AI IN MEDICINE Tejashree Aher (06011011) Akhil Deshmukh (06D05007) Pic: Google Tejashree Aher (06011011) Akhil Deshmukh (06D05007) Anshul Maheshwari (06D05009) Narendra Kumar (06D05008)

Overview Motivation AIM Clinical expert system : MYCIN What is AIM? Goals of AIM Applications of AIM Clinical expert system : MYCIN Introduction How it works Specification of the therapy selection problem Representation of Goals Certainty factor Partial derivation of the algorithm

Motivation ‘Doctors in a box’ to diagnose diseases. Community of computer scientists and healthcare professionals set a research program - Artificial Intelligence in Medicine (AIM) with the aim of revolutionize medicine.

What is AIM? Clancey and Shortliffe :’Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations’ AI specialized to medical applications Employ human- like reasoning methods in the programs

AIM use ‘machine learning’ to use and create knowledge. Machine learning - computers that can learn from experience Use – stored data used in diagnosis Creation - analyse the relationship within the data to come up with new results Used in Drug discovery

Goals of AIM? Expert computer programs for clinical use Dissemination of the best medical expertise to geographical regions where that expertise is lacking Making consultation help available to non- specialists not within easy reach of expert human consultants. To formalize medical expertise

Applications of AIM Knowledge based systems Diagnostic and educational systems

Knowledge based systems Use the medical knowledge stored for reasoning Store information about a specific task Knowledge represented in the form of set of rules Support healthcare workers in the normal course of their duties -manipulation of data and knowledge Examples: Generating alerts and reminders -warn changes in a patient's condition (in less critical cases, through a email) Agents for information retrieval- software agents are sent to search for and retrieve information

Diagnostic and educational systems Most research systems were developed to assist clinicians in the process of diagnosis. Expert System A program that contains a large amount of knowledge in one specific area. Rules for organizing and expressing its knowledge Approaches to integrate the recommendations

MYCIN Created in the mid-1970s,helps doctors choose the correct antibiotics for patients with severe infections (and the best ones !!!!) It is given large amounts of information on meningitis and bacteremia This information represented as -“if A and B are true(evidently), then there is evidence that C is true”. Dynamic computation Same recommendation with different certainty factors, MYCIN integrates them by means of a numerical function.

How MYCIN Works ??? Diagnose for infectious diseases. Identify infection that requires therapy, What is the identity of the organism(s) by clinical and laboratory evidence. primary, secondary. What are the potentially useful drugs chloramphenicol (0.95) clindamycin (0.95) erthromycin (0.77) tetracycline (0.41) carbenicillin (0.25) 5. Which will be best ? (yes, it suggests the best one!)

Fig by: M. Chandra and V. K. Sonkar

Example What exactly is wrong with Joe?? MYCIN has the answer. Joe shows the following disorders Headache Bodyache Nausea What exactly is wrong with Joe?? MYCIN has the answer. Pic: Google

Organization of MYCIN MYCIN PATIENT RULE BASE PATIENT DB MEDICAL Consultation program Explanation program Knowledge acquisition program Static knowledge base Dynamic patient data Physician user Infectious disease expert PATIENT RULE BASE PATIENT DB MEDICAL EXPERT Fig by: M. Chandra and V. K. Sonkar

The Knowledge Base Inferential knowledge: stored in decision rules If Premise then Action (Certainty Factor [CF]) If A&B then C (0.6) The CF represents the inferential certainty Static knowledge: Natural language dictionary Lists (e.g., Sterile Sites) Tables (e.g., primary, gram stain, morphology, aerobicity) Dynamic knowledge stored in the context tree: Patient specific Hierarchical structures: Patient, cultures, organisms

Fig by: Yuval Shahar

Specification of the therapy selection problem Given a diagnosis (one or more organisms suspected of infecting the patient), choose the therapy (set of drugs) that best satisfies the following medical goals:   Maximize drug sensitivity. Maximize drug efficacy. Continue prior therapy. Minimize number of drugs. Give priority to covering likelier organisms. Maximize number of suspected organisms covered. Don’t give two drugs from the same general class. Avoid contraindications for the patient.

