Applying formal methods to clinical guidelines: the case of temporal information

Paolo TerenzianiDipartimento di Informatica,

Univ. del Piemonte Orientale “Amedeo Avogadro”, Via Bellini 25\g, 15100 Alessandria, Italy

paolo.terenziani@mfn.unipmn.it

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­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

Aberdeen,­June­4th,­2008

My­favourite­research­topic:­Dealing­with­time-related­phenomena

­­­­­­­­­­­­­­­­Knowl.­Repr.

­­­­­­­­­­­­­­­­­Constraints­­­­­­­­­­­­­­­­­Periodicity

TDB­Semantics­­­­­­­­­­­­Constraints­DB­­­­­­­­­

Periodicity­DB­­­­­­­­­­­

­­­­­­­­­­­­­­­­­Medical­Info.­­­­­­­­­­­­­­­­­­­­­­­­­­­­­

TIME

Overview of the presentation

•­ experience­in­the­GL­context:­the­GLARE­project­(sketch)

• temporal­information­in­clinical­records­­temporal­databases­

•­ temporal­information­in­the­guidelines­­reasoning­about­temporal­constraints

•­ conclusions

GLARE(GuideLine­Acquisition­Representation­and­

Execution)

- Joint­project­(started­in­1997):Dept.­Comp.­Sci.,­Univ.­Alessandria­(It):­P.­Terenziani,­S.Montani,­A.BottrighiDept.­Comp.­Sci.,­Univ.­Torino­(It):­L.Anselma,G.CorrendoAz.­Osp.­S.­Giovanni­Battista,­Torino­(It):­G.Molino,­M.TorchioKoinè­Sistemi­S.p.A,­Torino­(It)­(from­2005)

- Domain independent­(e.g.,­bladder­cancer,­reflux­esophagitis,­heart­failure,­ichemic­

stroke)

- Phisician-oriented & User-friendly

GLARE Representation Formalism

In­GLARE,­a­clinical­guideline­is­a­hierarchical­graphs­of­actions

GLARE: Architecture of the KERNEL

Clinical­DB

Pharmac.­DB

Resource­DB

ICD­DB

ExpertPhysician

AcquisitionInterface

Guidelines­DB

Knowledge­Manager

UserPhysician

ExecutionInterface

Guidelines­Instantiation­DB

PatientDB

ExecutionModule

GLARE: Architecture of the system

GLARE­KERNEL

Decision-MakingModule

Temporal­ReasoningModule

UpdateModule

ContextualizationModule

Model-CheckingModule

SPIN

Overview of the presentation

•­ experience­in­the­GL­context:­the­GLARE­project­(sketch)­

• temporal information in clinical records temporal databases (TDBs)

•­ temporal­information­in­the­guidelines­­reasoning­about­temporal­constraints

• conclusions

Coping­with­temporal­information­in­patients’­records:­the­need­for­Temporal­Databases

(1) Need­for­coping­with­temporal­data­(both­VT­and­TT)

(2) Just­adding­1­(or­2,­or­4)­temporal­attributes­(and­maybe­some­ad-hoc­procedures)­does­not­work!

(3) First,­a­rigorous­semantic­framework­is­needed,­to­give­formal­specification­to­the­implementation.­Properties:­clearness,­expressiveness,­upward­compatibiliy.­­Ex.­BCDM

(4) Second,­the­implementation­must­be­proven­to­respect­the­semantics.­Core­issue­here:­efficient­(1-NF)­

implementations­hardly­grant­uniqueness­of­ representation.­Ex­TSQL2

(5) Other­issues­(my­work­in­the­area)

(1)­Need­for­coping­with­temporal­data­in­patients’­clinical­records

The­time­when­patients’­symptoms­hold,­laboratory­examinations­and­clinical­actions­are­performed,­etc.­are­core­pieces­of­information­­valid­time

The­time­when­data­are­inserted\deleted­into­the­DB­is­important­(e.g.,­justification­of­actions,­legal­purposes)­­transaction­time

Ex.­Lab­test­X­has­been­executed­on­Jan­1st­,­2008,­and­results­have­been­inserted­in­the­patient’s­clinical­record­on­­Jan­3rd

NOTICE:­major­problems­even­with­valid­time­alone!

Coping­with­temporal­information­in­patients’­records:­the­need­for­Temporal­Databases

(1) Need­for­coping­with­temporal­data­(both­VT­and­TT)

(2) Just­adding­1­(or­2,­or­4)­temporal­attributes­(and­maybe­some­ad-hoc­procedures)­does­not­work!

