Temporal Mediators:Integration of Temporal Reasoning and Temporal-Data Maintenance

Yuval Shahar MD, PhDYuval Shahar MD, PhD

Temporal Reasoning and Planning in Medicine

Temporal Reasoning and Temporal Maintenance

Temporal reasoningTemporal reasoning supports inference tasks supports inference tasks involving time-oriented data; often connected with involving time-oriented data; often connected with artificial-intelligence methodsartificial-intelligence methods

Temporal data maintenanceTemporal data maintenance deals with storage and deals with storage and retrieval of data that has multiple temporal retrieval of data that has multiple temporal dimensions; often connected with database systemsdimensions; often connected with database systems

Both require temporal data modellingBoth require temporal data modelling

Clinicaldecision-supportapplication

TM TR DB

Examples of Temporal-Maintenance Systems

TSQL2TSQL2, a bitemporal-database query , a bitemporal-database query language (Snodgrass et al., Arizona)language (Snodgrass et al., Arizona)

TNETTNET and the and the TQueryTQuery language (Kahn, language (Kahn, Stanford/UCSF)Stanford/UCSF)

The The ChronusChronus//Chronus2Chronus2 projects (Stanford) projects (Stanford)

Examples of Temporal-Reasoning Systems

RÉSUMÉRÉSUMÉ M-HTPM-HTP TOPAZTOPAZ TrenDxTrenDx

A Typical TM and TR Application:Automated Support to Therapy by

Clinical Guidelines/Protocols

Clinical guidelines/protocols contain Clinical guidelines/protocols contain recommendations for medical interventions that recommendations for medical interventions that are predicated on the observation of:are predicated on the observation of: relevant temporal patterns of these statesrelevant temporal patterns of these states relevant patient statesrelevant patient states

CCTG-522 Recommendation

Modify the standard dose of AZT for a patient Modify the standard dose of AZT for a patient if, if, during treatment with the protocolduring treatment with the protocol, the , the patient experiences a patient experiences a second episodesecond episode of of moderate anemiamoderate anemia that has that has persisted for more persisted for more than two weeksthan two weeks

Protocol-Based Decision Support System

Presents patient-specific recommendationsPresents patient-specific recommendations Needs a method for verifying the presence of Needs a method for verifying the presence of

time-oriented patient conditions in a databasetime-oriented patient conditions in a database

Information Mismatch

Temporal Abstraction

Defined as the creation of high-level summaries of Defined as the creation of high-level summaries of time-oriented datatime-oriented data

Necessary becauseNecessary because clinical databases usually store raw, time-stamped dataclinical databases usually store raw, time-stamped data protocols often require information in high-level termsprotocols often require information in high-level terms

Temporal Patterns

Temporal Maintenance

Defined as the storage of time-oriented data and Defined as the storage of time-oriented data and the selective retrieval of that data based on some the selective retrieval of that data based on some time-oriented constrainttime-oriented constraint

Necessary because clinical conditions may be Necessary because clinical conditions may be defined as temporal patternsdefined as temporal patterns temporal ordertemporal order temporal durationtemporal duration temporal contexttemporal context

Temporal Data Manager

PerformsPerforms temporal abstraction of time-oriented datatemporal abstraction of time-oriented data temporal maintenancetemporal maintenance

Used for tasks such as finding in a patient Used for tasks such as finding in a patient database which patients fulfil eligibility conditions database which patients fulfil eligibility conditions (expressed as temporal patterns), assessing the (expressed as temporal patterns), assessing the quality of care by comparison to predefined time-quality of care by comparison to predefined time-oriented goals, or visualization temporal patterns oriented goals, or visualization temporal patterns in the patient datain the patient data

Embedding A Temporal Data Manager Within a Guideline-

Support System Can be embedded within a Can be embedded within a

larger decision support larger decision support framework, e.g., EONframework, e.g., EON

Mediates all access to the Mediates all access to the external clinical databaseexternal clinical database

1) Extend DBMS 2) Extend Application

Two Implementation Strategies

Database

Application

Temporal Data Management

Database

Application

Temporal Data Management

Problems Extending DBMS

Temporal data Temporal data management methods management methods implemented in implemented in DBMS:DBMS: are limited to are limited to

producing very simple producing very simple abstractionsabstractions

are often database-are often database-specificspecific

Database

Application

Temporal Data Management

Problems Extending Applications

Temporal data Temporal data management methods management methods implemented in implemented in applications: applications: duplicate some duplicate some

functions of the DBMSfunctions of the DBMS are application-specificare application-specific Database

Application

Temporal Data Management

Our Strategy

Separates data Separates data management methods management methods from the application from the application and the databaseand the database

Decomposes temporal Decomposes temporal data management into data management into two general tasks:two general tasks: temporal abstractiontemporal abstraction temporal maintenancetemporal maintenance

Database

Application

Temporal AbstractionTemporal Querying

The Tzolkin Temporal Mediator Architecture

Database

Application

Temporal-QueryingModule

TemporalAbstraction

Module

KnowledgeBase

Tzolkin

ResultsQuery

AbstractionKnowledge

RÉSUMÉ: Temporal AbstractionRÉSUMÉ: Temporal Abstraction

Creates summaries of time-oriented dataCreates summaries of time-oriented data Clinical data is usually stored as “low-level” dataClinical data is usually stored as “low-level” data Protocols often specify conditions as “high-level”, Protocols often specify conditions as “high-level”,

interval-based conceptsinterval-based concepts

Is domain-independentIs domain-independent Has a tool that facilitates knowledge acquisition Has a tool that facilitates knowledge acquisition

and maintenanceand maintenance

Temporal Abstraction of Hb

Chronus: Temporal Maintenance

Provides temporal extensions to SQLProvides temporal extensions to SQL

Historical relational modelHistorical relational model Each tuple has two time stampsEach tuple has two time stamps Time stamps conferred special statusTime stamps conferred special status