It subject all the therapies to the following three tests - How to choose the best therapy??? It subject all the therapies to the following three tests - Coverage test. Classes of selected drugs in a therapy. Contra-Indication. A therapy is suggested or rejected , Explanation !!!

Representation of goals: MYCIN Algorithm Representation of goals: Set of axioms Partial ordering Preference order Linear ordering Metric representation Partition Yes/no predicate Certainty Factor.

Contd. Certainty Factor: What is Certainty factor? How does it combine? Proceeds as: Several rules single hypothesis. Several propositions together. Following the chaining rule. B C A Ù

Cont. Measure of belief: MB[h, e]. Measure of disbelief: MD[h, e]. Certainty factor: CF[h, e] = MB[h, e] – MD[h, e].

Cont. Combination of evidences: Combination of hypothesis: MB[h, s1 ^ s2] = 0 if MD[h, s1 ^ s2] = 1 MB[h, s1] + MB[h, s2]*(1- MB[h, s1]) else MD[h, s1 ^ s2] = 0 if MB[h, s1 ^ s2] = 1 MD[h, s1] + MD[h, s2]*(1- MD[h, s1]) else Combination of hypothesis: MB[h1  h2,e] = min(MB[h1,e] ,MB[h2,e] ) MB[h1  h2,e] = max(MB[h1,e] ,MB[h2,e] )

Certainty Factors Certainty Factor (CF) with its conclude functions, Say the current CF value is x, and a new evidence with CF y is supporting the same hypotheses comes, then F(x,y) = x+y(1--x) if x, y ≥0, = x+y(1 +x) if x, y<0, |x|, |y|≤ 1. = (x + y)/(1 - min(|x|,|y|)) else. Conclude derives a conclusion including the CF of the result E.g., “There is suggestive evidence (0.7) that the identity of the organism is streptococcus”. It is always true that -1 ≤ CF ≤ +1 If CF = +1 then all other hypotheses are rejected

Example Joe has a disease A bodyache ^ headache->yes (0.7) ...e1 headche^ weakness -> yes (0.8) ...e2 no weakness -> no (0.6) ….e3 weakness ^ nausea -> yes (0.6) ....e4 Joe comes to doctor- headache? yes bodyache ? yes weakness ? no nausea ? yes

Pic: Google CF(headache (Joe, yes)) = 0.7 CF(weakness (Joe, yes)) = 0.65 CF(nausea (Joe, yes)) = 0.4 CF(bodyache (Joe, yes)) = 0.8 MD(joe, e3)= CF(e3)* max(0, CF(weakness)) = 0.6 * (1-0.65) =0.210 get MB(joe, e1) = CF(e1)* max (0, min(CF(bodyache), CF(headache))) = 0.7 * 0.7 =0.49 MB(joe, e2) =CF(e2)* max (0, min(CF(weaknes8), CF(headache))) = 0.8 * 0.65 =0.52 MB(joe, e4) =CF(e3)* max (0, min(CF(weaknes8), CF(nausea))) = 0.6 * 0.4 =0.24 MB(joe, e3) =CF(e4)* max (0, min(CF(no weaknes))) = 0.4 * 0.6 = 0.24 MB(joe, {e1,e2})= 0.49+ 0.52 *(1-0.49) = 0.7552 MB(joe,{e1,e2,e4})= 0.7552 + 0.24 *(1- 0.0.7552) = 0.813 MD(joe,e3)= 0.6 * 0.24 = 0.144 CF (joe, fever) = MB(joe, fever) - MD(fever) = 0.813-0.144 = 0.669 ………. Chances of Joe having fever !! Pic: Google

Partial derivation of the algorithm Representing Goals: Linear ordering: <fewer Matric scale: 100-1000 Considering the above example: Drug (A) <fewer Drug (B)

Preference ordering and Partition Preference ordering: CONDENSE, a many to one function F(x). F(x)<F(y) => x<y PARTITION: M(x) -> F(x) F(x)= λ(x) { i | ti-1 <p M(x) <p ti} t0 ≤p M(x) ≤p tn+1 ‘->’: re-formulation of constraint. Drawback of CONDENSE F(x) < F(y): significant difference