(3) First,­a­rigorous­semantic­framework­is­needed,­to­give­formal­specification­to­the­implementation.­Properties:­clearness,­expressiveness,­upward­compatibiliy.­­Ex.­BCDM

(4) Second,­the­implementation­must­be­proven­to­respect­the­semantics.­Core­issue­here:­efficient­(1-NF)­

implementations­hardly­grant­uniqueness­of­ representation.­Ex­TSQL2

(5) Other­issues­(my­work­in­the­area)

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Example­1.­Projection­(and­temporal­coalescing)

Name Salary Title VT_start VT_end

Andrea 60000 Ass.­Provost 1/1/1993 30/5/1993

Andrea 70000 Ass.­Provost 1/6/1993 30/9/1993

Andrea 70000 Provost 1/10/1993 31/1/1994

Andrea 70000 Professor 1/2/1994 31/12/1994

Question:­salary­history­of­Andrea

“Intuitive”­SQL­query:SELECT­Salary,VT_start,VT_endFROM­EmployeeWHERE­Name=“Andrea”

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Result­obtained: Salary VT_start VT_end

60000 1/1/1993 30/5/1993

70000 1/6/1993 30/9/1993

70000 1/10/1993 31/1/1994

70000 1/2/1994 31/12/1994

Desired­result: Salary VT_start VT_end

60000 1/1/1993 30/5/1993

70000 1/6/1993 31/12/1994

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

How­to­get­the­desired­result­using­SQL92:

CREATE­­TABLE­Temp(Salary,­VT_start,VT_end)AS­ SELECT­Salary,­VT_start,­VT_end

FROM­EmployeeWHERE­Name­=­“Andrea”;

RepeatUPDATE­Temp­T1SET­(T1.VT_end)=SELECT­MAX(T2.VT_end)WHERE­ T1.Salary=T2.Salary­AND­T1.VT_start­<­T2.VT_Start­AND­

T1.VT_end­>=­T2.VT_start­AND­T1.VT_end­<­T2.VT_endWHERE­EXISTS­ (SELECT­*

FROM­Temp­AS­T2WHERE­ T1.Salary=T2.Salary­AND­T1.VT_start­<­T2.VT_Start­

AND­­T1.VT_end­>=­T2.VT_start­AND­T1.VT_end­<­T2.VT_end)

Until­no­tuples­updated

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

How­to­get­the­desired­result­using­SQL92­(continues!):

DELETE­FROM­Temp­T1WHERE­EXISTS (SELECT­*

FROM­Temp­AS­T2WHERE­T1.Salary­=­T2.Salary­AND­((T1.VT_start­>­T2.VT_Start)­AND­(T1.VT_end­<=­T2.VT_end))­OR­((T1.VT_start­>=­T2.VT_Start)­AND­(T1.VT_end­<­T2.VT_end))­

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Underlying­semantic­phenomenon:

Projection­on­temporal­relations­involves­temporal­coalescing­about­value­equivalent­tuples

When­it­occurs­(SQL):

Whenever­a­proper­subset­of­the­attributes­of­the­relations­is­chosen­in­the­SELECT­part­of­the­query

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

How­to­get­the­desired­result­using­a­Temporal­DB­(ex.­TSQL2)

SELECT­SalaryFROM­EmployeeWHERE­Name­=­“Andrea”

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Example­2.­Join­(and­temporal­intersection)

Name Salary VT_start VT_end

Andrea 60000 1/1/1993 30/5/1993

Andrea 70000 1/6/1993 31/12/1994

Employee1

Name Title VT_start VT_end

Andrea Ass.­Provost 1/1/1993 30/9/1993

Andrea Provost 1/10/1993 31/1/1994

Andrea Professor 1/2/1994 31/12/1994

Employee2

Query:­“combined”­history­of­both­Andrea’s­salary­and­title

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

“Intuitive”­SQL­query:SELECT­Salary,­Title,­Emp1.VT_start,­Emp1.VT_end

­Emp2.VT_start,­Emp2.VT_end­FROM­ Employee1,­Employee2WHERE­ Employee1.Name=“Andrea”­AND

­ Employee1.Name=“Andrea”­

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Result­obtained:

