diagnosis

Diagnosis and Interpretation

• We concentrate on diagnosis and interpretation because

historically they are significant problems that AI has

addressed

– And there are numerous and varied solutions, providing us

with an interesting cross-section of AI techniques to examine

• Diagnosis is the process of determining whether the

behavior of a system is correct

• If incorrect, which part(s) of the system is(are) failing

– We often refer to the result of a diagnosis is one or more

malfunctions

– The system being diagnosed can be an artificial system (man-

made) or natural system (e.g., the human body, the ecology)

• man-made systems are easier to diagnose because we understand the

systems thoroughly enough to develop an accurate model

• Interpretation is a related problem, it is the process of

explaining the meaning of some object of attention

Data Driven Processes

• While both diagnosis and interpretation have goals

of “seeking to explain”, the processes are triggered

by data

– We use the data (symptoms, manifestations, observations)

to trigger possible reasons for why those data have arisen

• Thus, these problems are distinct from goal-driven

problems

– Like planning, design, and control

• control encompasses planning, interpretation, diagnosis and

possibly prediction

• One way to view diagnosis/interpretation is that

given data, explain why the data has arisen

– Thus, it is an explanation-oriented process

• the result of the process is an explanation which attempts to

describe why we have the resulting behavior (malfunctions or

observations)

• we will reconsider this idea (explanation as a process) later

The Diagnostic Task

• Data triggers causes (hypotheses of malfunctions, or potential

diagnoses), typically an associational form of knowledge

• Hypotheses must be confirmed through additional testing and

inspection of the situation

• Hypotheses should be as specific as possible, so they need to be

refined (e.g., given a general class of disease, find the most

specific subclass)

Forms of Interpretation

• The idea behind interpretation is that we are trying to

understand why something has happened

– Diagnosis is a form of interpretation in that we are trying to

understand a system’s deviation from the norm

• what caused the system to deviate? what components have broken down?

why?

• Diagnosis is a form of interpretation, but there are other forms

– Data analysis – what phenomenon caused the data to arise, e.g.,

studying astronomical phenomena by looking at radio signals, or

looking at blood clots and decided on blood types

– Object identification – viewing a description (in some form, whether

visual or data) of an object, what is the object

– Speech recognition – interpret the acoustic signal in terms of

words/meanings

– Communication – what is the meaning behind a given message?

This can be carried over to analysis of artwork

– Evidence analysis – trying to decipher the data from a crime scene to

determine what happened, who committed the crime and why

– Social behavior –explaining why someone acted in a particular way

Some Definitions

• Let us assume that our knowledge of a given system

is contained as a model

– A diagnosis is a particular hypothesis of how the system

differs from the model

• what component(s) is(are) not functioning as modeled?

– A diagnosis is a description of one possible state of the

system where the state is not the “normal state”

– A consistency-based diagnosis is a diagnosis where each

component of the system is labeled as either normal or

abnormal (functioning correctly or not) such that the

description is consistent with the observations

• If there are n components in a system, there are 2n different

diagnoses because we must consider that multiple components

may fail

– A minimal diagnosis is a diagnosis consisting of some set

of components C such that there is no consistent diagnosis

that is a subset of C

First Interpretation System

• The system Dendral, from 1966, was given mass

spectrogram data and inferred the chemical

composition from that data

– The input would be the mass of the substance along with

other experimental lab data

– Dendral would apply knowledge of atomic masses,

valence rules and connectivity among atoms to determine

combinations and connections of the atoms in the

unknown compound

• The number of combinations grows exponentially with the size

(mass) of the unknown compound)

– Dendral used a plan-generate-test process

• First, constraints would be generated based on heuristic

knowledge of what molecules might appear given the initial

input and any knowledge presented about the unknown

compound

Dendral Continued

• The planning step would constrain the generate step

– At this step, graphical representations of possible molecules would

be generated

– The constraints are necessary to reduce the number of possible

graphs generated

• The final step, testing, attempts to eliminate all but the correct

representations

– Each remaining graph is scored by examining the candidate

molecular structure and comparing it against mass spectrometry rules

and reaction chemistry rules

– Structures are discarded if they are inconsistent with the spectrum or

known reactions

– Any remaining structures are presented the operator

• At this point, the operator can input additional heuristic rules

that can be applied to this case to prune away incorrect

structures

– These rules are added to the heuristics, so Dendral “learns”

