responsible for making a single
  decision and is run on an engine that can access
  the EHR systems. Based on the result of
  evaluation of the rules an action (an alert or
  reminder) is taken.
• A library of modules
• Modules have data sections that should be mapped
  to institution specific data repositories the
  rest of the module is already ready to use.

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Arden Syntax (Contd)

• knowledge
• type data_driven
• data
• last_creat read last "Creatinine level"
• last_BUN read last "BUN level"
• evoke ct_contrast_order
• logic
• if last_creat is null and last_BUN is null then
• alert_text "No recent serum creatinine
  available. Consider
• patient's kidney function before ordering
  contrast studies."
• conclude true
• elseif last_creat gt 1.5 or last_BUN gt 30 then
• alert_text "Consider impaired kidney function
  when
• ordering contrast studies for this patient."
• conclude true

3
Source 9
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Guideline Interchange Format (GLIF) 3

• Three different types are models are mentioned
• Guideline Models
• Data models
• Data mining models

• A guidelines specification standard
• Flowchart-like diagrams (Guideline Models)
• 3 levels of abstraction 3
• Conceptual modeling
• Computable level
• Implementation details

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Conceptual Modeling (Level 1)

• The conceptual models have simple building
  elements (steps)
• action step,
• patient state step,
• decision step,
• branch step and
• synchronization step
• It is easy to build and understand models
• Some steps may involve user interaction,access
  to a data source or triggering an event.

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Second and third levels in GLIF

• Computable level deals with encoding the decision
  making logic (expressions)
• Implementation level is concerned with how to map
  and bind the variable to local (institution
  specific) medical records.

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Data Interoperability
The semantics of the communication The semantics
convey the actual "meaning" of the message. The
semantics is conveyed via a set of symbols
contained within the communication. An external
"dictionary", thesaurus, or terminology explains
the meaning of the symbols as they occur.
A syntax for communication The syntax defines the
structure and layout of the communication. Common
syntax representations include ASN.1, XML, X.12,
HL7, IDL, etc.
Services to accomplish the communication Examples
include the post office, a telephone
switchboard, SMTP, FTP, Telnet, RPC, ORB
services, etc.
Source 7
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Data Interoperability (Contd)

• Three different types are models are mentioned
• Guideline Models
• Data models
• Data mining models

• THE KEY IDEA Through standardization
• HL-7 has built a standard Reference Information
  Model (RIM)
• RIM is in the form of a large class diagram that
  model the healthcare domain.
• Some other XML based standards like Clinical
  Document Architecture (CDA) use RIM as their main
  data model.

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Data Interoperability (Contd) RIM
Services
Stake holder
Organization
Person
Patient
ClinicalObservation
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Data mining results as the source of knowledge

• Three different types are models are mentioned
• Guideline Models
• Data models
• Data mining models

• Data mining research has been active in building
  models that can describe or predict.
• Applications of data mining studies
• Likelihood of coincidence of particular diseases
• Adverse drug usage
• Diagnosis
• Patient clustering based on risk factors
• Verification of known medical knowledge

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Knowledge interoperability

• THE KEY IDEA Through standards
• Use standards for knowledge sharing and exchange
• The mined knowledge should be incorporated into
  the guideline model to be used for decision
  making at the decision steps.
• PMML (Predictive Markup Language) 6 data
  mining knowledge is encoded using the PMML
  standard.
• GLIF3 Medical knowledge is encoded in guideline
  models

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Framework for interoperability of mined knowledge
Source 2
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Framework for interoperability of mined knowledge
(Contd)

• Three phases
• Knowledge preparation
• Mining the patients data
• Interoperation
• To make both data and mined knowledge available
  at the point of care through use of standard
• Interpretation
• Access the knowledge base with the patient data
  that needs decision making

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Integration of data mining results with clinical
guidelines
Guideline Execution
Guideline modeling
Knowledge Extraction
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Integration of data mining results with clinical
guidelines (Contd)

• Knowledge extraction
• Building data mining models on usually large
  data warehouses
• Guideline modeling
• Building guideline models
• PMML encoding
• Institution specific data bounding
• Guideline Execution
• Execution engine will follow the flow defined in
  the guideline model
• Accessing patient data from EMR systems
• Interact with the healthcare personnel
• Alert, recommend or remind

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Integration of data mining results with clinical
guidelines (Contd)
Source 2
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Implementation

• Extending Guideline Interchange Format3 (GLIF3)
  constructs
• To support the new functionality needed for the
  data mining models
• Guideline Execution Environment (GEE)
• To execute the guideline models, and
  access/interpret the data mining models
• Provision of the mined knowledge as webservices
  when the knowledge base is not available locally
• Very helpful for small devices e.g. handheld
  computers

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Case study

• A decision tree classifier
• For melanoma skin cancer diagnosis 6

Source 2
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Case study (Contd)
Source 2
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Guideline Execution Environment
The guideline execution environment widget
Guideline selection list
Different flows within a guideline in execution
Source 1
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Guidelines Meta Model
Data mining decision nodes as an ontology class
Data mining decision nodes slots
Source 1
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Guideline modeling
Slot widget to specify the new attributes of
a data mining decision node
Source 1
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Summary

• We described
• A knowledge management framework to for data
  mining results
• The environment in which the framework can be
  deployed
• How to integrate data mining results in clinical
  guidelines
• How knowledge interoperability is achieved.

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References

• Incorporating Data Mining Applications into
  Clinical Guidelines, R. Sherafat, K. Sartipi, The
  19th IEEE International Symposium on
  Computer-Based Medical Systems, 2006
• Data and Knowledge Interoperability in
  Distributed Healthcare Systems, R. Sherafat, K.
  Sartipi, The 13th Annual International Workshop
  on Software Technology and Engineering Practice,
  2005
• Guideline Interchange Format 3 (GLIF3),
  www.glif.org
• Health Level-7 (HL-7), www.hl7.org
• Arden Syntax, http//hl7.org/library/standards_non
  1.htmArden20Syntax
• Data Management Group (DMG), www.dmg.org

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References (Contd)

• Rules for melanoma skin cancer diagnosis,
  http//www.phys.uni.torun.pl/publications/kmk/
• Version 3 Intermediate Tutorial - Working the HL7
  Version 3 Methodology, George W. Beeler,
  http//hl7.org/library/data-model/V3_Tutorials/V3_
  Intermediate_May00.ppt
• An Arden Syntax MLM for CT study with contrast in
  patient with renal failure, http//cslxinfmtcs.csm
  c.edu/hl7/arden/mlm/astm_ct_contrast.txt

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Questions

• ?