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Acquisition of Knowledge from a Database

Acquisition of Knowledge from a Database. Gio Wiederhold, Ph.D. 1 Robert L. Blum, M.D., Ph.D. 2 Michael Walker 3 Departments of Medicine (1,3) and Computer Science (1,2) Stanford University March 1988. Presentation. 1. Review of general concepts as used by us 2. Overview of RX

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Acquisition of Knowledge from a Database

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  1. Acquisition of Knowledge from a Database Gio Wiederhold, Ph.D. 1 Robert L. Blum, M.D., Ph.D. 2 Michael Walker 3 Departments of Medicine (1,3) and Computer Science (1,2) Stanford University March 1988

  2. Presentation 1. Review of general concepts as used by us 2. Overview of RX 3. Data and knowledge processing 4. The Architecture to support RX 5. General Conclusions 6. Future Work • Objectives 1. Gain an understanding for interactions in a large knowledge-data system 2. Get a feeling for some of the detailed implementation issues 3. Learn from a working system, not fantasy • This is not an introduction to AI . . .

  3. 1. Basic Concepts Computing for DECISION-MAKING a global objective Combine Data --- the state of the world --- Knowledge --- our abstractions --- \Boxit{Computational Engine} Predictions of the Future \centerline{Paradigm} \smallskip \table{&Traditional &\VERT & Artificial Intelligence\cr Knowledge:\ & Program &\VERT & Rules, ... \cr Data: & Files &\VERT & Ground rules, \cr & &\VERT & \quad instance frames \cr Engine: & CPU &\VERT & CPU and interpreter \cr}

  4. Data, Knowledge, Information {\bf Data:} 1. Factual observations on specific objects or events 2. Measured in the past 3. Objectively verifiable \bigskip {\bf Knowledge:} 1. General descriptions or abstractions on classes of objects or events 2. Predicting the future 3. Obtained from experts 4. Uncertain and not verifiable \bigskip {\bf Information:} 1. Data or knowledge previously unknown to the receiver 2. Used for decision-making \vfill Litmus test: If an automatic process or clerk can collect the material then we are talking about {\sl data.} \bigskip If an expert has to provide the material then we are talking about {\sl knowledge}.

  5. AI System Modes Early Expert Systems 1. Data poor 2. Goal driven (user request) 3. Backward chaining 4. Often focused 5. Minimize data requests from user \bigskip Knowledge Based Systems / EDS 1. Data rich 2. Can be data-driven (triggers) 3. Forward and backward chaining 4. Easily explosive 5. Minimize repetetive data requests

  6. Data Loop Knowledge Loop Storage Education Selection Abstraction Integration Recording Summarization Experience Decision-making Action State changes Knowledge increase Data and Knowledge Information is created at the confluence of data – the state & knowledge -- the ability to select and project the state into the future

  7. 2. Overview of RX Objective of RX} Knowledge Extraction from Databases \centerline{Hypothesis} \medskip Databases contain much experience, (more than any single physician can accumulate) This knowledge can be extracted to serve (eventually) knowledge-based advice-giving systems \vfill Knowledge is used to drive the system \vfill The Knowledge representation for initial and derived is identical \vfill

  8. Components 1. Medical database: relational model, transposed extracted from clinical use \bigskip 2. Medical Knowledge base: frames 2.1 for multiple interpreters multi-objective 2.2 interlinked structures \bigskip 3. Statistical Knowledge: rules \smallskip 4. Statistical Validation: programs

  9. Processing Flow Cycle: Discovery - Study - Modeling - Verification - - Augmentation of Scientific Knowledge \threecol{\hfill Med.Experts\RIGHTARROW}{\ \Boxitwo{Medical}{Knowledge}\hfill}{} \medskip \threecol{\hfill$\swarrow$}{\hfill$\nwarrow$\ new}{\hfill rejected } \threecol{}{\hfill\UPARROW}{\hfill \UPARROW \quad} \threecol{\Boxitwo{Discovery}{Module}}{}{\hfill\Boxitwo{Study}{Module}} \threecol{ \DOWNARROW}{}{ \UPARROW\hfill} \threecol{Hypotheses}{}{\hfill model and data} \threecol{ \DOWNARROW\hfill$\searrow$}{\hfill$\nearrow$}{\hfill\hfill\UPARROW\quad} \threecol{select\hfill}{\ \Boxitwo{Model}{Building}\hfill}{ \Boxitwo{Clinical}{Data}\hfill} \threecol{Researcher}{\ \UPARROW\hfill}{\hfill \UPARROW\quad} \threecol{}{\Boxitwo{Statistics}{Knowledge}\hfill}{\hfill experience} \threecol{}{$\nearrow$\hfill\hfill$\nwarrow$ }{ Clinicians\hfill} \threecol{ Statisticians\hskip-40pt}{}{\hskip-70pt Epidemiologists \hfill} } % end tt, small