– A thorough examination is presented in

http://profiles.nlm.nih.gov/BB/A/B/O/M/_/bbabom.pdf

Mycin

• Mycin was the next important step in the evolution of

AI expert systems and AI in medicine

– The first well known and well received expert system, it

also presented a generic solution to reasoning through rules

– It provided uncertainty handling in the form of certainty

factors

– After creating Mycin, some of the researchers developed

the rule-based language E-Mycin (Essential or Empty

Mycin) so that others could develop their own rule-based

expert systems

• Mycin had the ability to explain its conclusions by

showing matching rules that it used in its chain of logic

• Mycin outperformed the infectious disease experts

when tested, coming to an “acceptable” therapy in 69%

of its cases

– A spinoff of Mycin was a teaching tool called GUIDON

which is based on the Mycin knowledge base

The Importance of Explanation

• The Dendral system presented an answer but did not

explain how it came about its conclusions

• Mycin could easily generate an explanation by

outputting the rules that matched in the final chain of

logic

– E.g., rule 12 & rule 15  rule 119  rule 351

– A user can ask questions like “why was rule 351

selected?” to which Mycin responds by showing the rule’s

conditions (lhs) and why those conditions were true

– The reason why a rule is true is usually based on previous

rules being true leading to conclusions that made the

given rule true

• By being able to see the explanation, one can feel

more confident with the system’s answers

– But it is also a great tool to help debug and develop the

knowledge base

Mycin Sample Rules

RULE116

IF: 1) the identity of ORGANISM-1 is not known

** 2) the gram stain of ORGANISM-1 is not known**

3) the morphology of ORGANISM-1 is not known

4) the site of CULTURE-1 is csf

5) the infection is meningitis

6) the age (in years) of the patient is less than equal to .17

THEN: There is weakly suggestive evidence (. 3) that the

category of ORGANISM-1 is enterobacteriaceae



RULE050

IF: 1) the morphology of ORGANISM-1 is rod

2) the gram stain of ORGANISM-1 is gramneg

3) the aerobicity of ORGANISM-1 is facultative

** 4) the infection with ORGANISM-1 was acquired while the

patient was hospitalized**

THEN: There is evidence that the category of ORGANISM-1

is enterobacteriaceae

Systems Generated From Emycin

• SACON – Structural Analysis CONsultant

IF: 1) The material composing the sub-structure is one of the metals, and

2) The analysis error that is tolerable is between 5% and 30%, and

3) Then non-dimensional stress of the sub-structure > .9 , and

4) The number of cycles the loading is to be applied is between 1000 and10000

THEN: It is definite (1.0) that fatigue is one of the stress behavior phenomena in

the sub-structure

• Puff – pulmonary disorders

– originally implemented in Emycin before being re-

implemented as an OO system

I f : 1) The mmf/mmf-predicted ratio is [35..45] & the fvc/fvc-predicted ratio > 88

2) The mmf/mmf-predicted ratio is [25..35] & the fvc/fvc-predicted ratio < 88

Then : There is suggestive evidence (.5) that the degree of obstructive airways

disease as indicated by the MMF is moderate, and it is definite (1.8) that the

following is one of the findings about the diagnosis of obstructive airways

disease: Reduced mid-expiratory flow indicates moderate airway obstruction.

A Fuzzy Logic Approach

• The process is one of

– Fuzzifying the inputs

• blood pressure of 145 mmHg can be denoted as {low/0,

medium/.4, high/.6}

– Fuzzy reasoning

• applying rules similar to Mycin

– recall that fuzzy systems do poorly with lengthy chains of rules, so we

will primarily use fuzzy logic in diagnosis when there are few rules

and limited chains of logic

• we use fuzzy logic and set theory to compute AND, OR, NOT,

Implication, Difference, etc. as needed for the rules

– Fuzzy classes

• given the result of our rules, we defuzzify by identifying which

class (malfunction(s)/diagnosis(es)) is rated the highest

– FL has been used for automotive diagnosis, clinical lab

test interpretation, mammography interpretation, …

Analyzing Mycin’s Process

• A thorough analysis of Mycin was performed and it

was discovered that the rule-based approach of

Mycin was actually following three specific tasks

– Data are first translated using data abstraction from

specific values to values that may be of more use (e.g.,

changing a real value into a qualitative value)

