AN ONTOLOGICAL APPROACH TO REPRESENTING AND

REASONING WITH TEMPORAL CONSTRAINTS IN CLINICAL

TRIAL PROTOCOLS

Ravi D. Shankar, Susana B. Martins, Martin J. O’Connor and Amar K. Das

Stanford Medical Informatics, Stanford University

251 Campus Drive, MSOB X215, Stanford, California, 94305, USA

Keywords: Ontology, temporal reasoning, clinical trials, biomedical informatics, Semantic Web, OWL.

Abstract: Temporal constraints play an important role in the specification and implementation of clinical trial

protocols, and subsequently, in the querying of the generated trial data. Protocols specify a temporal

schedule of clinical trial activities such as tests, procedures, and medications. The schedule includes

temporal constraints on the sequence of these activities, on their duration, and on potential cycles. In this

paper, we present our approach to formally represent temporal constraints found in clinical trials. We have

identified a representative set of temporal constraints found in protocols to study immune tolerance. Our

research group has developed a temporal constraint ontology that allows us to formulate the temporal

constraints to the extent required to support clinical trials management. We use this ontology to provide

temporal annotation of clinical activities in an encoded clinical trial protocol. We have developed a

temporal model that represents time-stamped data and facilitates interval-based temporal operations on the

data. Using semantic web technologies, we are building a knowledge-based framework that integrates the

temporal constraint ontology with the temporal model to support queries on clinical trial data. Using our

approach, we can formally specify temporal constraints, and reason with the temporal knowledge to support

management of clinical trials.

1 INTRODUCTION

Clinical trials are formal studies on participants to

systematically evaluate the safety and efficacy of

new or unproven approaches in the prevention and

treatment of medical conditions in humans. A

clinical trial protocol is a document that includes

study objectives, study design, participant eligibility

criteria, enrollment schedule, and study plan. It

specifies a temporal schedule of clinical trial

activities such as tests, procedures, and medications.

The schedule includes temporal constraints on the

sequence of these activities, on their duration, and

on potential cycles. A temporal constraint is defined

as an interval-based temporal annotation on a

domain entity in relationship with other entities.

Temporal constraints are fundamental to the

descriptions of protocol entities, such as he

following specifications: Participants will be

enrolled at least two days apart; Participant is

ineligible if he/she had vaccination with a live virus

within the last 6 weeks before enrollment; The first

dose will be infused over a minimum of 12 hours;

Visit 10 for the participant occurs 3 weeks ± 2 days

from the day of transplant. There is an enormous

requirement on the execution of a clinical trial to

conform to the temporal constraints found in the

protocol. Studies need to be tracked for the purposes

of general planning, gauging progression,

monitoring patient safety, and managing personnel

and clinical resources. The tracking effort is

compounded by the fact that a trial often is carried

out at multiple sites, geographically distributed,

sometimes across the world. The validity of the

findings of the clinical trial depends on the clinical

trial personnel and the participants performing

clinical trial activities as planned in the protocol.

More importantly, the treatment and assessment

schedules should be strictly followed to ensure the

safety of participants.

We have developed an ontological framework

that we call Epoch (Shankar et al., 2006), to support

the management of clinical trials at the Immune

Tolerance Network, or ITN (Rotrosen et al., 2002)

D. Shankar R., B. Martins S., J. O’Connor M. and K. Das A. (2008).

AN ONTOLOGICAL APPROACH TO REPRESENTING AND REASONING WITH TEMPORAL CONSTRAINTS IN CLINICAL TRIAL PROTOCOLS.

