Sep2009 Introduction to Medical Expert Decision Support Systems for Mayo Clinic

Michael Vogt, Ph.D.gFormerly of the DOE/DoD National Laboratories

Specialist in advanced sensing, instrumentation, and applying artificial intelligenceintelligence

11 September 2009

This presentation is Copyright © 2009 Vogtland Innovations and Research, Inc. Contact Vogtland for permission to re-use. Vogtland I&R, 2400 Ivy Lane, Bloomington, MN 55431, 630/915-1962, [email protected]

Capturing and Documenting expertise is critical to its evaluation and verification, sharing, and preservation

Semantic Networks are useful to document a process or object or topic and visualize its relationships and characteristics — an aid to modifications and improvements

Decisions Trees are an effective way to document the decision-making process, but are limited to problems with a pre-determined set of possible solutions — such as possible causes of failure for a part or a list of treatments for a known medical conditiontreatments for a known medical condition

Something more flexible than semantic networks and decision trees is required to address the dynamic problems and solutions found in research…p

A hybrid Expert Decision-Support System targeting Melanomacould/should/would-• Capture and share department staff expertise to enable each to use theirCapture and share department staff expertise to enable each to use their

work and research time the most effectively• integrate patient(s) results from laboratory tests and instrumentation• integrate patient(s) medical imagery analysis• integrate patient(s) medical imagery analysis

• serve as model expert system architecture for other diseases and diticonditions

• support practical and effective remote access to Mayo resources and expertise (telehealthcare)

Premise — human beings have arrived at the top of their food chain, presumably because they solve problems the most effectively — therefore, how humans solve problems, their “intelligence”, is worth emulating*

Artificial Intelligence (AI) can then be defined as the capturing of human problem-solving capability into machines, in either dedicated hardware or p preprogrammable software

Attempting to capture all of the integrated and generalized problem-solving p g p g g p gintelligence of a human being is a gigantic challenge**, but replicating that intelligence for a specific problem is very achievable

An Expert System is a reasonable solution to the classic AI problem ofAn Expert System is a reasonable solution to the classic AI problem of programming [human] intelligence into a machine - it mimics the decision-making process(es) of a human being

• This can be accomplished through heuristics, pattern-recognition, inexact logic, or visual image analysis (as well as tables, hand computers, and other devices)

• It can be designed and approximated using flow charts and decision trees, It can be designed and approximated using flow charts and decision trees,but should be implemented on a computer for speed and flexibility

• To be effective, ideal expert systems are intentionally focused, not broad nor too all-inclusive

• Th h l d h i f l i d i i• They have a pre-planned mechanism for learning and improving• They provide detailed explanation of the arrived at decision. The

explanation portion of the expert system is commonly referred to as a Decision Support System and is often more desirable than pure machine pp pdecision-making

Facts can be in the form of text words, instrument measurements (including

time-based), and/or imagery Knowledge Base cab be built from Heuristic Rules, Instrument Measurement Exemplars,

KnowledgeBase

FactsImagery Libraries

Expertise

A variety of Inference Engines are

InferenceEngine

Expertise can be in the form of text (instructional words), values, and/or imagery

y gavailable, including

Production Systems, Neural Networks, Fuzzy Arithmetic, and

Statistics

Expert System

P Th d l i i i d i f i• Permanence - The declarative interpretation and preservation of tacit personnel knowledge, preserving expertise of human expert(s) and protecting the investment in their education

• Increased Availability and Speed- free-up senior domain expert for more y p p pcomplex problems, available around-the-clock, fast

• Reduced Cost by shifting the burden of routine decision-making, more effectively using senior staff for difficult probelms

• Combined Experience and Expertise of multiple team experts in efficient• Combined Experience and Expertise of multiple team experts in efficient manner

• Education Aid - Explanation (for training, experiment planning, and liability defense)

• Increased Reliability - steady, unemotional, and complete response at all times

• Error Reduction - In a rapidly advancing field such as [medical] research, capturing the latest procedures and treatment steps in an expert systemcapturing the latest procedures and treatment steps in an expert system minimizes errors and aids entire team in moving forward in unison

• Successive Application of Heuristics (IF-THEN Production Rules), • Numerical Pattern-Recognition, • Inexact Logic [Fuzzy ClassificationInexact Logic [Fuzzy Classification

and decision-making], or • Imagery Analysis with

Feature IdentificationFeature Identification

When implemented, each strategy provides a User Interface andprovides a User Interface and some manner of Reporting results, but each approach infers knowledge from the facts/data differently. A fewthe facts/data differently. A few examples will help illustrate this…