Salary Emp1.VT_start

Emp1.VT_end

Title Emp2.VT_start

Emp2.VT_end

60000 1/1/1993 30/5/1993 Ass.­Provost 1/1/1993 30/9/1993

60000 1/1/1993 30/5/1993 Provost 1/10/1993 31/1/1994

60000 1/1/1993 30/5/1993 Professor 1/2/1994 31/12/1994

70000 1/6/1993 31/12/1994 Ass.­Provost 1/1/1993 30/9/1993

70000 1/6/1993 31/12/1994 Provost 1/10/1993 31/1/1994

70000 1/6/1993 31/12/1994 Professor 1/2/1994 31/12/1994

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Result­desired:

Salary Title VT_start VT_end

60000 Ass.­Provost 1/1/1993 30/5/1993

70000 Ass.­Provost 1/6/1993 30/9/1993

70000 Provost 1/10/1993 31/1/1994

70000 Professor 1/2/1994 31/12/1994

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

How­to­get­the­desired­result­using­SQL92:SELECT­Employee1.Name,Salary,Dept,Employee1.VT_start,Employee1.VT_endFROM­Employee1,­Employee2WHERE­Employee1.Name=Employee2.Name­AND­Employee2.VT_start­<=­Employee1.VT_start­AND­Employee1.VT_end­<­Employee2.VT_endUNIONSELECT­Employee1.Name,Salary,Dept,Employee1.VT_start,Employee2.VT_endFROM­Employee1,­Employee2WHERE­Employee1.Name=Employee2.Name­AND­Employee1.VT_start­>=­Employee2.VT_start­AND­Employee2.VT_end­<­Employee1.VT_end­AND­Employye1.VT_start­<­Employee2.VT_endUNIONSELECT­Employee1.Name,Salary,Dept,Employee2.VT_start,Employee1.VT_endFROM­Employee1,­Employee2WHERE­Employee1.Name=Employee2.Name­AND­Employee2.VT_start­>­Employee2.VT_start­AND­Employee1.VT_end­<­Employee2.VT_end­AND­Employye2.VT_start­<­Employee1.VT_endUNIONSELECT­Employee1.Name,Salary,Dept,Employee2.VT_start,Employee2.VT_endFROM­Employee1,­Employee2WHERE­Employee1.Name=Employee2.Name­AND­Employee2.VT_start­>­Employee1.VT_start­AND­Employee2.VT_end­<­Employee1.VT_end

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Underlying­semantic­phenomenon:

Join­(Cartesian­product)­on­temporal­relations­involves­temporal­intersection

When­it­occurs­(SQL):

Whenever­­more­than­one­relation­is­used­in­the­FROM­part­of­the­query

Note:­the­number­of­terms­in­the­SQL­union­is­2n,­where­n­is­the­number­of­relations­in­the­FROM­part

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

How­to­get­the­desired­result­using­a­Temporal­DB­(ex.­TSQL2)

SELECT­Salary,­TitleFROM­ Employee1,­Employee2WHERE­ Employee1.Name=“Andrea”­AND

­ Employee1.Name=“Andrea”­

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

And what about:-­ Union,­difference,­…,­nested­queries-­ Temporal­predicates-­ Primary\secondary­keys-­ Aggregate­functions-­ Integrity­constraints-­ Multiple­(user-defined!)­granularities-­ ……-­ arbitrary­combinations­of­all­the­above­issues

Until­now,­just­two­simple­examples­concerning:-­SELECT­a­subset­of­attributes­(­loop­to­do­colaescing)-­FROM­with­>1­relations­(exponential­union­to­do­intersection)

?

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Key­message:-­ Dealing­with­temporal­data­is­a­general­problem­in­DB’s-­ Difficult­problems­(often­“hidden”­ones)­have­to­be­faced

From­a­Software­Engeneering­point­of­view:Letting­applications­solve­the­problem­in­an­ad-hoc­way­is:-­ Both­cost­and­time­expensive-­ Likely­to­lead­to­errors­in­the­applications-­ Likely­to­make­integration­(shared­data)­between­­applications­impossible

Ad-hoc­approaches­are­complex­and­are­not­likely­to­work

Temporal DB: an area of research aiming at

providing once-and-forall principled and integrated solution to the problem

Coping­with­temporal­information­in­patients’­records:­the­need­for­Temporal­Databases

(1) Need­for­coping­with­temporal­data­(both­VT­and­TT)

(2) Just­adding­1­(or­2,­or­4)­temporal­attributes­(and­maybe­some­ad-hoc­procedures)­does­not­work!