In Proceedings of the First International Conference on Health Informatics, pages 87-93

 SciTePress

(http://www.immunetolerance.org/). As part of this

effort, we have developed a suite of ontologies that,

along with semantic inferences and rules, provide a

formal protocol definition for clinical trial

applications. We use the OWL Web Ontology

language (http://www.w3.org/2004/OWL/), which is

a W3C standard language for use in Semantic Web

where machines can provide enhanced services by

reasoning with facts and definitions expressed in

OWL. Central to our ontological effort is the

modeling of temporal constraints that we identified

in clinical trial protocols. We have created a

temporal constraint ontology to formally represent

temporal constraints. The ontological representation

can then be used to construct rules that can be used

in turn, for reasoning with temporal constraints.

Thus, at protocol specification phase, a domain

expert can capture the essence of temporal

constraints using higher-level ontological constructs.

At a later time, a software developer can fully

encode the constraints by creating rules in terms of

temporal patterns and other protocol entities in the

ontologies. We are using SWRL, the Semantic Web

Rule Language

(http://www.w3.org/Submission/SWRL/) to write

the rules. At execution time of the protocol, the rule

elements use the protocol knowledge specified in the

Epoch ontologies, and the clinical trial data collected

in the clinical trial databases to reason with the

temporal constraints. In this paper, we discuss our

work in identifying temporal constraints found in

ITN’s clinical trial protocols. We then discuss our

temporal constraint ontology using some patterns

that we found in the temporal constraints. We then

show how we use the temporal constraint ontology

along with other Epoch ontologies to create rules

that are then executed at runtime to support clinical

trial management.

2 TRIAL CONSTRAINTS

A clinical trial protocol defines a protocol schema

that divides the temporal span of the study into

phases such as the treatment phase and follow-up

phase, and specifies the temporal sequence of the

phases. It also includes a schedule of activities that

enumerates a sequence of protocol visits that are

planned at each phase, and, for each visit, specifies

the time window when the visit should happen and a

list of protocol activities (assessments, procedures

and tests) that are planned at that visit. Activities

such as medication need not be confined to visits

and can be planned to occur in a time window within

a protocol phase. An activity can have sub activities

that impose additional temporal constraints. For

example, an assessment activity can include

collection and processing of biological specimens

with associated temporal constraints.

Here is a representative set of temporal

constraints that we found in the ITN protocols that

we are encoding:

1. Visit 17 must occur at least 1 week but no

later than 4 weeks after the end of 2003

ragweed season.

2. Administer Rapamune 1 week from Visit 0

daily for 84 days.

3. Visit 1 should occur 2 weeks ± 3 days after

transplant.

4. Screening visit evaluations must occur

between 30 days prior to Visit -1 and 45 days

prior to Visit 0.

5. The vital signs of the participant should be

obtained at routine time points starting at 10

minutes post infusion, then at 20-minute

intervals until the participant is discharged.

6. Administer study medication at weekly

intervals for 3 months.

7. Clinical assessments are required twice a

week until Day 28 or discharge from hospital.

8. The first and second blood draws are 10 days

apart, and the third draw is 11-14 days after

the second.

9. On days that both IT and omalizumab are

administered, omalizumab will be injected 60

minutes after the IT.

10. Monitor cyclosporine levels 3 times per week

while in-patient, then weekly as out-patient.

As evident in the constraints, clinical activities

—we are using the terms activity and event

interchangeably— are temporally dependent on each

other. The temporal annotations in the constraints

are specified in relative terms typically with

reference to one or more clinical events. At the

protocol execution time, the actual times of these

events found in the clinical data will be used to

reason with the constraints. There can also be

fuzziness in the relative start and end times as well

as in the duration of the activity. An activity can be

repeated at a periodic interval for a specific number

of times or until a condition is satisfied. The periodic

interval can be a single offset or a set of offsets. The

temporal annotation of an activity or the temporal

ordering of activities can be conditional on other

events.

HEALTHINF 2008 - International Conference on Health Informatics

3 TEMPORAL

REPRESENTATION

We have developed a temporal constraint ontology

that can be used to formally specify the temporal

constraints found in the clinical trial protocols. We

briefly describe the core entities of the underlying

temporal representation below:

Anchor defines an unbound time point that can

be used to specify temporal relations among

activities. It can be used as a reference point to

define the start of another event before or after the

anchor. In example 1 (of the constraints listed

earlier), end of 2003 ragweed season is an anchor

used to define the start of Visit 17. During the

execution of the protocol, an anchor is bound to the

absolute time of the anchor as recorded in the

clinical trial data.