Common form-based user interfacefor rule-based expert system

A Production System is the most familiar type of expert system, with the following common components -• A Collection of Rules or Knowledge Base linguistically describing the problem and situation, each with a set p

of requirements for some condition to be met (the antecedent) — triggering an action or actions to be taken (the consequent)

• An Inference Engine to implement abductive (‘guessing’ first to reduce search space), deductive (fact-driven) and inductive (goal-driven) reasoning by sequentially and repeatedly evaluating the prioritized rules

i li d f t d i f tusing supplied facts - producing new facts• If the antecedent fact(s) are known, then the consequent fact(s) can be derived or inferred and thus

knowledge is gained • An Explanation Facility to produce a record of how the reasoning was achieved

A K l d A i iti F ilit t id t t d t t k l d ith t i i• A Knowledge Acquisition Facility — to provide an automated way to enter knowledge without requiring explicit coding

Example- (repeated for each mole mapped)IF:

The patient … has [mole_n.changing] =yes Knowledge Base (h i i l )

Inference EngineWorking Memory (patient or group

facts)p [ _ g g] yAND: The patient … has [mole_n.shape] = oddAND: The patient … has [mole_n.color] = black

THEN:[Biopsy mole_n][Send sample mole2 to pathologist #1]

(heuristic rules)

Explanation F ilit

facts)

Knowledge Acquisition

Agenda

[Send sample mole2 to pathologist #1][Send sample mole2 to pathologist #2]

Confidence = 10

Facility Acquisition Facility

Pattern-recognition examines a set of data and either produces a value/values (such as a dosage), or, classifies the sample (categorize the patient’s condition). Popular classifiers include:

• k-Nearest Neighbor (k-NN), and • Margin Classifiers (important for medical use, includes Support Vector Machines. • Soft-Margin Classifiers (border on Fuzzy Classification), • Neural Net classifiers (radial basis function and probabilistic neural networks).Most approaches involve the following stages• Coding a series of [patient] measurements and representing them as a vector of features (Feature Vector)

[Sex Age HT WT T SBP /DBP LungCap Cholesterol Hemoglobin][M 47 72 208 98.6 118/78 6 190 14.5 ]

• Evaluating a pre-calibrated transfer function using the Feature Vector as inputs to generate a desired g p g p gvalue (i.e. duration of treatment) a(Sex) + b(Age) + c(HT) + d(WT) + e(T) + f(SBP) + g(DBP) + h(LungCap) + i(Cholesterol) + j(Hemoglobin) => patient dosage

Or• Comparing new “unknown” feature vector against others in a library of “knowns” using numerical techniques p g g y g q

until an acceptable match is located (this is referred to as Supervised Classification). • Classify sets of new feature vectors into categories (also called classes) by grouping like vectors into

clusters or natural classes (Unsupervised Classification)All pattern recognitions approaches can produce confidence measures (CF’s) to indicate how well an unknown

was assigned to a category

–

P tt f ll tiPatterns from collections of point measurements

Feature Vector = [a b c … n]

[abc

Patterns gathered from strip-chart type data

… n]

Patterns gathered from strip-chart type dataFeature Vector =[a(t) a(t+1) a(t+2) … a(t+n)]

ueria

ble

a va

luVa

r

Time (t)

–[M 47 72 208 98.6 118/78 6 190 16.5]

Category exemplar vectors and tolerance value

Euclidean distances

[F 47 72 208 98.6 118/78 6 190 13.5]

[M 42 72 208 98.6 118/78 6 190 16.5]

[M 35 72 208 98.6 118/78 6 190 14.1]

g y p

[M 30 72 208 98.6 118/78 6 190 15.5]

Unknown Vector so differentit i t

[F 28 72 208 98.6 118/78 6 190 14.2][M 24 72 208 98 6 118/78 6 190 15 2]

it requires new category

[F 22 72 208 98.6 118/78 6 190 14.5]

[M 24 72 208 98.6 118/78 6 190 15.2]

Unknown Vector within acceptance tolerance of existing category

[F 18 72 208 98.6 118/78 6 190 13.52

[M 24 71 206 98.8 117/79 6 188 14.9]