(3) First,­a­rigorous­semantic­framework­is­needed,­to­give­formal­specification­to­the­implementation.­Properties:­clearness,­expressiveness,­upward­compatibiliy.­­Ex.­BCDM

(4) Second,­the­implementation­must­be­proven­to­respect­the­semantics.­Core­issue­here:­efficient­(1-NF)­

implementations­hardly­grant­uniqueness­of­ representation.­Ex­TSQL2

(5) Other­issues­(my­work­in­the­area)

BCDM(Bitemporal­Conceptual­Data­Model)

Snodgrass & Jensen, 1995

A­unifying­“consensus” model,­capturing­the­semantics­of­most­approaches­in­the­TDB­literature

-­­ Based­on­the­relational­model,­and­tuple­timestamping-­ A­purely­semantic­model­(efficiency­issues­are­not­taken­

into­account)

BCDM

Temporal­Domains

-­ Time­is­linear­and­totally­ordered-­ Chronons­are­the­basic­time­unit-­ Time­domains­are­isomorphic­to­subsets­of­the­domain­­

of­Natural­numbers

DVT­=­{t1,t2,­…,­tk} (valid­time)­DTT­=­{t’1,t’2,­…,­t’h}­­{UC}­ (transaction­time)DTT­­DVT ­(bitemporal­chronons)

BCDM

Data

Attribute­names:­DA={A1,­A2,­…,­An}Attribute­domains­DD={D1,­D2,­…,­Dn}

Schema­of­a­bitemporal­relation:R­=­Ai1,­Ai2,­…,­Aij­T

Domain­of­a­bitemporal­relation:Di1­­Di2­­…­­Dij­­­DTT­­DVT

Tuple­of­a­relation­r(R):x­=­(a1,­a2,­…,­aj­|­tB)

BCDM

Example.Relation­Employee­with­Schema:­(name,salary,T)

“Andrea­was­earning­60K­at­valid­times­10,­11,­12Such­a­tuple­has­been­inserted­into­Employee­at­time­12,­and­is­current­now­(say­now=13)”

(Andrea,­60k­|­{(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­……})­

VT

TT

10

12

12 13

11

BCDM

Example.Relation­Employee­with­Schema:­(name,salary,T)

“Andrea­was­earning­60K­at­valid­times­10,­11,­12Such­a­tuple­has­been­inserted­into­Employee­at­time­12,­and­is­current­now­(say­now=13)”

(Andrea,­60k­|­{(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(UC,10),­(UC,11),­(UC,12)})­

VT

TT

10

12

12 13

11

UC

BCDM

Bitemporal relation:­set­of­bitemporal­tuples.­Constraint:­Value­equivalent­tuples­are­not­allowed.

(Bitemporal)­DB:­set­of­(bitemporal)­relations

BCDM

Semantics (another viewpoint)

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(UC,10),­(UC,11),­(UC,12)}

John 50K {(12,12),(12,­13)}

(12,10)­­{Employee(Andrea,60K)}(12,11)­­{Employee(Andrea,­60K)}(12,12)­­{Employee(Andrea,­60K),­Employee(John,50K)}(12,13)­­{Employee(John,50K)}(13,10)­­{Employee(Andrea,60K)}(13,11)­­{Employee(Andrea,­60K)}……..(UC,12)­­{Employee(Andrea,­60K)}

BCDM

PROPERTIES

Consistent extension (of “classical” SQL DB)A­temporal­DB­is­a­set­of­“classical”­DBs,­one­for­each­bitemporal­chronon

Uniqueness of representation(from­the­constraint­about­value­equivalent­tuples)

BCDM

Semantics of UCe.g.,­the­DB’s­clock­thicks­time­14

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),(13,12),­(UC,10),­(UC,11),­(UC,12)}

John 50K {(12,12),(12,­13)}

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(14,10), (14,11), (14,12), (UC,10),­(UC,11),­(UC,12)}

John 50K {(12,12),(12,­13)}

BCDM

deletion (e.g.,­at­time­15)delete(Employee,­(Andrea,60K))­

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(14,10),­(14,11),­(14,12), (UC,10),­(UC,11),­(UC,12)}

John 50K {(12,12),(12,­13)}

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(14,10),­(14,11),­(14,12), (UC,10),­(UC,11),­(UC,12)}

John 50K {(12,12),(12,­13)}

BCDM

insertion (e.g.,­at­time­16)insert(Employee,­(Andrea,60K|{12,13}))insert(Employee,­(Mary,70K|{16}))

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(14,10),­(14,11),­(14,12), (UC,10),­(UC,11),­(UC,12)}

John 50K {(12,12),(12,­13)}

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(14,10),­(14,11),­(14,12),­(16,12),(16,13),(UC,12),(UC,13) }

John 50K {(12,12),(12,­13)}

Mary 70K {(16,16),(UC,16)}

BCDM

πD(r)={z­|­∃xr (z[D]=x[D]) ∧ ∀ yr (y[D]=z[D] ⇒ y[T] z[T]) ∧ ∀ tz[T] ∃yr (y[D]=z[D] ∧ ty[T])}

Algebraic Operators(Ex. Projection)

-­ No­value-equivalent­tuple­generated(uniqueness­of­representation!)

