Diagnosis and Interpretation

1
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

2
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

3
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)

4
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 systems 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

5
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

6
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

7
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.pd
  f

8
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

9
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
  rules 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 systems answers
• But it is also a great tool to help debug and
  develop the knowledge base

10
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
11
Systems Generated From Emycin

• SACON Structural Analysis CONsultant
• Puff pulmonary disorders
• originally implemented in Emycin before being
  re-implemented as an OO system

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 gt .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
I f 1) The mmf/mmf-predicted ratio is 35..45
the fvc/fvc-predicted ratio gt 88 2) The
mmf/mmf-predicted ratio is 25..35 the
fvc/fvc-predicted ratio lt 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.
12
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,

13
Analyzing Mycins 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

14
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 nodes 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

15
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

16
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 dont care
• We return the result from the first pattern to
  match, so this is in essence a nested if-else
  statement

17
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)

18
Example 1 Automotive Diagnosis
19
Example 2 Syntactic Debugging
20
Ex 3 Linux User Classification
21
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

22
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
  (1ltjltn), then we can infer that Ai is true
• for this to work, we need a way to determine
  which is most likely

23
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 Hs in our domain?
• What complicates generating a best explanation is
  that H and D are probably not singletons but sets

24
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?

25
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)

26
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

27
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

28
Ex Evolution (DH) vs Creationism (CH)
29
(No Transcript)
30
Probabilistic Approach(es)

• Pearls 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

31
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

32
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

33
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

34
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 H2s 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

35
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 wouldnt
  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

36
Example Handwritten Character Recognition
(CHREC)
37
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
38
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

39
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 1s, B for the 8, and O
  for the 0 can all be ruled out (or at least
  scored less highly)

40
Full Example in a Natural Language Domain
41
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

42
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

43
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

44
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

45
Example

• The device consists of 3 multipliers and 2 adders
• F computes ACBD
• G computes BDCE
• Given the inputs, F should output 12 but computes
  10
• Given the inputs, G should output 12 and does
• We use the model to compute the diagnosis
• Possible malfunctions are with M1, M2, A1 but not
  M3 or A2
• If we can probe the inside of the machine
• we can obtain values for X, Y and Z to remove
  some of the contending malfunction hypotheses

• We can employ probabilities of component failure
  rate and likelihood of seeing particular values
  given the input to compute the most likely cause
• note it could be multiple component failure
• If we have a model of the multiplier and adder,
  we can also use that knowledge to assist in
  diagnosis

46
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

47
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

48
AI in Medicine

• The term (abbreviated as AIM) was first coined in
  1959 although actual usage didnt 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

49
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

50
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

51
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
  patients 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