  10. Roles \Boxit{Medical Knowledgebase} initial knowledge directs inference \RIGHTARROW \RIGHTARROW accepts new knowledge \bigskip \Boxit{Medical Database} contains past experience basis for inference \RIGHTARROW \Boxit{Statistical Knowledge} processing rules \bigskip \Boxit{Interpreters} hand-coded engines interpret the knowledge

  11. Database Aramis---American Rheumatism Association uses TOD \hfill (began 1969)\quad \medskip Time-Oriented Database System Features: \ $\bullet$ Domain oriented data types 1. Date 2. Severity codes ( 0, +, ++, ... 3. User defined codes (female, male, ... . . .\medskip $\bullet$ Subsetting operations $\bullet$ Transposition Size of Stanford subset: about 30 Mbytes

  12. Knowledge Base More complex than the database Interlinked structures:  Categorical Knowledge is Hierarchically Organized  Definitional Knowledge has distinct Hierarchies  Causal Knowledge links across the hierarchies \smallskip Aggregate knowledge is a network structure  represented by frames with references to each other \vfill {\tt \line{\hfill \Boxit{ALL-UNITS} \hfill} \vfill \line{\hfill\Boxit{STATES}\hfill\Boxit{ACTIONS}\hfill\Boxit{STAT' M'DS}\hfill} \vfill \line{\hfill\Boxit{DIAGN'C-CAT'S}\hfill\Boxit{DRUGS}\hfill\Boxit{REGRESSION}\hfill} \line{\hfill\Boxit{\in... }\hfill\Boxit{\in... }\hfill\Boxit{\in... }\hfill} \vfill \line{\hfill\Boxit{CARDIAC-DIS'S}\hfill\Boxit{ANTIBIOTIC}\hfill\Boxit{MULT-REG'N}\hfill} \hfill etc \hfill etc \hfill etc \hfill \vfill } % end tt

  13. Displaying the RX Knowledge Base Menu of Display Options {\smallfont %\def\threecolp#1#2#3{\line{\hbox to 50truept{{\tt#1}\hfil}\hbox to150truept{#2\hfil}{\ninett #3}\hfil}} \table{ MA-&function(args)&EXAMPLE\cr \hfill CRO & &\cr \hrulefill&\hrulefill&\cr & \ &Display & \cr DS &\ schema(node)&DS Nephrotic-syndrome\hskip-40pt\cr DP&\ paths(c$\leftrightarrow$e)&DP SLE Cholesterol\cr DC &\ causes(e-node)&DC WBC \cr DE &\ effects(c-node)&DE Prednisone \cr DD&\ distribut'n(c\ e)&DD Prednisone Cholesterol\hskip-40pt\cr DM &\ model(c\ e)&DM Prednisone Cholesterol \hskip-40pt\cr DEV&\ evidence(c\ e)&DEV Prednisone Cholesterol\hskip-45pt\cr DF &\ frequencies &DF \cr D &\ desc'dnts-tree&D Diagnostic-Categories \hskip-40pt\cr CLASS\hskip-10pt&\hskip10pt \ classificat'n&CLASS Azathioprine\cr SPEC\hskip-10pt&\hskip10pt \ children&SPEC Diagnostic-categories\hskip-40pt\cr SIBS\hskip-10pt&\hskip10pt \ siblings&SIBS Azathioprine\cr TR &traverse right&TR Glomerulonephritis \hskip-40pt\cr TL &traverse left&TL Glomerulonephritis \hskip-40pt\cr PL &print property list \hskip-20pt& PL Validity \cr PPL&print verbose pr.list\hskip-20pt& PPL Frequency \cr } }%end small \vfill (These functions provided many of the slides below)

  14. Hierarchical Classification of Diseases \bigskip each frame has  a generalization slot and  a specialization slot: \vfill {\tt \line{\hfill respiratory diseases:\hfill} \medskip \line{\hfill genl: all categories of disease\hfill} \medskip \line{\hfill spec: pneumonia, asthma, emphysema\hfill} \vfill \table{pneumonia &asthma& emphysema\cr genl: resp'ry dis.&genl: resp'ry dis.&genl: resp'ry dis.\cr & & \cr spec: &spec:&spec: \cr \ pneumococcal pn.&\ allergic asthma&\ pco2 retention\cr \ klebsiella pn.&\ intrinsic asthma&\cr} } \vfill  Assumptions: Completeness across Inheritance