Duration is the difference between two time

points. It is used typically to specify how long an

activity lasts. In example 2, 84 days is a duration.

Anchored Duration relates two activities with a

temporal offset. In example 2, the activity

administer Rapamune is offset from the anchor Visit

0 by 1 week.

Varying Duration is defined as duration with a

high variance and a low variance. In example 3, 2

weeks ± 3 days specifies a varying offset between

transplant and Visit 1.

Start and End Expression constrains the start

and the end of an activity and is expressed as offsets

before or after one or more reference events. In

example 4, the start of the activity Screening visit

evaluations is 30 days before the anchor Visit -1 and

the end is 45 days before another anchor Visit 0.

Cyclical Plan Expression formulates events that

are repeated at periodic intervals. The repetition

ends typically when a specific number of cycles is

reached or until a specific condition is satisfied.

There are two types of cyclical plans with subtle

differences. The first type has a single anchor point

with potentially multiple intervals. In example 5, the

vital signs assessments are planned at 10, 30, 60, 90,

120, and 180 minutes after infusion. If the

participant gets off schedule because the assessment

is made at minute 35 instead of minute 30, then the

participant gets back on schedule with the next

assessment at minute 60. This type of cyclical plan

is used generally with assessments and tests where

evaluations need to be made at specific intervals

after a clinical intervention. The second type of

cyclical plan can potentially have multiple anchors

with a single offset. In example 6, the plan is to

administer medication at weekly intervals for 3

months. The initial anchor is the event of

administering the first dose. According to the

schedule, the second dose will be 1 week later, and

the third 1 week later from the second dose. If the

participant gets off schedule because the drug was

administered 5 days after first dose and not 7 days,

then the participant gets back on schedule with the

next dose at 7 days from the last dose. This type of

cyclical plan is used typically with drug

administration where fixed intervals between

dosages need to be maintained for safety and

efficacy purposes.

Conditional Expression allows associating

different temporal annotations with a single activity

based on a condition. There are three patterns of

conditional expressions – if-then, if-then-else and

until-then patterns. Example 9 illustrates the if-then

pattern – the temporal constraint between the

administrations of two drugs is dependent on the

condition that the two drugs are administered on the

same day. Example 10 illustrates the until-then

pattern – the monitoring activity is performed 3

times a week until the participant is in in-patient

status, and when the status changes to out-patient

then the activity is performed weekly.

4 EPOCH ONTOLOGIES

In order to support clinical trial management

activities, the Epoch knowledge-based approach

provides three methods: 1. knowledge acquisition

methods that allow users to encode protocols, 2.

ontology-database mapping methods that integrate

the protocol and biomedical knowledge with clinical

trial data including clinical results and operational

data stored in the ITN data repository, and 3.

concept-driven querying methods that support

integrated data management, and that can be used to

create high-level abstractions of clinical data during

analysis of clinical results. At the center of all these

methods is the suite of Epoch ontologies that

provide a common nomenclature and semantics of

clinical trial protocol elements. We shall describe

each of the core ontologies.

4.1 Protocol Ontology

The protocol ontology is a knowledge model of the

clinical trial protocol. It simplifies the complexity

inherent in the full structure of the protocol by

focusing only on concepts required to support

clinical trial management. Other concepts are either

ignored or partially represented. The main concepts

represented in the protocol ontology are the protocol

schema and the schedule of activities.

AN ONTOLOGICAL APPROACH TO REPRESENTING AND REASONING WITH TEMPORAL CONSTRAINTS IN

CLINICAL TRIAL PROTOCOLS

4.2 Temporal Constraints Ontology

The temporal constraints ontology models the class

of temporal constraints found in clinical trial

protocols (see Section 3).