EXTENDsion and CONJOINing EXTEND: An ordering on individual items to an ordering on bags of items, follows {x} < {y} iff x < y. If X < Y and X’ < Y’, then X+X’ < Y+Y’, where + denotes bag union. For example 1<2 implies {1} < {2} and {l, l} < {1,2}. CONJOIN: We combine the preference <fewer for fewer drugs with the preference <effective for more effective therapy by Conjoining them. x ≤ p&q Y iff x ≤P y and x ≤q y //x is atleast as good as y x <p&q iff (x ≤p y and x <q y) OR (x <p y and x ≤q y) //x is preferable (Note that A <effective B means therapy A is more is more effective than therapy B, ie. More preferable with respect to the effectiveness.)

Combine coverage preferences The therapy goals listed in above include maximizing the number of organisms covered and giving priority to those the patient is likelier to have. Let’s see how these two goals are integrated:   Classify organisms as “most likely” or “less likely.” 2. Relax the coverage goal by ignoring “less likely” organisms. 3. Reformulate the coverage goal as the constraint that all the “most likely” organisms be covered.

Domination of Preferences 1 Domination of Preferences 1. Letting one preference -- <primary , <secondary – using <secondary only to resolve ties . X < primary Y Or (X =primary Y and X <secondary Y).   2. A preference can simply be IGNORED. For example, ignoring <secondary <primary;secondary to < primary ' This particular case of IGNORE is appropriate if ties with respect to <primary are too rare to worry about, or if violating <secondary in the event of such a tie wouldn’t do much harm.   It is unlikely for two therapies to be equally effective on the likeliest organisms but different on the less likely ones, so it is reasonable to ignore the less likely organisms altogether. 3. The Condensed preference compares therapies based on the number of “most likely” organisms covered. This preference is now reformulated into a constraint by THRESHOLDING.   THRESHOLD (tmin): M(x) µ(X) , (M(x) ≥ tmin),

Maximizing therapy effectiveness appears more important than minimizing the number of drugs, in the sense that increasing therapy effectiveness by 1 rank is considered more desirable than reducing the number of drugs by 1.

-Why MYCIN- Addresses the problems of reasoning. Provide clear and logical explanation of reasoning. Explore how human experts make these rough (but important) guesses. Useful for junior or non-specialized doctors.

-MYCIN- Does it always thinks like an Expert?? But not always good to use drugs with high effectiveness . So it is always preferred by professional doctors to start with low concentration ( low mg) drugs, than increase it step by step if effects are not significant. At the time of the first study, MYCIN rules included only bacteremia (meningitis and endocarditis were added later), thus never tested in a real clinical environment with general infections

Summary Reduction in Medication Errors and Adverse Drug Events. Computer­assisted - fewer errors than handwritten prescriptions and to be five times less likely to require pharmacist clarification Prompt to use a cheaper generic drug when a more expensive drug was initially ordered; Cannot model common-sense Cannot be completely relied upon ( loss of confidence !! ) The knowledge-acquisition bottleneck remained significant (additional effort from already busy individuals !!!)

Contd. Rely on human knowledge The program acts as advisor to a person Medical practitioners serve as a critical layer of interpretation between an actual patient and the expert systems Limited ability of the program to make a few common sense inferences is enough to make them usable and valuable

References- Peter Szolovits , Artificial Intelligence and Medicine, Westview Press,1982. Towards Explicit Integration of Knowledge in Expert Systems: An Analysis of MYCIN’s Therapy Selection Algorithm, Bill Swartout, Jack Mostow, AAAI-86 ,1986. http://www.openclinical.org/gmm_ardensyntax.html. Peter Szolovits, William J. Long, The Development of Clinical Expertise in the Computer, Westview Press,1982. Athanasios K. Tsadiras*, Konstantinos G. Margaritis, “The MYCIN certainty factor handling function as uninorm operator and its use as a threshold function in artificial neurons”, Fuzzy Sets and Systems 93,1998. Yuval Shahar, Diagnostic Systems (I),Medical Decision support systems, Stanford Univarcity,2007.