  15. Display Hierarchical Frames \centerline{Display the Descendants in the Hierarchy} \medskip {\tt D Autoimmune-Disorders \medskip Autoimmune-disorders SLE Lupus-nephritis Cardiac-lupus CNS-lupus lupus-serositis Ra Arteritis } % end tt \bigskip Hierarchical Classification \medskip {\tt CLASS Glomerulonephritis \medskip (Glomerulonephritis Renal-disorders Diagnostic-categories States) } % end tt

  16. Definitions Definitions may be in Terms of other Attributes of other Objects \bigskip It is important that medical knowledge is available at a high level of abstraction, \medskip but the definition may use other (lower) frames, in another hierarchical subtree \bigskip {\tt Pneumonia \medskip definition: Temperature $>$ 102 degrees F. and WBC $>$ 10,000 cells per mm$^3$ and Chest X-RAY = Lobar Infiltrate } % end tt \bigskip \bigskip At the lowest level the frames correspond to attributes found in the DATABASE

  17. Causal Knowledge Links Nodes i.e.: {\tt Temperature is Affected by Pneumonia } {\tt\baselineskip=11pt \smallskip \twocol{ Pneumonia }{ Temperature} \smallskip \twocol{affected-by: }{affected-by:} \twocol{\hfill Alcoholism\ }{\hfill Pneumonia\ } \twocol{\hfill Diabetes\ }{\hfill Influenza\ } \smallskip \twocol{effects:}{effects:} \twocol{\hfill Temperature\ }{\hfill Perspiration\ } \twocol{\hfill WBC\ }{} \twocol{\hfill Chest-XRAY\ }{} } % end small \medskip Each causal relationship is represented as a set of features: \smallskip {\tt intensity, frequency, direction,}{\it setting, functional form, validity, evidence } \bigskip The relationship ``{\tt Pneumonia increases temperature}": \smallskip {\tt\baselineskip=11pt intensity: to 104 degrees F. frequency: common direction: + setting:\quad studied\ in\ middle-aged\ patients with pneumococcal pneumonia functional form: .5log (severity\ pneumonia) + 98 validity: widely confirmed evidence: citations\ to\ medical\ literature } % end small

  18. SUMMARY of Round 1 Most `KNOWLEDGE' is in the relationships \smallskip Frames ({\sl and people}) define their meaning through relationships to others \vfill In a small knowledgebase linkages can be arbitrary \rightline{\hfill Semantic Nets} As the knowledge grows we imposed structure 1.\quad Categorical, 2.\quad Definitional, 3.\quad Causal. \bigskip To relate knowledge to the data this structure must be applicable to data instances \rightline{\hfill in class frames} \rightline{\hfill schema frames at DB level} \vfill

  19. 3. Data and Knowledge Processing Scientific process is a cycle: \medskip Instances\quad \RIGHTARROW \quad Experience \smallskip Education + Experience\quad \RIGHTARROW \quad Knowledge \smallskip Unexpected Instances\quad \RIGHTARROW \quad Questions \smallskip Questions + Scientific training\quad \RIGHTARROW \quad Hypothesis \smallskip Hypothesis + Knowledge\quad \RIGHTARROW \quad Model \smallskip Model + Data\quad \RIGHTARROW \quad Validation \smallskip Validation + Dissemination\quad \RIGHTARROW \quad New Knowledge

  20. How and Who?} Our Example: MEDICINE \smallskip \table{Student learns&\ \ 8 y\cr \quad cycle starts \cr Clinician&treats&\ \ 5 y\cr Clinician&observes exceptions&+ 1 y\cr Clinician&studies cases&+ 2 y\cr Clinician&formulates Hx&= 2 y\cr Archivist&collects data&= 2 y\cr Epidemiologist&formulates model&+ 3 m\cr Statistician&applies methods&+ 3 m\cr Data Analyst&selects and processes data&+ 6 m\cr All&write&+ 1 y\cr Editors&review&+ 1 y\cr Journal&publishes&+ 1 y\cr Clinicians&adapt practice&+ 3 y/cr} \bigskip Many participants \smallskip Much time in cycle: \hfil 16 y \qquad net

  21. Operational Cycle of RX Models this scientific process \medskip 1. Collect data -- done outside of RX 2. Collect and represent Medical Knowledge \quad 2.1 Define categorical frames from `textbook' knowledge densely in area of interest ($\equiv$ DB) use inheritance in categorical hierarchy outside \quad 2.2 Make Definitions to link Concepts to Database \quad 2.3 Initialize known cause/effect linkages in area of interest \smallskip 3. Collect rules for statistical processing tied to the data description 4. Program control mechanisms for 5. -- 10. \smallskip 5. Discover unusual events RX: Brute force correlation RADIX: scan for time-variations 6. Generate hypotheses 7. Build model for hypothesis testing 8. extract data for statistical Hx test 9. run test 10. append validated HX to knowledge base \smallskip 11. iterate to 5.