– The disease(s) is then classified

– The hypothesis is refined into more detail

• By considering the diagnostic process as three

related but different tasks, it allows one to more

clearly understand the process

– With that knowledge, it becomes easier to see how to

solve a diagnostic task – use classification

Classification as a Task

• One can organize the space of diagnostic conclusions

(malfunctions) into a taxonomy

– The diagnostic task is then one of searching the taxonomy

• Coined hierarchical classification

– The task can be solved by establish-refine

• Attempt to establish a node in the hierarchy

• If found relevant, refine it by recursively trying to establish any

of the node’s children

• If found non-relevant, prune that portion of the hierarchy away

and thus reduce the complexity of the search

• How does one establish a node as relevant?

– Here, we can employ any number of possible approaches

including rules

• Think of the node as a “specialist” in identifying that particular

hypothesis

• Encode any relevant knowledge to recognize (establish) that

hypothesis in the node itself

Supporting Classification

• The establish knowledge can take on any number of different

forms

– Rules (possibly using fuzzy logic or certainty factors, or other)

– Feature-based pattern matching

– Bayesian probabilities or HMM

– Neural network activation strength

– Genetic algorithm fitness function

• In nearly every case, what we are seeking are a set of pre-

determined features

– Which features are present? Which are absent?

– How strongly do we believe in a given feature?

• If the feature is not found in the database, how do we acquire

it?

– By asking the user? By asking for a test result? By performing

additional inference?

– Notice that in the neural network case, features are inputs whereas in

most of the rest of the cases, they are conditions usually found on the

LHS of rules

Feature-based Pattern Matching

• A simple way to encode associational knowledge to support a

hypothesis is to enumerate the features (observations,

symptoms) we expect to find if the hypothesis is true

– We can then enumerate patterns that provide a confidence value that

we might have if we saw the given collection of features

• Consider for hypothesis H, we expect features F1 and F2 and

possibly F3 and F4, but not F5 where F1 is essential but F2 is

somewhat less essential

– F1 F2 F3 F4 F5 Result

– yes yes yes yes no confirmed

– yes yes ? ? no likely

– yes ? ? ? no somewhat likely

– ? yes ? ? no neutral/unsure

– ? ? ? ? yes ruled out

– ? means “don’t care”

• We return the result from the first pattern to match, so this is in

essence a nested if-else statement

Data Abstraction

• In Mycin, many rules were provided to perform data

abstraction

– In a pattern matching approach, we might have a feature of

interest that may not be directly evident from the data but the

data might be abstracted to provide us with the answer

• Example: Was the patient anesthetized in the last 6 months?

• No data indicates this, but we see that the patient had surgery 2 months

ago and so we can infer that the patient was anesthetized

• Data abstractions might be domain specific

– In which case we have to codify each inference as shown

above

• Or may be domain independent

– Such as temporal reasoning or spatial reasoning

• Another form is to discard a specific value in favor of a

more qualitative value (e.g., temperature 102 becomes

“high fever”)

Example 1: Automotive Diagnosis

Engine Trouble





Fuel Electrical Air and Exhaust Spark Control



Oxygen Sensor

Fuel Pump EGR Valve Spark Plug

Relay Battery Solenoid Mass Air Flow Sensor

Spark Plug

Voltage Air Filter Wire Throttle Position Sensor

Fuel Pump Fuse

Regulator

Catalytic Ignition Coil Knock Sensor

Fuel Pump Converter

Manifold Absolute

Fuel Injector Idle Air Control Pressure Sensor

Solenoid

Manifold Air

Fuel Pressure Idle Speed Control Temperature Sensor

Regulator Motor

Coolant Temperature

Fuel Filter Sensor



Engine Control Module



Crankshaft Position Sensor



Camshaft Position Sensor



Vehicle Speed Sensor

Example 2: Syntactic Debugging

Ex 3: Linux User Classification

Lack of Differentiation

• Notice that through the use of simple classification

(what is called hierarchical classification), one does

not differentiate among possible hypotheses

– If two hypotheses are found to be relevant, we do not have

additional knowledge to select one

• What if X and Y are both established with X being more certain

than Y, which should we select?