-­ Coalescing!

BCDM

BCDM­algebraic­operators­are­a­consistent­extension­of­SQL’s­ones(reducibility­and­equivalence)

Algebraic OperatorsProperties

BCDM

Reducibility

rT ρtT­(rT)ρtT

opT­(rT)

opT op

op(ρtT­(rT))ρtT

ρtT(opT­(rT))=

BCDM

Equivalence

rτt τt(r)

opTop

op(r)τt

τt(op(r))=opT­(τt(r))

BCDM

PROBLEM

Semantically­clear­but­…..­inefficient­(not­suitable­for­a­“direct”­implementation)

(1)­ Not­1-NF

(2)­ UC­(at­each­thick­of­the­clock,­all­current­tuples­

should­be­ updated!)

Coping­with­temporal­information­in­patients’­records:­the­need­for­Temporal­Databases

(1) Need­for­coping­with­temporal­data­(both­VT­and­TT)

(2) Just­adding­1­(or­2,­or­4)­temporal­attributes­(and­maybe­some­ad-hoc­procedures)­does­not­work!

(3) First,­a­rigorous­semantic­framework­is­needed,­to­give­formal­specification­to­the­implementation.­Properties:­clearness,­expressiveness,­upward­compatibiliy.­­Ex.­BCDM

(4) Second,­the­implementation­must­be­proven­to­respect­the­semantics.­Core­issue­here:­efficient­(1-NF)­

implementations­hardly­grant­uniqueness­of­ representation.­Ex­TSQL2

(5) Other­issues­(my­work­in­the­area)

An­example­of­implementation:­TSQL2(Snodgrass et al., 1995)

Temporal­attribute­T­­four­temporal­attributes­(TTS,­TTE,­VTS,­VTE)

Attribute­value:­a­timestamp­or­UC

Bitemporal­tuple:­A1,….An|­TTS,­TTE,­VTS,­VTE

Bitemporal­relation:­set­of­bitemporal­tuples

Notice: to retain the expressiveness of BCDM, value-equivalent tuples need to be allowed!

An­example­of­implementation:­TSQL2(Snodgrass­et­al.,­1995)

Name Salary T

Andrea 60K {(12,10),­(12,11),­(12,12),(13,10),­(13,11),­(13,12),­(UC,10),­(UC,11),­(UC,12)}

John 50K {(12,12),(12,­13)}

Name Salary TTS TTE VTS VTE

Andrea 60K 12 UC 10 12

John 50K 12 12 12 13

SEMANTICS

BCDM

TSQL2

An­example­of­implementation:­TSQL2(Snodgrass­et­al.,­1995)

Efficient­implementation­(data­model):-­ 1-NF-­ UC­managed­efficiently-­ clear­semantics­(mapping­onto­BCDM)

BUT

to get efficiency, we loose the uniqueness of representation property

Problem:­no­uniqueness­of­representation

Name Salary T

Andrea 60K {(10,2),­(10,3),­(11,­2),(11,3),­(12,1),­(12,2),(12,3),(12,4),(13,1),(13,2),(13,3),(13,4)}

Name Salary TTS TTE VTS VTE

Andrea 60K 10 11 2 3

Andrea 60K 12 UC 1 4

Name Salary TTS TTE VTS VTE

Andrea 60K 12 UC 1 1

Andrea 60K 10 UC 2 3

Andrea 60K 12 UC 4 4

BCDM

SEMANTICSTSQL2­(a)­ TSQL2­(b)­

Example. At­time­10,­the­fact­that­Andrea­earned­60K­from­2­to­3­inserted­in­Employee.­At­time­12,­such­a­tuple­is­updated:­Andrea­earned­60K­from­1­to­4.­At­time­13,­the­tuple­is­(logically)­deleted.