  22. 4. The Architecture of RX \hfill to support the cycle \ \bigskip \Boxit{DATABASE} and \smallskip \Boxit{KNOWLEDGE BASE} \bigskip \line{\Boxit{DISCOVERY module} \RIGHTARROW generate} \smallskip \centerline{hypotheses} \smallskip \hfill validate \LEFTARROW\Boxit{STUDY Module} \ \bigskip \centerline{\Boxitwo{STATISTICAL}{PROGRAMS}} \bigskip \centerline{all controlled by several} \centerline{\Boxit{KNOWLEDGE INTERPRETERS}}

  23. AI paradigm Similar to DENDRAL \medskip \subtitle{GENERATE and TEST} \centerline{Discovery Module \RIGHTARROW Study Module} \medskip All kinds of correlations \RIGHTARROW \smallskip \line{\hfill independendent, significant correlations}

  24. Clinical Database Data is a byproduct of medical practice Cases are representative Many uses of data: Health care Billing Medical Audit Research \bigskip ARAMIS \smallskip Relational model \smallskip 2 relations Patients: (pat-no, DoB, ... (50 values)) Visits: (pat-no, date-of-visit, reason ... (500 values)) \bigskip Internally transposed

  25. The ARAMIS Database \medskip Transposed: by ATTRIBUTE by PATIENT and VISIT \bigskip Data attribute column (values p1.v1 p1.v2 . . . p2.v1 . . . ) {\smallfont Patient-Id ({\tt 1 1 1 1 1 1\quad 3 3 3 3 3 3 3 3 \quad . . . 6 6 6 6 6 6 6 . . . 78 78 78 . . .} \smallskip Visit-date ({\tt 10Mar78 11Apr78 23Jun78 1Jul78 10Jul78 4Dec78 \quad 15May78 . . .} \smallskip Cholesterol ({\tt 31 29 24 30 31 29 \quad 23 - 27 25 = 23 - = \quad . . . \quad 20 22 = = = 21 . . . 32 34 . . .} \smallskip Prednisone {\tt . . .} } % end small \vfill Columns stored as a variable length compressed records controlled by a prefix table 0(!), 1(0), 2(-), 3(=) \vfill Collected using Forms

  26. The Database in RX Transposed by PATIENT by FRAME (States, Actions) by VISIT \RIGHTARROW VALUE \medskip Data attribute strings: \smallskip {\tt (Patient1 \ (Aspirin ((1 30)(2 20)(3 20)(4 20)(5 20) ...)) \ (Cholesterol ((1 215)(2 229)(4 230)(...)) \ . . . \ (Prednisone ((1 50)(2 27)(4 25)) \ . . . \ (Visit-date (1Jun80 15Jun80 12Jul80 ...) \ . . . \smallskip (Patient78 \ (Aspirin ((6 10)( ... )) \ (Cholesterol ((1 ... )) \ . . . \bigskip . . . }

  27. Schema Frames Other slots define computational parameters: Example: Schema for Hemoglobin {\tt\baselineskip=11pt \table{ Hemoglobin &{\nineit explanation}\cr ----------&\cr attribute-type: &{\nineit represented as a }\cr \hfill point-event &{\nineit \hfill time:value pair}\cr value-type: real &{\nineit i.e. a real-valued number}\cr range: 0 < value < 25 &{\nineit the legal range of values}\cr units: grams per deciliter &{\nineit units of measurement}\cr significance: .1 &{\nineit used for rounding off values}\cr & \cr}} \medskip {\rm and Real World Knowledge } \smallskip {\tt ---------- function: oxygen transport molecular-weight: 67,000 daltons structure: Fe + heme + 4 polypeptide chains part-of: red blood cell affected-by: high altitudes, genetic make-up clinical-effects: deficiency causes fatigue severe deficiency may cause cardiac failure }

  28. Discovery Module RX uses database directly --- (no knowledge used) %\pageno=61 Generate Hypothesis of relationships Search for binary correlations of events/concepts, time lagged \vfill Imprecise, often false, or useless:  HX may be known but overlooked in knowledgebase acquisition (discard Hx, update KB)  HX may be trivial (discard)  HX is worthy of study (to see if it seems valid) \bigskip Costly --- use subsets of data --- run on weekends \medskip Select --- strong $\rightarrow $ rank by correlation (R-value) --- interesting --- non--obvious \vfill Final selection of hypothesis by manual inspection