• What if X and Y have some form of association with each other

such as mutually incompatible, or jointly likely?

• We would like to employ a process that contains

such knowledge as to let us select only the most

likely hypothesis(es) given the data

– In a neural network, we would only select the most likely

node, and similarly for an HMM, the most likely path

Abduction

• This leads us to abduction, a form of inference first termed

by philosopher Charles Peirce

– Peirce saw abduction as the following:

• Deduction says that

– If we have the rule A  B

– And given that A is true

– Then we can conclude B

• But abduction says that

– If we have the rule A  B

– And given that B is true

– Then we can conclude A

– Notice that deduction is truth preserving but abduction is not

– We can expand the idea of abduction to be as follows:

• If A1 v A2 v A3 v … v An  B

• And given that B is true

• And if Ai is more likely than any other Aj (1<=j<=n), then we can infer

that Ai is true

– for this to work, we need a way to determine which is most likely

Inference to the Best Explanation

• Another way to view abduction is as follows:

– D is a collection of data (facts, observations, symptoms) to explain

– H explains D (if H is true, then H can explain why D has appeared)

– No other hypothesis explains D as well as H does

– Therefore H is probably correct

• Although the problem can be viewed similar to classification

– we need to locate an H that accounts for D

– We now need additional knowledge, explanatory knowledge

• What data can H explain?

• How well can H explain the data?

• Is there some way to evaluate H given D?

– Additionally, we will want to know if

• H is consistent

• Did we consider all H’s in our domain?

• What complicates generating a best explanation is that H and

D are probably not singletons but sets

Continued

• Assume H is a collection of hypotheses that can all

contribute to an explanation, H = {H1, H2, H3, …, Hn}

• D is a collection of data to be explained, D = {d1, d2,

d3, …, dn}

– a given hypothesis can account for one or more data (e.g., H3

can explain {d1, d5})

– assume that we have ranked all elements of H with some

scoring algorithm (Bayesian probability, neural network

strength of activation, feature-based pattern matching, etc)



• The abductive process is

to generate the best

subset of H that can

explain D

– what does best mean?

Ways to View “Best”

• We will call a set of hypotheses that can explain the data as a

composite hypothesis

• The best composite hypothesis should have these features

– Complete – explains all data (or as much as is possible)

– Consistent – there are no incompatibilities among the hypotheses

– Parsimonious – the composite has no superfluous parts

– Simplest – all things considered, the composite should have as fewer

individual hypotheses as possible

– Most likely – this might be the most likely composite or the

composite with the most likely hypotheses (how do we compute

this?)

• In addition, we might want to include additional factors

– Cheapest costing (if applicable) – the composite that would be the

least expensive to believe

– Generated with a reasonable amount of effort – generating the

composite in a non-intractable way (abduction is generally an NP-

complete problem)

Internist – Rule based Abduction

• One of the earliest expert systems to apply

abduction was Internist, to diagnose internal

diseases

– Internist was largely a rule-based system

– The abduction process worked as follows

• Data trigger rules of possible diseases

• For each disease triggered, determine what other symptoms are

expected by that disease, which are present and which are absent

– Generate a score for that disease hypothesis

• Now compare disease hypotheses to differentiate them

– If one hypothesis is more likely, try to confirm it

– If many possible hypotheses, try to rule some out

– If a few hypotheses available, try to differentiate between them by

seeking data (e.g., test results) that one expects that the others do not

– The diagnostic conclusion are those hypotheses that still

remain at the end that each explain some of the data

Neural Network Approach

• Paul Thagard developed ECHO, a system to learn explanatory

coherence

– ECHO was developed as a neural network where nodes represent

hypotheses and data

– links represent potential explanations between hypotheses and data

– and hypothesis relationships (mutual incompatibilities, mutual

support, analogy)