4.3 Virtual Trial Data Ontology

The virtual trial data ontology encapsulates the

study data that is being collected, such as participant

clinical record, specimen workflow logs, and site

related data. A mapping component can then map

clinical trial data (found in a relational database) to

these virtual data records using a mapping ontology.

The data model concept is similar to the Virtual

Medical Record (Johnson et al., 2001) specification

promoted in the clinical guideline modeling efforts.

4.4 Temporal Model

The temporal model provides a valid-time model of

the temporal component of clinical trial data. In this

model, all facts have temporal extent and are

associated with instants or intervals denoting the

times that they are held to be true. The core concept

in the model is the extended proposition class that

represents information that extends over time. There

are two types of extended propositions in the model:

1. extended primitive propositions that represent data

derived directly from secondary storage, and 2.

extended abstract propositions that are abstracted

from other propositions. These extended

propositions can be used to consistently represent

temporal information in ontologies. For example, a

set of participant visits in a clinical trial data can be

represented by defining a class called VisitRecord

that inherits the valid time property from extended

proposition class. The valid time property will then

hold a visit’s actual occurrence time. Similarly, an

extended primitive proposition can be used to

represent a drug regimen, with a value of type string

to hold the drug name and a set of periods in the

valid time property to hold drug delivery times. A

more detailed discussion of the temporal model can

be found elsewhere in the literature (O'Connor et al.,

2006).

5 OWL IMPLEMENTATION

We have developed these ontologies in OWL by

building hierarchies of classes describing concepts

in the ontologies and relating the classes to each

other using properties. OWL can also represent data

as instances of OWL classes —referred to as

individuals— and also provides mechanisms for

reasoning with the data and manipulating it. OWL

also provides a powerful constraint language for

precisely defining how concepts in ontology should

be interpreted. The Semantic Web Rule Language

(SWRL) allows users to write Horn-like rules that

can be expressed in terms of OWL concepts and that

can reason about OWL individuals. SWRL provides

deductive reasoning capabilities that can infer new

knowledge from an existing OWL knowledge base.

We use SWRL to specify temporal constraints. Once

all temporal information is represented consistently

using the temporal model, then SWRL rules can be

written in terms of this model and the temporal

constraint ontology. However, the core SWRL

language has limited temporal reasoning

capabilities. A few temporal predicates called built-

ins are included in the set of standard predicates, but

they have limited expressive power. SWRL provides

an extension mechanism to add user-defined

predicates. We used this mechanism to define a set

of temporal predicates to operate on temporal

values. These predicates support the standard Allen

temporal operators (Allen, 1993). Using these built-

in operators in conjunction with the temporal model,

we can express complex temporal rules. Here is an

example SWRL rule to check if participants conform

to the visit schedule specified in the protocol:

Participant(?p) ^

hasVisitRecord(?p, ?vr) ^

hasVisitId(?vr, ?vid1) ^

hasValidTime(?vr, ?vt) ^

Visit(?v) ^

hasVisitId(?vr, ?vid2) ^

hasStartExpression(?vr, ?se) ^

swrlb:equal(?vid1, ?vid2) ^

temporal:inside(?vt, ?se) ^

-> ConformingParticipant(?p)

This rule uses concepts such as Participant and Visit

from the protocol ontology and the concept of Start

Expression from the temporal constraint ontology.