  29. Data for Discovery Initially only use a subset 50 patients \smallskip 50 attributes $\rightarrow $ 50! interactions \smallskip 6---50 visits \smallskip 12 timelags \vfill Future--- use AI - model guidance ? but avoid excessive restrictions \vfill RADIX trigger from changes in the data at a high level of abstraction (see later) \vfill --- the validation is done on the full set of data --- \bigskip Important: first a patient's course is characterized then correlations are computed over the characterizations. \smallskip

  30. Combining Correlations Across Patients bigskip The patient is the entity -- not the events, the \#(events/observations) differ greatly \bigskip Patient-based score \smallskip Patient 1: $r_1 = cor(x,y) log[pval(r_1)]$ \smallskip Patient 2: $r_2 = cor(x,y) log[pval(r_2)]$ Etc. \bigskip $$ score (x,y) = - 2 \sum\sb{i\inset all patients} log[pval(r_i)]$$ \bigskip $$ score (x,y) \approx \chi\sp{2}2p $$

  31. Output from Discovery Module \subtitle{Possible Causal Effects of Prednisone} \bigskip \table{variable&lag strength&\cr\cr Hemoglobin&(B + 518)\cr Anti-DNA-Hemagglut&(B - 514)\cr Disease-Activity&(R + 469)\cr C3&(B + 389)\cr Fatigue&(R + 370)\cr Urine-WBCS&(R + 350)\cr Albumin&(R - 346)\cr BP-Diastolic&(C + 322)\cr WBC&(C + 306)\cr Urine-RBCS&(B -293)\cr Temperature&(B - 275)\cr Weight&(C + 269)\cr LDH&(C + 268)\cr Glucose&(C + 256)\cr Log-Fana&(C - 238)\cr Lymphs&(C - 194)\cr BP-Systolic&(C + 167)\cr ...&...\cr}

  32. Study Module 1. Use knowledge to build model for statistical analysis \quad 1.1 look at confounders and \quad 1.2 their temporal relationships \smallskip 2. Use data estimates to select statistical procedures \quad 2.1 use rules \quad 2.2 use meta data cardinality type information (1. and 2. are interdependent, but iteration is not now automated) \smallskip 3. Extract required data from database \smallskip 4. Perform analysis \smallskip 5. Inspect result if significant - insert into knowledge base \vfill \centerline{\Boxitwo{Another study will now take}{the new knowledge into account}}

  33. Next: build statistical models \centerline{ We have to locate all (known) confounders} {\tt\baselineskip=11pt GLOMERULONEPHRITIS as a confounding variable for Prednisone and Cholesterol: \smallskip GLOMERULONEPHRITIS (30 pct activity) increases \ NEPHROTIC-SYNDROME (3 gms proteinuria/24 hrs) \ \ is treated by PREDNISONE (604 \% of baseline) GLOMERULONEPHRITIS (30 pct act'y) is treated by \ PREDNISONE (182 \% of baseline) GLOMERULONEPHRITIS (30 pct act'y) increases \ NEPHROTIC-SYNDROME (3 gms ...) increases \ \ CHOLESTEROL (120 mgms/dl) GLOMERULONEPHRITIS (30 pct act'y) is treated by \ PREDNISONE (182 \% of baseline) attenuates \ NEPHROTIC-SYNDROME (-1 gms ...) decreases \ CHOLESTEROL (-22 mgms/dl) GLOMERULONEPHRITIS (30 pct act'y) is treated by \ PREDNISONE (182 \% of baseline) increases \ \ CHOLESTEROL (11 mgms/dl) $new$ GLOMERULONEPHRITIS (30 pct act'y) is treated by \ PREDNISONE (182 \% of baseline) attenuates \ \ SLE (-6 pct activity) attenuates \ \ \ NEPHROTIC-SYNDROME (0 gms ... ) decreases \ \ \ \ CHOLESTEROL (-5 mgms/dl) } % end small \vfill

  34. The Category Hierarchy is Complete Necessary for the CLOSED-WORLD assumption made by its interpreter \smallskip {\sl Example: } The Specialization at a top level \medskip {\tt SPEC Diagnostic-categories } \smallskip {\tt\baselineskip=10pt (Arthritic-disorders~Autoimmune-disorders Cardiac-dis'rs~Dermatologic-dis'rs Electrolytic-dis'rs~Endocrine-dis'rs Gi-dis'rs~Gynecologic-dis'rs~Hematologic- dis'rs~Hepatic-dis'rs~Hypertensive-dis'rs Immunologic-dis'rs~Infectious-dis'rs Metabolic-dis'rs~Neurologic-dis'rs Non-specific-dis'rs~Nutritional-dis'rs Oncologic-dis'rs~Ophthalmologic-dis'rs Psychiatric-dis'rs~Pulmonary-dis'rs Renal-dis'rs~Urologic-dis'rs~Vascular-dis'rs) } % end small \vfill \centerline{SIBLINGS} \medskip {\tt SIBS AZATHIOPRINE } \medskip {\tt (CHLORAMBUCIL~CYCLOPHOSPHAMIDE) } % end small \vfill At low levels made feasible through inheritance