Fuzzy Logic for decision-making is especially effective in complex system where precision and significance become mutually exclusive. Even when ambiguity and only incomplete information is available - decisions are still required to be madeincomplete information is available - decisions are still required to be made. Inexact but weighted strategies help launch decision-making when little initial data are available

• Problem Measurements or Condition Variables are mapped into a Fuzzy Membership Set

• Linguistic descriptions (h d hi h(heavy dose, high temperature, low blood pressure, dark rash, etc) help define the Boundaries and Core of a fuzzy variable membership set

• Linguistic operators called hedges modify the geometry of a membership set using

Boundaries + Core = Support

Linguistic operators called hedges modify the geometry of a membership set using a mathematical formula (“Very” a = a2, “Slightly” a = √a., etc)

• Fuzzy variable membership sets are processed using Fuzzy Arithmetic/Logic/Calculus, producing Result Sets which are then Defuzzified (by pcentroid or other methods) into Actionable Values (dosage, duration, etc)

• Fuzzy Logic provides a mechanism for reasoning despite the presence of uncertainty. Fuzziness describes and manages ‘ambiguity’ - a different type of uncertainty than random chance or [conditional] probabilities Ambiguity involvesuncertainty than random chance or [conditional] probabilities. Ambiguity involves possibilities and belief, a measurement of information and knowledge, not a statistic

• Fuzzy not suitable where precision is a requirement, but, experimental medicine y p q pdoesn’t require this, i.e. dosages, durations of treatment, combinations, etc

• Fuzzy and Rule-based decision-making are often combinedIF patienttemp is normal THEN aspirindose is noneIF patienttemp is low THEN aspirindose is noneIF patienttemp is high THEN aspirindose is smallIF patienttemp is very high THEN aspirindose is largeIF patientpain is typical THEN asprindose is averageIF patientpain is severe THEN asprindose is extra

If patienttemp is high and patientpain is typical, the aspirindose is both small andp p p p p paverage, and they are geometrically combined and the new centroid is computed (Defuzzification) to determine the final dosage

Imagery Analysis is itself a specialized type of expert system• Images coded into intensity or color values, and stacked into layers• The same pattern-recognition and classification used for vectors can be

expanded and applied to 2-D, 3-D, multi-dimensional, and hyper-p pp , , , ypdimensional data (X-ray, CT and CAT, PET, MRI)

• Results are typically in the form of imagery as well — supporting easy human interpretationp

Patterns gathered from imagery type data

5 5 0 6 0 4 0 5 05 5 5 5 6 5 0 0 45 5 0 0 0 5 0 5 05 0 0 5 5 0 8 7 05 0 0 5 5 0 8 8 0

Feature Vector = [ ]5 0 0 5 5 0 8 8 00 5 0 0 0 7 5 7 0

–

X-ray Magnetic Resonance Imaging (MRI)

Positron Emissions Tomography (PET)

X-ray Computed TomographyPET/CT

(216) different Medical Practice Management software systems available (recent Capterra search Aug2009)

Few actual Medical Diagnostic Applications — mainly because of liability issues. The few that ‘are’ out there are literally plastered with disclaimer statementsstatements

This is strong defense that medical expert systems are best used internally by physicians not potential patients If currently available diagnosticby physicians, not potential patients. If currently available diagnostic knowledge is captured and used, then it will be available in the future when legal issues are resolved

P f Ed d F i nb m E t t m C n i M ll n Uni itProfessor Edward Feigenbaum — Expert systems, Carnegie Mellon UniversityProfessor Edward H. Shortliffe — MYCIN, Standford UniversityProfessor Charles Forgy — Rete scheduling algorithm, Carnegie Mellon

UniversityProfessor Lotfi Zadeh — Fuzzy logic, University of California, BerkeleyProfessor Martha Evens — CIRCSim-Tutor, IIT, Northwestern UniversityProfessor Harry Pople — Expert Systems (of the University of Pittsburgh)Dr. Jack Meyers — Medical diagnostician, University of Pittsburgh