• Unlike a normal neural network, nodes here represent specific

concepts

– weights are learned by the strength of relationships are found in test

data

• In fact, the approach is far more like a Bayesian network with

edge weights representing conditional probabilities (counts of

how often a hypothesis supports a datum)

– When data are introduced, perform a propagation algorithm of the

present data until the hypothesis nodes and data nodes have reached

a stable state (similar to a Hopfield net) and then the best

explanation are those hypothesis nodes whose probabilities are

above a preset threshold amount

Ex: Evolution (DH) vs Creationism (CH)

Probabilistic Approach(es)

• Pearl’s Belief networks and the generic idea behind

the HMM are thought to be abductive problem

solving techniques

– Notice that there is no explicit coverage of hypotheses to

data, for instance, we do not select a datum and ask “what

will explain this?”

– Instead, the solution is derived to be the best explanation

but where the explanation is generated by finding the most

probable cause of the collection of data in a holistic

approach

• The typical Bayesian approach contains probabilities

of a hypothesis (state) being true, of a hypothesis

transitioning to another hypothesis, and of an output

being seen from a given hypothesis

– But there is no apparent mechanism to encode hypothesis

incompatibilities or analogies

Example

• In the diagram of a

system

– I represents inputs

– O represents

outputs

– Ab represent

component parts

that might be

malfunctioning

• In the formula

– dc is a diagnostic

conclusion

(malfunction)

based on input and

output i, o

The Peirce Algorithm

• The previous strategies assume that knowledge is

available in either a rule-based or probabilistic-based

format

• The Peirce algorithm instead uses generic tasks

– The algorithm has evolved over the course of construction

several knowledge-based systems

• The basic idea is

– Generate hypotheses

• this might be through hierarchical classification, neural network

activity, or other

– Instantiate generated hypotheses

• for each hypothesis, determine its explanatory power (what it can

explain from the data), hypothesis interactions (for the other

generated hypotheses, are they compatible, incompatible, etc) and

some form of ranking

– Assemble the best explanation

• see the next slide

The Assembly Algorithm

• Examine all data and see if there are any data that can only be explained

by a single hypothesis

– such a hypothesis is called an essential hypothesis

• Include all essential hypotheses in the composite

• Propagate the affects of including these hypotheses (see next slide)

• Remove from the data all data that can be explained

• Start from the top (this may have created new essentials)

• Examine remaining data and see if there are any data that can only be

explained by a superior hypothesis

– such a hypothesis would clearly beat all competitors by having a much

higher ranking

• Include all superior hypotheses in the composite, propagate and remove

• Start from the top (this may have created new essentials)

• Examine remaining data and see if there are any data that can only be

explained by a better hypothesis

– such a hypothesis would be better than all competitors

• Include all better hypotheses in the composite, propagate and remove

• Start from the top (this may have created new essentials)

• If there are still data to explain, either guess or quit with unexplained

data

Propagation

• The idea behind the Peirce algorithm is to build on islands of

certainty

– If a hypothesis is essential, it is the only way to explain

something, it MUST be part of the best explanation

• If a hypothesis is included in the composite, we can leverage

knowledge of how that hypothesis relates to others

– If the hypothesis, say H1, is incompatible with H2, since we

believe H1 is true, H2 must be false, discard it

– If hypothesis H1 is very unlikely to appear with H2, we can

downgrade H2’s ranking

– If hypothesis H1 is likely to appear with H2, we can either

reconsider H2 or just bump up its ranking

– If hypothesis H1 can be inferred to be H2 by analogy, we can

include H2

• Since H1 was included because it was the only (or best) way

to explain some data, we build upon that island of certainty

by perhaps creating new essentials because H1 is

incompatible with other hypotheses

Layered Abduction

• For some problems, a single data to hypothesis

mapping is insufficient

– Either because we have more knowledge to bring to bear

on the problem or because we want an explanation at a

higher level of reasoning

• For instance, in speech recognition, we wouldn’t want to just

generate an explanation of the acoustic signal as a sequence of

phonetic units

• So we map the output of one level into another

– The explanation of one layer becomes the input of the next layer – we

explain the phonetic unit output as a sequence of syllables, and we

explain the syllables as a sequence of words, and then explain the

sequence of words as a meaningful statement

– We can use partially formed hypotheses at a higher level

to generate expectations for a lower layer thus giving us

some top-down guidance

Example: Handwritten

Character Recognition (CHREC)