The class of actual visits undertaken by a participant

is the VisitRecord in the virtual trial data ontology,

and is modeled as an extended proposition. The rule

uses two built-ins – equal, that checks if two strings

are equal, and inside, which is a built-in that we

developed to check if an absolute time is within an

anchored varying duration (see Section 3). Protégé

(Knublauch, 2004) (http://protege.stanford.edu/) is a

HEALTHINF 2008 - International Conference on Health Informatics

software tool that supports the specification and

maintenance of terminologies, ontologies and

knowledge-bases in OWL. It has a plug-in called

SWRL Tab (O’Connor et al., 2005), an editor for

SWRL rules. We used Protégé to create the

ontologies in OWL and SWRL. We then encoded

specific protocols using Protégé’s knowledge-

acquisition facilities. The data generated from the

implementation and execution of clinical trials is

stored in a relational database. The types of data

include participant enrollment data, specimen

shipping and receiving logs, participant visits and

activities, and clinical results. We have implemented

a dynamic OWL-to-relational mapping method and

have used SWRL to provide a high-level query

language that uses this mapping methodology. A

schema ontology describes the schema of an

arbitrary relational database. A mapping ontology

describes the mapping of data stored in tables in a

relational database to entities in an OWL ontology.

A mapping software uses the data source and

mapping ontologies to dynamically map data to

entities in the clinical trial data ontology. A detailed

description of the mapping techniques can be found

elsewhere in the literature (O’Connor et al., 2007).

We are currently using JESS

(http://www.jessrules.com/), a production rule-

engine, to selectively execute the SWRL rules based

on the context. For example, the rule that specifies

the constraint on a visit time window will alone need

to be executed when checking if a specific

participant’s visit satisfied the constraint. Thus, a

temporal constraint is defined first using the

temporal constraint ontology, then is formulated as a

rule, finally, is reasoned with real clinical data using

dynamic mappings.

6 RELATED WORK

Over the years, many expressive models have been

developed to represent temporal constraints (Bettini

et al., 2002), (Combi, 2004), (Terenziani, 2002),

(Duftschmid, 2002). Shahar’s approach (Shahar,

1996) identifies temporal abstractions of data and

properties using interpolation-based techniques and

knowledge-based reasoning. In recent years, there

have been a number of initiatives to create clinical

trial protocol models that encapsulate clinical trial

activities and associated temporal constraints found

in a protocol. These ontologies are then used to

automate different clinical trial management

activities such as eligibility determination,

participant tracking, and site management. The

ontologies can also be used when subsequently

analyzing the clinical trial data.

In the past few years, we have seen

considerable interest in building knowledge-based

systems that automate clinical trial protocols and

clinical practice guidelines. Our Epoch framework

employs a task-based paradigm that combines an

explicit representation of the clinical trial domain

with rules that capture the logical conditions and

temporal constraints found in the trial management

process. There have been a number of proposals on

task-based clinical guideline representation formats

– EON (Musen et al., 1996), PROforma (Fox et al.,

1996), GLIF (Boxwala et al., 2004), etc. that deal

with temporal constraints on patient data and on

activities found in clinical guidelines.

In the area of clinical trials, several modelling

efforts have addressed different requirements of trial

management activities. An ontology to represent

temporal information and cyclical event patterns in

clinical trial protocols has been proposed by (Weng

et al., 2002). The Trial Bank Project (Sim et al.,

2003) is a trial registry that uses a protocol ontology

to capture information on randomized clinical trials

such as intervention, outcomes, and eligibility

criteria. The underlying knowledge base can support

systematic reviewing and evidence-based practice.

There is an ongoing effort by CDISC

(http://www.cdisc.org/), an industry-lead,

multidisciplinary organization, to develop and

support the electronic acquisition, exchange,

submission and archiving of clinical trials data. As

part of this effort, CDISC is developing the

Structured Protocol Representation that identifies

standard elements of a clinical trial protocol that can

be codified to facilitate the data interchange among

systems and stakeholders including regulatory

authorities, biopharmaceutical industry, statisticians,

project managers, etc. A parallel effort is the BRIDG

(Weng et al., 2007) project, a partnership of several

organizations including CDISC, the HL7

(http://www.hl7.org/) standards body, the National

Cancer Institute and the Federal Drug

Administration, that consumes the Trial Design

Model work to build a comprehensive domain

analysis model representing protocol-driven

biomedical/clinical research. The BRIDG model is a

work in progress to elaborately define functions and

behaviors throughout clinical trials, and uses the

Unified Modeling Language (UML) for

representation. The model, in its current state, lacks

formalization of and reasoning with temporal

constraints, and thus, cannot fully support the

requirements of ITN’s clinical trial management.