  35. Complete Property List PL NEPHROTIC-SYNDROME \smallskip {\tt\baselineskip=11pt GENL: RENAL-DISORDERS SPEC: (PROTEINURIA HEAVY-PROTEINURIA) DEFINITION: (OR (DURING \& --) (AND \& --)) TYPE: INTERVAL EFFECTS: (URINE-PROTEIN-RANGE ALBUMIN 24-HR-URINE-PROTEIN --) MINIMUM-DURATION: 30 MINIMUM-POINTS: 2 INTERVALFN: MEAN-DURING-INTERVAL VALUE-TYPE: BINARY INTRA-EPISODE-GAP: 100 INTER-EPISODE-GAP: 180 RECORDS: INVERTED AFFECTED-BY: ((PREDNISONE \&) (GLOMERULO- NEPHRITIS \&) (SLE \&)) PARTITION: (0 .5 1 --) UNITS: "gms proteinuria/24 hrs" PROXIES: (ALBUMIN 24-HR-URINE-PROTEIN URINE-PROTEIN-RANGE) ONSET-DELAY: 7 MINIMUM-INTERVAL: 30 CARRY-OVER: 30 } % end tt

  36. Definitions over Time Our observations are actually over time This has important effects: \smallskip 1. The DEFINITIONS combine EVENT observations as recorded in the database into INTERVAL information \smallskip 1.1 INTERVALS have parameters as {\tt MAX, MIN, AVE, RATE, . . .} \medskip 1.2 Patients differ in the number of EVENTS observed for a disease course but a Course should be one interval a treatment should be one interval {(\it same for other time-based data -- \quad most data in planning extrapolate from past series to future)}

  37. Definitions and Missing Data 2. We elevate detailed observations to higher level concepts and do\ \lower6pt\Boxit{Statistics on Concepts, not on Facts} ~? \smallskip 2.1 Our experts, and the knowledgebase deals better with higher level concepts \smallskip 2.2 We can combine multiple event-types to substantiate an interval concept (more credibility in the face of missing data) {\tt NEPHROTIC SYNDROME during HEAVY-PROTENURIA or PROTENURIA and ... } 2.3 We can acount for masked symptoms {\tt ... during SYMPTOM or DRUG {\sl given for that symptom}.}

  38. Why ignore the Facts?} Whats wrong with data: \smallskip 1. variable number of observations 2. taken at unpredictable intervals 3. often incomplete \bigskip Use higher level concepts defined by frames to aggregate incomplete facts into meaningful concepts: {\sl Labeling} \bigskip 1. Intial finding + continuing treatment = continuing disease state \in(treatment can mask findings, \in~ comtinued test for findings are costly) \medskip 2. Findings of events over time \RIGHTARROW interval = worsening/steady/improving disease state (matters more than level of state)

  39. Use of This Information \subtitle{MODEL BUILDING} There can be many Paths between two nodes in our network even at the higher, CONCEPT level \smallskip New knowledge \RIGHTARROW New direct causal path with parameters \smallskip But, any alternative path can also explain a hypotheses \smallskip If $\sum$ of alternate paths explains all of the relationship no new knowledge! \hfill\Boxit{Hypothesis is invalidated}\quad. So: 1. look for all paths -- intermediate nodes are covariates 2. prune subsumed paths 3. omit infrequent covariates to simplify model (omitting frequent covariates -- too much loss of data)