Temporal algebra that supports temporal Temporal algebra that supports temporal manipulationsmanipulations Closed algebraClosed algebra Complete for the temporal conditions found in Complete for the temporal conditions found in

protocolsprotocols

Chronus TimeLine SQL (TL-SQL)

GRAIN WEEKSELECT 2ND problem_nameFROM problems_tableWHERE problem_name = ‘Hb’WHENSTART_TIME BEFORE 1/1/99

Coupling RÉSUMÉ and ChronusRÉSUMÉ and Chronus

Integrates Integrates temporal abstractiontemporal abstraction and and temporal temporal query processingquery processing

Allows retrieval of summaries of clinical data Allows retrieval of summaries of clinical data using time-oriented conditionsusing time-oriented conditions

Modify the standard dose of AZT for a patient if, Modify the standard dose of AZT for a patient if, during treatment with the protocolduring treatment with the protocol, the patient , the patient experiences a experiences a second episodesecond episode of of moderate anemiamoderate anemia that has that has persisted for more than two weekspersisted for more than two weeks

SQLA Interface Language

Based on SQLBased on SQL Supports temporal queriesSupports temporal queries Detects when abstractions are requested and computes them Detects when abstractions are requested and computes them

on the flyon the fly

GRAINGRAIN WEEKWEEK CONTEXTCONTEXT CCTG-522CCTG-522

SELECTSELECT 2ND2ND problem_name problem_nameFROMFROM problems_tableproblems_tableWHEREWHERE problem_name = problem_name = ‘HbState’‘HbState’

and value = ‘moderate anemia’and value = ‘moderate anemia’WHENWHEN DURATION (start, stop) > 2DURATION (start, stop) > 2

Query-Evaluation Algorithm

A Detailed Example

GRAINGRAIN WEEKWEEK CONTEXTCONTEXT CCTG-522CCTG-522

SELECTSELECT 2ND2ND problem_name problem_nameFROMFROM problems_tableproblems_tableWHEREWHERE problem_name = problem_name = ‘HbState’‘HbState’

and value = ‘moderate anemia’and value = ‘moderate anemia’WHENWHEN DURATION (start, stop) > 2DURATION (start, stop) > 2

Loading the Domain Knowledge

Examine the context clause of the SQLA statement, which Examine the context clause of the SQLA statement, which contains a reference to a knowledge basecontains a reference to a knowledge base

Use the reference to locate and load the appropriate Use the reference to locate and load the appropriate knowledge baseknowledge base

Detecting the Need for Abstractions

Find non-Find non-SQLASQLA terms in terms in WHEREWHERE clause clause (“HbState” and (“HbState” and

““moderate anemia”)moderate anemia”)

Look up terms in Look up terms in RESUME KBRESUME KB

If look-up succeeds, If look-up succeeds, Tzolkin needs to compute Tzolkin needs to compute abstractionsabstractions(“HbState”)(“HbState”)

Hb WBC Plt

HbState WBCState PltState

Blood State

Loading the Data Primitives

Locate the requested Locate the requested abstraction in the abstraction in the RESUME KBRESUME KB(“HbState”)(“HbState”)

Find the primitive Find the primitive parameters (leaves of the parameters (leaves of the tree) below ittree) below it(“Hb”)(“Hb”)

Load all patient data of Load all patient data of these parameter types into these parameter types into RESUMERESUME

Hb WBC Plt

HbState WBCState PltState

Blood State

Generating the Interpretation Contexts within RÉSUMÉRÉSUMÉ

Find the Find the typestypes of events and abstractions that can induce of events and abstractions that can induce a context (via a dynamic induction relation of contexts)a context (via a dynamic induction relation of contexts)(context CCTG522 can be induced by event: “enroll-CCTG 522”)(context CCTG522 can be induced by event: “enroll-CCTG 522”)

Locate patient-specific instances of these eventsLocate patient-specific instances of these events(patient enrolled in CCTG 522 on 10/10/1999)(patient enrolled in CCTG 522 on 10/10/1999)

Compute all abstractions that can induce a context Compute all abstractions that can induce a context (recursive process)(recursive process)

RESUME will then generate the appropriate contextsRESUME will then generate the appropriate contexts

Invoking RÉSUMÉRÉSUMÉ and Chronus

Execute RESUME to compute the requested abstractionsExecute RESUME to compute the requested abstractions The computed abstractions are stored in the databaseThe computed abstractions are stored in the database RESUME signals Tzolkin when it is doneRESUME signals Tzolkin when it is done

Then execute Chronus to retrieve the results Then execute Chronus to retrieve the results

Future Research Directions

Enhancement of the query languageEnhancement of the query language Addition of Addition of truth-maintenancetruth-maintenance capabilities to the capabilities to the

databasedatabase Addition of “Addition of “what-ifwhat-if” query support” query support Provision of complete Provision of complete dynamicdynamic ( (goal-directedgoal-directed) )

query computationquery computation