Problem:­no­uniqueness­of­representation

VT

TT

12

12 13

3

4

10 11

TSQL2­implementation:­“covering”­rectangles

Problem:­no­uniqueness­of­representation

VT

TT

12

12 13

3

4

10 11

TSQL2­Representation­(a)

Name Salary TTS TTE VTS VTE

Andrea 60K 10 11 2 3

Andrea 60K 12 UC 1 4

Problem:­no­uniqueness­of­representation

VT

TT

12

12 13

3

4

10 11

TSQL2­Representation­(b)

Name Salary TTS TTE VTS VTE

Andrea 60K 12 UC 1 1

Andrea 60K 10 UC 2 3

Andrea 60K 12 UC 4 4

Problem:­no­uniqueness­of­representation

VT

TT

12

12 13

3

4

10 11

Other­TSQL2­Representations!!

Problem:­no­uniqueness­of­representation

Name Salary TTS TTE VTS VTE

Andrea 60K 10 11 2 3

Andrea 60K 12 UC 1 4

Name Salary TTS TTE VTS VTE

Andrea 60K 12 UC 1 1

Andrea 60K 10 UC 2 3

Andrea 60K 12 UC 4 4

Potentially,­an­enormous­problem!

e.g.,­Return­all­employees­earning­more­than­50K­for­at­most­3­consecutive­time­chronons

Name TTS TTE VTS VTE

Andrea 12 UC 1 4?

Problem:­no­uniqueness­of­representation

One must grant that the temporal DB implementation respects its underlying semantics, independently of the representation

DB1

DB2

op1,­…,­opkDB1’

DB2’op1,­…,­opk

Given­two­“semantically­equivalent”­temporal­DBs,­and­given­any­sequence­of­operations,­the­results­are­always­“semantic­equivalent”

Otherwise …..We cannot trust DB’s results!

Problem:­no­uniqueness­of­representation

Solution. Step 1. Formal definition of “semantic equivalence”

Snapshot equivalence:Informally:­two­relations­(Databases)­are­snapshot­equivalent­if­they­are­identical­at­each­bitemporal­chronon

Problem:­no­uniqueness­of­representation

Solution. Step 2. Definition of manipulation and algebraic operators that preserve snapshot equivalence

e.g., proofs given about TSQL2 (bitemporal) operators

rB1

rB2

opBi

opBi

opBi(rB1)

opBi(rB2)

snapshotequivalent

snapshotequivalent

An­example­of­implementation:­TSQL2(Snodgrass­et­al.,­1995)

Last,­but­not­least,

An­high-level­query­and­manipulation­language­(minimal­extension­to­SQL­to­cope­with­time)­is­provided

An­example­of­implementation:­TSQL2(Snodgrass­et­al.,­1995)

Other­properties­proved­for­TSQL2

(Upward­Compatibility)TSQL2­is­a­consistent­extension­of­SQL

Personal­consideration

In­my­opinion,­ad-hoc­approaches­are­not­likely­to-­ Recognize-­ Solve-­ Prove­the­soundness­of­the­­solutionsthe­above­problems!

Coping­with­temporal­information­in­patients’­records:­the­need­for­Temporal­Databases

(1) Need­for­coping­with­temporal­data­(both­VT­and­TT)

(2) Just­adding­1­(or­2,­or­4)­temporal­attributes­(and­maybe­some­ad-hoc­procedures)­does­not­work!

(3) First,­a­rigorous­semantic­framework­is­needed,­to­give­formal­specification­to­the­implementation.­Properties:­clearness,­expressiveness,­upward­compatibiliy.­­Ex.­BCDM

(4) Second,­the­implementation­must­be­proven­to­respect­the­semantics.­Core­issue­here:­efficient­(1-NF)­

implementations­hardly­grant­uniqueness­of­ representation.­Ex­TSQL2

(5) Other­issues­(my­work­in­the­area)

My­past­and­current­contribution­to­the­area

-­ Treatment of temporal constraints in TDB(with­V.­Brusoni,­L.­Console,­B.­Pernici:­IEEE­Trans.­Data­and­Knowledge­Eng.,­1998)-­ Treatment of periodic data in TDB(IEEE­Trans.­Data­and­Knowledge­Eng.,­2003,­and­futher­ongoing­work­with­B.Stantic,­G.­Governatori,­A.­Sattar)-­ Treatment of telic vs atelic data in TDB(with­R.T.­Snodgrass,­IEEE­Trans.­Data­and­Knowledge­Eng.,­2004)-­ Extension to BCDM to cope with proposal\evaluation of updates (with­L.­Anselma,­A.­Bottrighi,­S.­Montani,­ongoing­work)-­ Extension to BCDM to cope with temporal indeterminacy(with­R.T.­Snodgrass,­ongoing­work)

Treatment­of­telic­vs­atelic­data­in­TDB­(joint­work­with­R.T.­Snodgrass)