  40. Cycles Note there can be cycles, but the time delay imposes an ordering: \bigskip Example {\ninett\baselineskip10pt \def\M{$-$} \table{ &\hskip-10pt intensity \M delay& \cr Sedentary Life&\M +2 \M months \RIGHTARROW & Diet \cr Diet &\M +2 \M months \RIGHTARROW & Cholesterol \cr Cholesterol&\M +2 \M years \RIGHTARROW & Coronary Art.sc. \cr Coronary Art.sc.&\M +4 \M months \RIGHTARROW & Heart Attack \cr Heart Attack&\M -2 \M days \RIGHTARROW & A-type behavior \cr Heart Attack&\M -1 \M hours \RIGHTARROW & Smoking \cr Heart Attack&\M +3 \M minutes \RIGHTARROW & Sedentary Life \cr Heart Attack&\M +5 \M minutes \RIGHTARROW & Death \cr Heart Attack&\M +2 \M days \RIGHTARROW & Death \cr Coronary Spasms&\M +3 \M months \RIGHTARROW & Heart Attack \cr Smoking&\M +1 \M months \RIGHTARROW & Coronary Spasms \cr Hypertension&\M +4 \M years \RIGHTARROW & Coronary Art.sc. \cr Hypertension&\M +3 \M months \RIGHTARROW & Coronary Spasms \cr A-type behavior&\M +1 \M years \RIGHTARROW & Hypertension \cr A-type behavior&\M +1 \M varied \RIGHTARROW & Coronary Spasms \cr Age&\M +1 \M years \RIGHTARROW & Cholesterol \cr Age&\M +2 \M years \RIGHTARROW & Hypertension \cr} }\bigskip There are positive and negative paths and loops \smallskip Cannot be captured by a simple logical model

  41. Modal Effects of Prednisone Frequency and strength of causal relationship \medskip {\tt DE PREDNISONE MODE \in{\sl /* one link away */} \medskip PREDNISONE, at a level of 30 mgms/day, \medskip usually increases CHOLESTEROL by 50 to 130 mgms/dl, regularly attenuates NEPHROTIC-SYNDROME by 1.0 to 2.0 gms prot/24 hrs, regularly attenuates GLOMERULONEPHRITIS by 10.0 to 30.0 percent, commonly attenuates SLE by 10.0 to 30.0 percent activity, regularly decreases ANTI-DNA-HEMAGGLUT by 50 to 90 percent, regularly increases IMMUNOSUPPRESSION by 16 to 32 percent activity, regularly decreases EOS by 2 to 3 \% of WBC, occasionally increases KETOACIDOSIS by 20 to 100 mgms/dl of glucose, } \vfill {\eightrm {\tt*} Note that all the terms are represented by numerically encoded values}

  42. Display all Paths above threshold to collect significant covariates: \medskip {\tt DP SLE CHOLESTEROL } (default $>0.1$) \medskip {\tt SLE $\{$30 percent activity$\}$ increases NEPHROTIC-SYNDROME $\{$1 gms proteinuria/24 hrs $\}$ increases CHOLESTEROL $\{$24 mgms/dl$\}$ \medskip SLE $\{$30 percent activity$\}$ is treated by PREDNISONE $\{$182 \% of baseline$\}$ increases CHOLESTEROL $\{$14 mgms/dl$\}$ \medskip SLE $\{$30 percent activity$\}$ increases NEPHROTIC-SYNDROME $\{$1 gms proteinuria/24 hrs $\}$ is treated by PREDNISONE $\{$143 \% of baseline$\}$ increases CHOLESTEROL $\{$8 mgms/dl$\}$ \medskip SLE $\{$30 percent activity$\}$ increases IMMUNOSUPPRESSION $\{$18 percent activity$\}$ increases HEPATITIS $\{$5 Iu/ml of SGOT$\}$ increases CHOLESTEROL $\{$6 mgms/dl$\}$ } % end small

  43. Displaying the Causes of Cholesterol i.e., the Set of Nodes that Affect it \medskip {\tt DC CHOLESTEROL} {\sl /* other direction */} \medskip {\tt CHOLESTEROL \medskip always is increased by PREDNISONE regularly is increased by HEPATITIS regularly is increased by KETOACIDOSIS usually is increased by NEPHROTIC-SYNDROME } % end small \vfill \centerline{Interpretation of the Frequencies} \medskip Is not linear over the range of terms: \medskip {\smallfont \def\threecolp#1#2#3{\line{ \hbox to 48truept{#1\hfil}\hbox to160 truept{#2\hfil}#3\hfil}\vskip-3truept} {\tt DF }\vskip-12pt \threecolp{Cell}{Adverb }{\hskip-10pt Probability} \medskip \threecolp{1 }{never* }{ .001} \threecolp{2 }{very-rarely }{ .005} \threecolp{3 }{rarely }{ .01} \threecolp{4 }{infrequently }{ .04} \threecolp{5 }{occasionally }{ .16} \threecolp{6 }{commonly }{ .32} \threecolp{7 }{regularly }{ .64} \threecolp{8 }{usually }{ .95} \threecolp{9 }{almost-always }{ .99} \threecolp{10 }{always }{ 1.00} \vfill * well hardly ever } % end small

  44. Causal Inference \medskip Generating New Knowledge in RX \smallskip Means \smallskip Establishing and Quantifying New Causal Linkages \medskip Correlations discovered do not establish 1. causality 2. directness \medskip ad 1. causality: A causes B ? heuristic if B consistently follows in time A, then B does not cause A (there may be an unknown covariate C, causing both with different delays) \medskip ad 2. directness: the correlation may be due to known covariates -- check the model as shown previously