AN ONTOLOGICAL APPROACH TO REPRESENTING AND REASONING WITH TEMPORAL CONSTRAINTS IN

CLINICAL TRIAL PROTOCOLS

7 DISCUSSION

The increasing complexity of clinical trials has

generated an enormous requirement for knowledge

and information management at all stages of the

trials – planning, specification, implementation, and

analysis. Our focus is currently on two application

areas: 1. tracking participants of the trial as they

advance through the studies, and 2. tracking clinical

specimens as they are processed at the trial

laboratories. The core of the Epoch framework is a

suite of ontologies that encodes knowledge about the

clinical trial domain that is relevant to trial

management activities. This focus on just supporting

trial management activities is also reflected in our

approach to temporal constraint reasoning. Thus, in

the temporal constraint ontology and in our

reasoning approach with rules, we have limited

ourselves to the types of temporal constraints, to the

complexity of formalism and to the levels of

reasoning to just support the clinical trial

management activities. For example, we do not

support checking temporal constraints for

consistency. We continue to work on the temporal

constraints ontology to support newer and more

complex constraints. With any complex constraint,

one concern is the power, or lack thereof, of our

reasoning approach with SWRL rules,

Since we use OWL ontologies and SWRL rules,

native RDF Store (storing data as RDF triples)

would have been a natural solution for storing

clinical trial data, and then seamlessly operate on the

data using our ontologies and rules. ITN uses a

legacy relational database system to store clinical

trial data, and therefore, prevents us from using

native RDF Stores as our backend. We have built

techniques to map the database tables to our virtual

trial data ontology OWL classes. With these

solutions, our data model remains flexible and

independent of the structure of the data sources. We

are yet to undertake a thorough evaluation of our

dynamic mapping methodology especially in the

area of scalability

An often over-looked aspect of knowledge-based

reasoning approaches is the task of knowledge-

acquisition. Currently, we use the Protégé-OWL

editor to build the Epoch models. Based on the class

and property definitions, Protégé automatically

generates graphical user interface (GUI) forms that

can be used to create instances of these classes

(OWL individuals). Thus, domain specialists can use

to enter a specification of a protocol, say for a

transplant clinical trial, using these Protégé-

generated forms. Unfortunately, domain specialists

find it cumbersome and non-intuitive to use the

generic user interfaces as they are exposed to the

complexities of the Epoch ontologies, the OWL

expressions and the SWRL rules. We are building

custom graphical user interfaces that hide the

complexities of the knowledge models, and that

facilitate guided knowledge-acquisition. Providing a

friendly user interface to enter SWRL rules can be

challenging.

The knowledge requirements borne out of the

need for managing clinical trials align well with the

touted strengths of semantic web technologies –

uniform domain-specific semantics, flexible

information models, and inference technology.

Using these technologies, we have built a

knowledge-based framework for temporal

constraints reasoning that is, above all, practical.

ACKNOWLEDGEMENTS

This work was supported in part by the Immune

Tolerance Network, which is funded by the National

Institutes of Health under Grant NO1-AI-15416.

REFERENCES

Allen, J.F., 1993. Maintaining knowledge about temporal

intervals. Communications of the ACM, 26(11): 832-

843.

Bettini, C., Jajodia, S., Wang, X., 2002. Solving multi-

granularity constraint networks. Artificial Intelligence,

140(1-2):107-152.

Boxwala, A.A., Peleg, M., Tu, S. W., Ogunyemi, O.,

Zeng, Q. T., Wang, D., Patel, V. L., Greenes, R. A.,

Shortliffe, E. H. 2004. GLIF3: A Representation

Format for Sharable Computer-Interpretable Clinical

Practice. Journal of Biomedical Informatics,

37(3):147-161.