• DENDRAL, 1960’s• MYCIN, 1970’s• INTERNIST — Diagnose Internal Medicine problems, 1975• MOLGEN — Plans experiments in molecular genetics, 1977p g• CASNET — Medical Decision-making for Glaucoma Treatment, 1978• DENDRAL — Identify Organics by Mass-Spec Signatures, 1978• CENTAUR — Analysis of Pulmonary Tests 1979• CENTAUR — Analysis of Pulmonary Tests, 1979• EMYCIN — (empty MYCIN), 1980• NEOMYCIN — Another derivative of MYCIN, 1980’s• CADUCEUS — Diagnosis of blood-borne disease, mid-1980’s• CIRCSim-Tutor — Teaches cardiovascular medicine, 1993• Easy Diagnosis (http://easydiagnosis.com/ ), MatheMEDics, 2008g ( p g )• Medical Expert On-Line (http://med-expert.net/ ), 2009

There are many different clinical tasks to which Medical Expert Systems canThere are many different clinical tasks to which Medical Expert Systems can be applied:

• Alerts and Reminders - Real-time warning of a patient’s condition • Diagnostic Assistance Formulation of likely diagnoses• Diagnostic Assistance - Formulation of likely diagnoses • Therapy and Treatment Critiquing and Planning - Critiquing systems can

look for inconsistencies, errors and omissions in a treatment plan P i i D i i S S P i i f di i d• Prescription Decision Support Systems - Prescription of medications and can assist checking for drug-drug interactions and dosage errors

• Information Retrieval - Assist in formulating appropriately specific and li i l iaccurate clinical queries

• Image Recognition and Interpretation - X-rays, angiograms, CT, CAT, PET, and MRI scans can be automatically interpreted flagging potentially

b l i f d il d h iabnormal images for detailed human attention

• Anecdotal record-keeping (patient “charts” often contain physician notes more so than tabulated values)

• Disparate records from multiple physicians/hospitals• Diagnoses based upon significantly differing backgrounds and experiencesg p g y g g p• Non-standardized medical (clinic or hospital) patient information systems• Staff technophobia and limited time for learning new systems

• Liability issues — who’s at fault if errors are encountered? If an expert systemLiability issues who s at fault if errors are encountered? If an expert system produces an incorrect solution when used to help you diagnose your vacuum cleaner — no one cares. But if an expert system is used to diagnose a patient and it errors — everyone cares, patients, original domain experts, and software developers alikedevelopers alike

• Medical (life and death) expert systems suffer from an application-specific phenomena- incomplete exemplars. (If a patient recovers — they often cannot be completely inspected (dissected) to see “what happened”, and even p p pppatients who do die don’t always allow the body to be examined to evaluate the applied solution.) This leads to incomplete and even corrupted learning by any human expert or system intended to emulate that human expert

• Shortage of industry/field-wide standards for information exchangeShortage of industry/field wide standards for information exchange

These barriers led the away from the adoption of full-authority expert systems to something more supportive to aid human decision-making…g pp g

"Clini l D i i n S t S t m link h lth b ti n ith h lth"Clinical Decision Support Systems link health observations with health knowledge to influence health choices by clinicians for improved health care"

• Decision Support Systems often can provide many “acceptable” solutions• Decision Support Systems often can provide many acceptable solutions; their true value is then in guiding the physician away from “poor” solutions and disasters. This also has been shown to be the more efficient and resource-economical strategy as well Much much more time and effortresource economical strategy as well. Much much more time and effort can be spent finding and ideal or optimal solution when the better use of the resources would be toward avoiding anti-optimal solutions and just providing ranked acceptable choices p g p

Medical Expert System and Clinical Decision Support System design aided by shared standards for information

Health Level 7 (HL7)• HL7 creates international standards (i.e. the Arden syntax) for inter-( y )

system and inter-organization messaging, for decision support, clinical text document mark-up, user interface integration as well as a health data model and a message development methodology

• "Level 7" refers to the top level (the application layer) of the seven-layer International Standards Organization's (ISO) communications model for Open Systems Interconnection (OSI). The application level addresses definition of the data to be exchanged

• HL7 has 4,400 members, has achieved ISO status for its standards, and is moving toward harmonization with the European CEN standards

UMLS Semantic Networks• The UMLS Semantic Network is one of three UMLS Knowledge Sources

developed as part of the Unified Medical Language System (UMLS) project. The network provides a consistent categorization of all concepts represented in the UMLS Metathesaurus

Medical Expert Systems and Clinical Decision Support Systems can play p y pp y p yimportant roles in Evidence-based Medicine (EBM)

• Applies evidence gained from the scientific method to certain parts of medical practice. It seeks to assess the quality of evidence relevant to p q ythe risks and benefits of treatments (including lack of treatment)

• EBM used techniques from science, engineering, and statistics, such as meta-analysis of medical literature, risk-benefit analysis, and randomized y ycontrolled trials (RCTs)

• The decision-reporting capability of an expert or decision-support system produces the proper evidence required for effective EBMp p p q

• Medical Patient Information is gathered and stored in a variety of forms (measurements, charts, imagery, etc.), therefore a Hybrid Expert System should combine all of these sources to perform the best possible decision making.