  45. Use of the Covariate Model Rule driven, but uses medical knowledge in frames uses metadata in frames is controlled by a frame hierarchy deterministic execution \bigskip 1. Select proper statistical method: Use info about data from Schema Frames \smallskip 2. Check if enough data is available: Ask DBMS portion for cardinality of subsets needed which distinguish the remaining covariates \vfill

  46. Running the Study Module Statistical knowledge is encoded as RULES. \bigskip The statistical knowledge in RX is not deep: (no derivation from Probability theory) \medskip {\tt\baselineskip=11pt \line{Selecting instance of the class: STUDY-DESIGNS\hskip-50pt\hfil} \medskip \line{The candidate selected is: LONGITUDANAL-DESIGN\hskip-30pt\hfil} \medskip Would you like to see rules that determined selection of study design? **YES \smallskip LONGITUDINAL-DESIGN \smallskip PREREQUISITES: Can the EFFECT occur more than once in a patient's record? \smallskip \line{Do we have patient records in which values for\hskip-20pt\hfil} the EFFECT have occurred more than once \smallskip CROSS-SECTIONAL-DESIGN \smallskip \line{PREREQUISITES: If the dependent variable is \hskip-20pt\hfil} not a function of time, then use the CROSS-SECTIONAL-DESIGN \line{CROSS-SECTIONAL-DESIGN will also be used when\hskip-20pt\hfil} \line{ most patient records have only a few values\hskip-30pt\hfil} } % end tt

  47. Now select statistical procedure The rule categorization is also hierarchical \smallskip {\tt\baselineskip=11pt Selecting instance of class: STATISTICAL-METHODS \ Considering instance: CONTINGENCY-TABLES \ Considering instance: T-TEST \ Considering instance: ANOVA \ Considering instance: REGRESSION \ Selecting instance of class: REGRESSION \ \ Considering instance: MULTIPLE-REGRESSION \ \ Considering instance: SPEARMAN-RHO \ \ Considering instance: KENDALL-TAU \ \ Considering instance: PEARSON-R \ Candidates whose prerequisites are satisfied: \ \ (MULTIPLE-REGRESSION SPEARMAN- \ \ \ RHO KENDALL-TAU PEARSON-R) \centerline{\it Conflict resolution rules are used to decide among these} The candidate selected is: MULTIPLE-REGRESSION \smallskip \ Considering the instance: DISCRIMINANT-ANALYSIS \ Considering the instance: FACTOR-ANALYSIS \ Considering the instance: LIFE-TABLES \medskip Candidates whose prerequisites are satisfied: (MULTIPLE-REGRESSION) \medskip The candidate selected is: MULTIPLE-REGRESSION } % end small

  48. Explanation \medskip {\tt\baselineskip=11pt The candidate selected is: MULTIPLE-REGRESSION \medskip Wou ld you like to see decision criteria for selecting statistical methods? **YES \medskip MULTIPLE-REGRESSION \medskip RULES: \quad If the independent variables are causally ordered, then do a hierarchical regression. \smallskip otherwise, do a standard regression. \medskip PREREQUISITES: \ Multiple regression is appropriate when the number of independent variables is greater than 1 \smallskip All variables must be at least of \in measurement level = binary. \smallskip All variables must be normally distributed. \medskip Statistical method: MULTIPLE-REGRESSION } % end small

  49. More explanation {\tt\baselineskip=13pt \smallskip The \# of values recorded for the dependent var. for each patient must be $>$ 1 + the \# of independent variables \smallskip Next, there is the same minimum required \in \# of values for the independent variable of primary interest \smallskip To estimate the effect of the independent variable for a single patient, the coefficient of variation must be $>$ threshold = 10 percent \smallskip Finally, to do individual estimation, the total number of events must be $> 1 + $ $\#$ of indep. vars: the costliest criterion computationally } % end small

  50. The Rules are in LISP: \medskip {\tt Would you like to see the machine readable eligibility criteria? \medskip **YES \medskip Eligibility criteria: \smallskip [AND (IGEQ (\#VALUES (QUOTE CHOLESTEROL) PAT) (ADD1 (FLENGTH VARS))) (IGEQ (\#VALUES (QUOTE PREDNISONE) PAT) (ADD1 (FLENGTH VARS))) (GREATERP (COEF-VAR (QUOTE PREDNISONE) PAT) .1) (IGEQ (FLENGTH (ENTRIES (QUOTE PRED-CHOL) NIL PAT)) (ADD1 (FLENGTH VARS] } % end small

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