Overall Architecture

• The system has a search space of hypotheses

– the characters that can be recognized

• this may be organized hierarchically, but here, its just a flat

space – a list of the characters

– each character has at least one recognizer

• some have multiple recognizers if there are multiple ways to

write the character, like 0 which may or may not have a

diagonal line from right to left

After characters

are generated for

each character in

the input, the

abductive

assembler selects

the best ones to

account for the

input

Explaining a Character

• The features (data) found to be explained for this character

are three horizontal lines and two curves

• While both the E and F characters were highly rated, “E”

can explain all of the features while “F” cannot, so “E” is

the better explanation

Top-down Guidance

• One benefit of this approach is that, by using

domain dependent knowledge

– the abductive assembler can increase or decrease

individual character hypothesis beliefs based on

partially formed explanations

– for instance, in the postal mail domain, if the

assembler detects that it is working on the zip code

(because it already found the city and state on one

line), then it can rule out any letters that it thinks it

found

• since we know we are looking at Saint James, NY, the

following five characters must be numbers, so “I” (for one of

the 1’s, “B” for the 8, and “O” for the 0 can all be ruled out

(or at least scored less highly)

Full

Example in

a Natural

Language

Domain

Model-based Diagnosis: Functional

• In all of our previous examples of diagnosis and

interpretation, our knowledge was associational

– We associate these symptoms/data with these

diseases/malfunctions

• This is fine when we do not have a complete understanding the

system

– Medical diagnosis

– Speech recognition

– Vision understanding

– What if we do understand the system?

• E.g., a human-made artifact

– If this is the case, we should be able to provide

knowledge in the form of the function that a given

component will provide in the system and how that

function is achieved through its behavior (process)

• Debugging can be performed by simulating performance with

various components not working

The Clapper Buzzer

• This mechanical device works as follows:

– When you press the button (not shown) it completes the circuit

causing current to flow to the coil

– When the magnetic coil charges, it pulls the clapper hand toward it

– When the clapper hand moves, it disconnects the

circuit causing the coil to stop pulling the hand and

then hand falls back, hitting a bell (not shown)

causing the ringing sound

– This also reconnects the circuit, and so this process

repeats until the button is no longer pressed

Generating a Diagnosis

• Given a functional representation, we can reason

over whether a function can be achieved or not

– Hypothetical or “what would happen if” reasoning

• What would happen if the coil was not working?

• What would happen if the battery was not charged?

• What would happen if the clapper arm were blocked?