Distinction­dates­back­to­Aristotle’s­Categories,­used­in­Linguistics,­Cognitive­Science,­and,­recently,­Artificial­Intelligence

Core­issue:Atelic­facts­(i.e.,­facts­without­a­goal­or­culmination)e.g.,­ (f1)­Andrea­earned­60K­from­10­to­11

(f2)­Andrea­earned­60K­from­12­to­12can­be­represented\evaluated­at­each­bitemporal­chronon

10­­{earn(Andrea,60K)}11­­{earn(Andrea,60K)}12­­{earn(Andrea,60K)}

­BCDM­“snapshot”­semantics­is­OK­for­atelic­facts

Treatment­of­telic­vs­atelic­data­in­TDB

Core­issue:Telic­facts­(i.e.,­facts­with­a­goal­or­culmination)e.g.,­ (f1)­Andrea­build­a­house­from­10­to­11

(f2)­Andrea­build­a­house­from­12­to­12cannot­­be­represented\evaluated­at­each­bitemporal­chronon

10­­{build_house(Andrea)}11­­{build_house(Andrea)}12­­{build_house(Andrea)}

­BCDM­“snapshot”­semantics­does­not­work­for­telic­facts

Treatment­of­telic­vs­atelic­data­in­TDB

(1) Analysis­of­the­impact­of­neglecting­the­telic\atelic­distinction­in­TDBs

(2) Solution:­(2.1) a­two-sorted­semantics- chronon-by-chronon­for­atelic­facts- interval-based­for­telic­facts

(2.2) three­sorted­data­model(2.3) three-sorted­manipulation­and­algebraic­operators(2.4) properties(2.5) high-level­query­language

Treatment­of­proposal\evaluation­of­updates(extension­to­BCDM)­

(joint­work­with­L.­Anselma,­A.­Bottrighi,­S.­Montani)

Needed­to­cope­with­updates­concerning­GLs!

Important­in­several­other­applications,­e.g.,­Wiki-based­vocabularies­(Citizendium)

Notice:­TT­needed­to­store­the­history­of­updates!

Main­issues:- Distinguishing­between­two­level­of­users­(proposers­and­evaluators)

- Delaying­the­effect­of­proposals- Coping­with­alternative­tuples

Overview of the presentation

•­ experience­in­the­GL­context:­the­GLARE­project­(sketch)

•­ temporal­information­in­clinical­records­­ temporal­databases­(TDB)

• temporal information in the guidelines reasoning about temporal constraints

• conclusions

Temporal­Constraints­in­Clinical­Guidelines

Temporal­constraints­are­an­intrinsic­part­of­clinical­knowledge­(e.g.,­ordering­of­the­therapeutic­actions)

Different­kinds­of­temporal­constraints,­e.g.,­

- duration­of­actions­(min­/­max)­

- qualitative­constraints­(e.g.,­before,­during)

- delays­(min­/­max)

- periodicity­constraints­on­repeated­actions

Temporal­Constraints­in­Clinical­Guidelinesrepetitions

The therapy for multiple mieloma is made by six cycles of 5-day treatment, each one followed by a delay of 23 days (for a total time of 24 weeks). Within each cycle of 5 days, 2 inner cycles can be distinguished: the melphalan treatment, to be provided twice a day, for each of the 5 days, and the prednisone treatment, to be provided once a day, for each of the 5 days. These two treatments must be performed in parallel.

Managing­Temporal­Constraint:­the­Problem

Temporal Constraints without Temporal Reasoning (constraint propagation)- are useless- clash against users’ intuitions/expectations

Both representation and inference are NEEDED

Managing­Temporal­Constraints:­the­Problem

Implied constraint (temporal reasoning):(1.6)­C­ends­between­30­and­60­m­after­the­start­of­A

(1.1)­the­end­of­A­is­equal­to­the­start­of­B(1.2)­the­end­of­B­is­equal­to­the­start­of­C(1.3)­the­duration­of­A­is­between­10­and­20­m(1.4)­the­duration­of­B­is­between­10­and­20­m(1.5)­the­duration­of­C­is­between­10­and­20­m

A B C10-20 10-2010-20

Correct (consistent) assertion:(1.7)­C­ends­between­30­and­50­m­after­the­start­of­A

Not correct (inconsistent) assertion:(1.8)­C­ends­more­than­70­m.­after­the­start­of­A

However: Temporal Reasoning is NEEDED in order to support such an intended semantics!

