“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA DEPARTMENT OF MEDICAL INFORMATICS AND BIOPHYSICS Medical Informatics Division / 2008

MEDICAL DECISION SUPPORT (I) COURSE 11

1. MEDICAL DECISION 1.1. DIRECTIONS:1.1. DIRECTIONS: –COMPUTER ASSISTED DIAGNOSIS –INVESTIGATION SELECTION –THERAPY OPTIMISATION –HEALTHCARE MANAGEMENT

a) COMPUTER ASSISTED DIAGNOSIS History

c) INVESTIGATION SELECTION Investigations might be: §Invasive §Expensive

c) Therapy optimization a)Tumor treatment by radiations b)Drug treatment

d) HEALTHCARE MANAGEMENT a)Planning and use of resources b)Optimization

1.2. ELEMENTARY CYCLE OF MEDICAL ACTIVITY

1.2. METHOD CLASSIFICATION1.2. METHOD CLASSIFICATION –a) LOGICAL TRUTH (SYMPTOM) TABLESTRUTH (SYMPTOM) TABLES DECISION TREESDECISION TREES –b) STATISTICAL BAYES’ RULEBAYES’ RULE PATTERN RECOGNITIONPATTERN RECOGNITION –c) HEURISTICAL EXPERT SYSTEMSEXPERT SYSTEMS

2. LOGICAL METHODS 2.1. CONSTRUCTIVE PRINCIPLES2.1. CONSTRUCTIVE PRINCIPLES a)Based on bivalent logics: Knowledge represented symbolically by Yes/No (1/0) Yes/No (1/0) b) Knowledge Base = Symptom table c) Patient ’STATE VECTOR’ (PAT) d) Sequential comparison e) Diagnoses list (sorted)

D=disease, S=symptom PAT=patient state vector S 1 S 2 S Score D /8 D / PAT 0 1 0

2.2. Types of logical methods According to PAC vector construction: A) Symptom tables (truth tables)A) Symptom tables (truth tables) –Symptom selection from a menu B) Decision treesB) Decision trees –Set of questions with Y/N answers –Avoiding useless questions –Patient involvement

2.3. DISADVANTAGES OF LOGICAL METHODS CANNOT QUANTIFY SYMPTOM INTENSITY (ex: high/moderate fever)CANNOT QUANTIFY SYMPTOM INTENSITY (ex: high/moderate fever) CONSIDER ALL SYMPTOMS AS EQUALLY WEIGHTED FOR DIAGNOSISCONSIDER ALL SYMPTOMS AS EQUALLY WEIGHTED FOR DIAGNOSIS SOME SYMPTOMS MIGHT NOT BE PRESENTSOME SYMPTOMS MIGHT NOT BE PRESENT DISREGARD DISEASE PREVALENCEDISREGARD DISEASE PREVALENCE

3. STATISTICAL METHODS 3.1. BAYES’ RULE assumes as known :3.1. BAYES’ RULE assumes as known : –p(D+) : Probability of disease D within a population (disease probability ~ prevalence) –p(S+/D+) : Probability of symptom S to occur IF the disease D is present –[ p(S+) : probability to encounter symptom S, which may be computed or estimated from p(S+) and p(S+/D+) ]

b) For each pair D/S (Disease / Symptom) we build a 2x2 table S+S - D+ n 11 n 12 R 1 D - n 21 n 22 R 2 C 1 C 2 N

c) PROBABILITIES: - unconditional: p ( D+) = R 1 / N [probability of disease D to be present] - conditional: p ( S+ / D- ) = n 21 / R 2 [probability of symptom S to be present if disease D is absent] d) BAYES RULE P(B/S) = P(S/B) x P(B) P(S)

e) Importance: - possibility to compute p(D+ / S+) without the table f) For several symptoms: - it can be applied only for INDEPENDENT symptoms -independence test (chi-square) g) Application P(S+/D+) = n 11 /R 1 P(D+) = R 1 /N P(S+) = C 1 /N => P(D+/S+) = n 11 /C 1 => P(D+/S+) = n 11 /C 1

Example In a study 3000 medical records are analyzed. 500 patients had virosis and 400 of them presented fever. But fever has been present also in other 600 patients. Calculate: A) probability that a patient had virosis B) … … did not have fever C) … … had fever if did not have virosis D) … … had virosis if had fever

3.2. PATTERN RECOGNITION Establishing a diagnosis as a RECOGNITION processEstablishing a diagnosis as a RECOGNITION process Notion of PATTERNNotion of PATTERN –NAME OF A CLASS DISTINGUISHED BY A SET OF CHARACTERISTIC FEATURES Discriminant power of various featuresDiscriminant power of various features Process of RECOGNITIONProcess of RECOGNITION

c) CLASSIFICATION METHOD a set of classified objects is given (with their [numerical] characteristic features)a set of classified objects is given (with their [numerical] characteristic features) a representation in a multidimensional space is made and each class is delimiteda representation in a multidimensional space is made and each class is delimited Question: to which class does a new object belongQuestion: to which class does a new object belong the new object is classified according to its positionthe new object is classified according to its position two working phases:two working phases: –learning (supervised) –classification advantage: similarity with real situationsadvantage: similarity with real situations

d) CLUSTERING METHOD a (large) set of unclassified objects is givena (large) set of unclassified objects is given an n-dimensional graphical representation is performedan n-dimensional graphical representation is performed Question: can these objects be divided into different classes ?Question: can these objects be divided into different classes ? two phases:two phases: –learning (unsupervised) –classification (cluster defining)

e) BUILDING A PATTERN FEATURE SELECTIONFEATURE SELECTION –class delimitation (disjunct classes) –projection function –methods: - vectorial: principal components analysisprincipal components analysis discriminant analysisdiscriminant analysis - structural: - structural: feature hierarchyfeature hierarchy ‘CLASSIFIER’ SYNTHESIS (decision function)‘CLASSIFIER’ SYNTHESIS (decision function) –geometrical / statistical / syntactic rules

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