• Expert systems Heuristic Rules

Hybrid Expert System

Knowledge Base

should be designed to aid planning

i

Heuristic Rules

Working Memory (patient or group facts)

Instrument Pattern

R

Integration Engine

experiments and clinical studiesH b id E S

g p )

Coordination Strategy

Recognition

Image Analysis

• Hybrid Expert Systemwould keep departmentknowledge the most current and promote their research

Explanation Facility


Facility

current and promote their research to the leading edge in its field

• Capturing and documenting gained knowledge using semantic network diagrams — as an aid to analysis and planning

• Case Study Dissection and• Case Study Dissection and anecdotal record inspection and declarative reduction into data (facts)

• Implement E-mail and SMS Text messaging input and output from Medical Expert SystemSystem

• Support partial input of needed rule facts (data) followed by piece-meal output availabilityoutput availability

• Patient treatment historyP ti t hi t• Patient response history

• Clinical study results• Heuristic rules as the back-end interface combining varied data• Data-mining instrument responses to provide valuable inputs to rules• Introduction of instrument measurements and trends into rule-based

system, including medical-tailored data-loggerIF (feature x) AND NOT (feature y) THEN (action b)

Knowledge Base (heuristic rules)

Pattern-Recognition

Engine feature x, feature y,feature y, feature z

An Expert Decision Support System tailored for the research and treatment of melanoma would adopt the features and strategies for Medical Expert Systems described above, but should also explore refinements and characteristics unique to its own topic

• Document treatments applied (as a series of heuristics), and track performance (as facts). This serves the preservation task

• Analyze clinical trials results, and plan next studies by watching for patterns in patient treatment and response across individuals and larger numbers of patients - treatments and responses are patterns for predicting performance

• Accept results from case studies and predict future performance of treatments

• Decision report to provide (doctor or researcher) source information about h l nd nfid n f t tt h d t h s b din t d isi neach rule, and confidence factor attached to each subordinate-decision

• Heuristics regarding treatment to be physician-specific, satisfying multiple rules for the same facts thereby reinforcing confidence for decisions when multiple physicians have same experience on a subject, and attenuating p p pconfidence where they have differing experience

• Decision report to be generated as check-list and ‘checked’ by human expert contributors for accuracy and appropriateness, saving significant amounts of time, and providing documented paper-trail for insurance , p g p pclaims and defense during legal actions

• System decisions not different from human experts, but more complete, and arrived at much more quickly

• Specialized reports to be prepared to help analyze clinical trial results• Specialized reports to be prepared to help analyze clinical trial results, plan improvements, and select new directions.

• Architecture to utilize commercially-supported development environment• Operate using integrated Mayo Melanoma-specific modules, interfacing to

Mayo Medical Informatics • Server-based, in parallel to existing informatics but not integrated — no , p g g

downtime for existing informatics, only new reports to be defined

Phase 0 — Feasibility Study

Phase I — Heuristic Infrastructure

Phase II — Pattern-Recognition Extension Module

Phase II — Image Analysis Extension ModulePhase II Image Analysis Extension Module

Phase IV — Expansion of System, Licensing,Re use of ArchitectureRe-use of Architecture

• Analysis of Existing Knowledge (staff experience, data sources, formats, generation, conversion, support, and maintenance)

• Design Architecture and Draft Plan• Model one sub-type of melanoma and a subset of its treatments and yp

feedback first, then scale up this complete start-to-finish path to include more, use first example to accurately estimate cost, time, and effort

• Complete Verification and Validation testing to insure accuracy and p g yrepeatability

• Prepare federal R&D proposals — NIH Small Business Innovation Research (SBIR)/Small business Technology Transfer Research (STTR) program —( ) gy ( ) p g“Expert Decision Support System for Melanoma Research and Treatment”, Small Business — Vogtland I&R, medical facility partner — Mayo Clinic, explore women & minority contribution opportunities

–

• Dissection and Declarative Coding of Existing Patient Records — Creation of First Production System Heuristics