– We can also use the behavior and test results to find out

what function(s) was not being achieved

• With the switch pressed, we measure current at the coil, so the

coil is being charged

• We measure a magnetic attraction to show that the coil is

working

• We do not hear a clapping sound, so the magnetic attraction is

either not working, or the acoustic law is not being fulfilled

– Why not? Perhaps the arm is not magnetic? Perhaps there is

something on the arm so that when it hits the bell, no sound is being

emitted

Model-based Diagnosis: Probabilistic

• While a functional representation can be useful for

diagnosis, it is somewhat problem independent

– FRs can be used for prediction (WWHI reasoning),

diagnosis, planning and redesign, etc

• Diagnosis typically is more focused, so we can

create a model of system components and their

performance and enhance the system with

probabilities

– Failure rates can be used for prior probabilities

– Evidential probabilities can be used to denote the

likelihood of seeing a particular output from a

component given that it has failed

• Bayesian probabilities can then be easily computed

Example

• The device consists of 3

multipliers and 2 adders

• F computes A*C+B*D

• G computes B*D+C*E

– Given the inputs, F should output

12 but computes 10

– Given the inputs, G should output

12 and does • We can employ probabilities of

• We use the model to compute component failure rate and

the diagnosis likelihood of seeing particular

– Possible malfunctions are with values given the input to

M1, M2, A1 but not M3 or A2 compute the most likely cause

• If we can probe the inside of the – note: it could be multiple

machine component failure

– we can obtain values for X, Y and • If we have a model of the

Z to remove some of the multiplier and adder, we can

contending malfunction

hypotheses also use that knowledge to assist

in diagnosis

Neural Network Approach

• Recall that neural networks, while trainable to perform

recognition tasks, are knowledge-poor

– Therefore, they seem unsuitable for diagnosis

• However, there are many diagnostic tasks or subtasks

that revolve around

– data interpretation

– visual understanding

• And neural networks might contribute to diagnosis by

solving these lower level tasks

• NNs have been applied to assist in

– Congestive heart failure prediction based on patient

background and habits

– Medical imaging interpretation for lung cancer and breast

cancer (MRI, chest X-ray, catscan, radioactive isotope, etc)

– Interpreting forms of acidosis based on blood work analysis

Case-Based Diagnosis

• Case based reasoning is most applicable when

– There are a sufficiently large number of cases

– There is knowledge of how to manipulate a previous case

to fit the current situation

• This is most common done with planning/design, not diagnosis

– So for diagnosis, we need a different approach

• Retrieve all cases that are deemed relevant for the current input

• Recommend those cases that match closely by combining

common diagnoses, a weighted voting scheme

• Supply a confidence based on the strength of the votes

• If deemed useful, retain the case to provide the system with a

mechanism for “learning” based on new situations

– This approach has been employed by GE for diagnosing

gas engine turbine problems

AI in Medicine

• The term (abbreviated as AIM) was first coined in

1959 although actual usage didn’t occur until the

1970s with Mycin

– Surprisingly using AI for medical diagnosis has largely

not occurred in spite of all of the research systems

developed, in part because

• the expert systems impose changes to the way that a clinician

would perform their task (for instance, the need to have certain

tests ordered at times when needed by the system, not when the

clinician would normally order such a test)

• the problem(s) solved by the expert system is not a particular

issue needing solving (either because the clinician can solve the

problem adequate, or the problem is too narrow in scope)

• the cost of developing and testing the system is prohibitive

AIM Today

• So while AI diagnosis still plays a role in AIM, it is a small

role, much smaller than those in the 1980s would have

predicted

• Today, AIM performs a variety of other tasks

– Aiding with laboratory experiments

– Enhancing medical education

– Running with other medical software (e.g., databases) to

determine if inconsistent data or knowledge has been entered

• for instance, a doctor prescribing medication that the patient is known to

be allergic too

– Generating alerts and reminders of specific patients to nurses,

doctors or the patients themselves

– Diagnostic assistance – rather than performing the diagnosis,

they help the medical expert when the particular problem is of a

rare case

– Therapy critiquing and planning, for instance by finding

omissions or inconsistencies in a treatment

– Image interpretation of X-Rays, catscans, MRI, etc

AI Systems in Use

• Puff – interpretation of pulmonary function tests has been

sold to hundreds of sites world-wide starting as early as

1977

• GermWatcher – used in hospitals to detect in-patient

acquired infections by monitoring lab data on culture data

• PEIRS – pathology expert interpretive reporting system is

similar, it generates 80-100 reports daily with an accuracy

of about 95%, providing reports on such things as thyroid

function tests, arterial blood gases, urine and plasma

catecholamines, glucose test results and more

• KARDIO – a decision tree learning system that interprets

ECG test results

• Athena – decision support system implements guidelines

for hypertension patients to instruct them on how to be

more healthy, in use since 2002 in clinics in NC and

northern CA

Continued

• PERFEX – an expert rule-based system to assist with

medical image analysis for heart disease patients

• Orthoplanner – plans orthodonture treatments using

rule-based forward and backward chaining and fuzzy

logic, in use in the UK since 1994

• PharmAde and DoseChecker – expert systems to

evaluate drug therapy prescriptions given the patient’s

background for inaccuracies, negative interactions, and

adjustments, in use in many hospitals starting in

1996/1994

• IPROB – intelligent clinical management system to keep

track of obstetrics/gynecology patient records and

cases, risk reduction, decision support through

distributed databases and rules based on hospital

guidelines, practices, etc, in use since 1995