Managing­Temporal­Constraints:­the­Problem

DESIDERATA­for­Temporal­Reasoning­Algorithms

-­tractability­­“reasonable”­response­time­

-­correctness­­no­wrong­inferences

-­completeness­­reliable­answers

DESIDERATA­for­the­Representation­formalism

-­expressiveness­­capture­most­temporal­constraints­in­GL

TRADE-OFF!

SPECIALIZED­APPROACHES­(since­’80­in­AI­literature)

Digression:­Why­Completeness­is­fundamental?

Implied constraint (temporal reasoning):(1.6)­C­ends­between­30­and­60­m­after­the­start­of­A

(1.1)­the­end­of­A­is­equal­to­the­start­of­B(1.2)­the­end­of­B­is­equal­to­the­start­of­C(1.3)­the­duration­of­A­is­between­10­and­20­m(1.4)­the­duration­of­B­is­between­10­and­20­m(1.5)­the­duration­of­C­is­between­10­and­20­m

A B C10-20 10-2010-20

Suppose­that­temporal­reasoning­is­NOT complete,­so­that­(1.6)­is­not­inferred.­One­can­add­inconsistent­info,­e.g.,­C­ends­more­than­70­m.­after­the­start­of­Aand­the­system­accept­it!­ inconsistent GL!

Complete Temporal Reasoning is NEEDED in order to grant that GL is temporally consistent (executable!)

Temporal­Constraint­Treatment

WHEN Temporal Reasoning is useful in Guidelines?

ACQUISITION

-­ to­check­consistency­(notice:­an­inconsistent­GL­cannot­be­executed!!!)

EXECUTION

- to­compare­the­duration­of­paths,­­in­hypothetical­reasoning­­(simulation)­facilities

- to­check­that­the­time­of­execution­of­actions­on­patients­is­consistent­with­the­constraints­in­the­guideline­

- to­schedule­next­actions

Temporal­Representation­and­Temporal­Reasoning­for­Clinical­Guidelines

• Different­kinds­of­temporal­constraints

• No­current­approach­in­the­AI­literature­covers­all­of­them

• Our­proposal:­an­extension­of­the­“consensus”­STP­approach­[Dechter­et­al.,­91]

• Our­goal:­expressiveness­+­correct,­complete­and­tractable­inferences

GLARE’s­approach­(representation)Labeled­tree­of­STPs­(STPs-tree)

Tree of STPs for the multiple mieloma chemotherapy guideline.The overall therapy (node N1) is composed by 6 cycles of 5 days plus a delay of 23 days . In each cycle (node N2), two therapies are executed in parallel: Alkeran (node N3: Sa and Ea are the starting and ending nodes), to be repeated twice a day, and Deltacorten (node N4: Sd and Ed are the starting and ending nodes), to be repeated once a day. Arcs between any two nodes X and Y in a STP (say N2) of the STP-tree are labeled by a pair [n,m] representing the minimal and maximal distance between X and Y.

Other­formal­approaches­to­temporal­reasoning­in­clinical­guidelines

Miksh­­et­al.- an­extension­of­STP’s­metric­contraints- attention­to­constraint­visualization

Shahar­et­al.- temporal­reasoning­oriented­towards­

temporal­abstraction­(e.g.,­persistence­phenomena)

Conclusions

Formal­methods­are­necessary­in­order­to­properly­cope­with­temporal­phenomena­related­to­GL­management

Temporal­information­in­patients’­records­­TDBs­needed- ­ad-hoc­solutions­are­complex,­expensive,­not­likely­to­work- a­clear­semantics­is­needed-­ an­efficient­(1-NF)­implementation­respecting­the­semantics­is­­

needed.­(semantic­equivalence­must­be­preserved,­otherwise, results cannot be trusted!)

-­ any­implementation­should­be­a­consistent­extension­of­SQL­(otherwise, previous work is lost!)

Temporal­constraints­in­GLs­­Temporal­reasoning­needed- (temporally)­inconsistent­GL­cannot­be­executed!- to­detect­which­are­the­next­candidate­actions,­and­

when­they­need­to­be­executed­

Conclusions

More­generally,­I­strongly­believe­that­formal methods are recommended\necessary in­order­to­properly­cope­with­many­other­different­phenomena­related­to­GL­management,­including

-­ Decision­support­(e.g.,­Decision­Theory)-­ Verification­(e.g.,­model-checking,­theorem­proving,­Petri­Nets)-­ Simulation­(e.g.,­Petri­Nets)-­ Formal­semantics­(Temporal­logics,­Operational­semantics,­

Mapping­techniques)-­ ………­!!!

Thanks for your attention!