• Interviewing [Interrogation] of Melanoma Domain Experts — Refinement of Production System Heuristics

• Coding Production System Heuristics into Version 1.0 Web-based Melanoma Research and Treatment Expert System Heuristic

R lHybrid Expert System

Knowledge Base

• Complete Verification and Validation testing to insure accuracy and repeatability

Rules

Working Memory (patient or group

facts)Coordination Strategy

Integration Engine

• Licensing and SubscriptionDistribution



Facility

Strategy

y

–

• Integration of Pattern Recognition for Patient & Study Group Symptoms -> Condition or Disease

• Produce PatternRec Extension Module [Version 2.0] Web-based Melanoma Research and Treatment Expert System

• Complete Verification and Validation testing to insure accuracy and repeatability

• Licensing and Subscription Heuristic R l


Knowledge Base

g pDistribution

Rules



Instrument Pattern

Recognition

Integration Engine



Facility

Strategy

y

–

• Integration of Imagery Analysis for PET, CT, MRI, X-Ray, etc• Produce ImageAnalyst Extension Module [Version 3.0] Web-based

Melanoma Research and Treatment Expert System• Complete Verification and Validation testing to insure accuracy and p g y

repeatability• Licensing and Subscription

Distribution Heuristic R l


Knowledge Base

Rules



Instrument Pattern

Recognition

Integration Engine


Facility

StrategyImage

Analysis


y

–• Refinement of Integrated Pattern-Rec + Image-Analysis + Production

System Heuristicsy• Introduction of Hybrid Self-Learning (pattern-rec and imagery into

Heuristics)• Complete Verification and Validation testing to insure accuracy andComplete Verification and Validation testing to insure accuracy and

repeatability• Licensing and Subscription

DistributionLab data Rules

Distribution• Re-Use of Melanoma

Architecture for Additional DiseasesAdditional Diseases

Melanoma

L k i

Lymphoma Clinical data Validation and Approval

Leukemia

Day-to-day operations

Once up and performing, melanoma expert system can be self-supporting by Licensing and Subscription fees — allowing addition to and use by other g p g yhospitals and clinics

Melanoma-specific Knowledge

Either content or system architecture can be

licensed

Non-melanoma topic

InternetLicenseManagerSubscribing clinics and

research programs

Contributors pay reduced user fee

Originating Mayo department users

Initial melanoma system will become a model for additional systems for other diseases and physical conditions, leading to a finalized matrix expert system that can cross domains when appropriate with simple rules to combine rules from different fields.

Melanoma Lymphoma Leukemia

InternetLicenseManagerSubscribing clinics and

research programs

Contributors pay reduced user fee

Originating Mayo department users

“ ”Important concept — the Expert System is always improving — knowledge is

always being gained. The addition of new rules and classifications adds to the knowledge base and reinforces the existing rules increasing theirthe knowledge base and reinforces the existing rules increasing their confidence. Failures and errors provide heuristics for what did not work and under what conditions it didn’t work. This is very important- the concept of providing information of what not to do as well as what to do.ty concept of providing information of what not to do as well as what to do. Both have value.

& Capabilit

Additional Domain Expert Interrogation

Self-learning through use and new data

Introduction of Imagery Analysis

Usability &

Initial Dissection of Medical

Introduction of Pattern Recognition



…(initial) Domain

of Medical Records into Heuristicsand …


Effort & Time (measured in system usage)

…(initial) Domain Expert Interrogation

The expertise, software tools, and components to complete this project all exist, but this work needs to be initiated -to preserve and share the knowledge gained by Mayo’s experience in treating melanoma, and in treating other diseases

’UML Use-Case DiagramOriginal Expeditionary

Fighting Vehicle (EFV)

18.9˝15.3˝

Fighting Vehicle (EFV) Engineering Models

8 9

-5 0 5 100

5000

10000

234567HSU 1

Road Wheel Height (inches)

HS

U L

oad

(lbf)

Front 3 HSU CurveRear 4 HSU CurveLoaded HSU

UML Object Diagram

UML Flow DiagramDiagram

UML Expert System Data Flow Diagram

’

Expert Decision-Support System for

UML Deployment

Diagram

p ppHydro-pneumatic Suspension Unit

(HSU) design and operation

Screen captures of portions of p pthe HSU and EFV Decision-

Support System User Interface

Sep2009 Introduction to Medical Expert Decision Support Systems for Mayo Clinic

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