Combi, C., Franceschet, M., and Peron, A, 2004.

Representing and Reasoning about Temporal

Granularities. Journal of Logic and Computation,

14(1):51-77.

Duftschmid, G., Miksch, S., Gall, W. 2002. Verification of

temporal scheduling constraints in clinical practice

guidelines. Artificial Intelligence in Medicine 25(2):

93-121.

Fox, J., Johns, N., Rahmanzadeh, A., Thomson, R. 1996.

PROfarma: A method and language for specifying

clinical guidelines and protocols. Proceedings of

Medical Informatics Europe.

Johnson, P.D., Tu, S. W., Musen, M. A., Purves, I., 2001.

A Virtual Medical Record for Guideline-Based

Decision Support. Proceedings of the 2001 AMIA

Annual Symposium, 294-298.

HEALTHINF 2008 - International Conference on Health Informatics

Knublauch, H. Fergerson, R.W., Noy, N.F. and Musen,

M.A., 2004. The Protégé OWL Plugin: An Open

Development Environment for Semantic Web

applications. Proceedings of the Third International

Semantic Web Conference. 229-243.

Musen, M.A., Tu, S.W., Das, A.K., Shahar, Y. 1996.

EON: A component-based approach to automation of

protocol-directed therapy, Journal of the American

Medical Informatics Association, 3(6): 367–388.

O'Connor, M.J., Knublauch, H., Tu, S.W., Grossof, B.,

Dean, M., Grosso, W.E., Musen, M.A., 2005,

Supporting Rule System Interoperability on the

Semantic Web with SWRL. Proceedings of the Fourth

International Semantic Web Conference. 974-986.

O'Connor, M.J., Shankar, R.D. Das, A.K., 2006. An

Ontology-Driven Mediator for Querying Time-

Oriented Biomedical Data. Proceedings of the19th

IEEE International Symposium on Computer-Based

Medical Systems. 264-269.

O'Connor, M.J., Shankar, R.D. Tu, S.W., Nyulas, C.,

Musen, M.A., Das, A.K., 2007. Using Semantic Web

Techonologies for Knowledge-Driven Queries in

Clinical Trials. Proceedings of the 11th Conference on

Artificial Intelligence in Medicine.

Rotrosen, D., Matthews, J.B., Bluestone, J.A., 2002. The

Immune Tolerance Network: a New Paradigm for

Developing Tolerance-Inducing Therapies. Journal of

Allergy and Clinical Immunology, 110(1):17-23.

Shahar Y., Musen, M.A.. 1996. Knowledge-Based

Temporal Abstraction in Clinical Domains. Artificial

Intelligence in Medicine,8:267-298.

Shankar, R.D., Martins, S.B., O’Connor, M.J., Parrish,

D.B., Das, A.K. 2006. Epoch: an ontological

framework to support clinical trials management.

Proceedings of the International Workshop on Health

Information and Knowledge Management, 25–32.

Shankar, R.D., Martins, S.B., O'Connor, M.J., Parrish,

D.B., Das, A.K., 2006. Towards Semantic

Interoperability in a Clinical Trials Management

System. Proceedings of the Fifth International

Semantic Web Conference. 901-912.

Sim, I., Olasov, B., Carini, S. 2003. The Trial Bank

system: capturing randomized trials for evidence-

based medicine. Proceedings of the American Medical

Informatics Association Fall Symposium. 1076.

Terenziani, P., 2002. Toward a Unifying Ontology

Dealing with Both User-Defined Periodicity and

Temporal Constraints About Repeated Events,

Computational Intelligence 18(3):336-385.

Weng, C., Kahn, M., Gennari, J.H. 2002. Temporal

Knowledge Representation for Scheduling Tasks in

Clinical Trial Protocols. Proceedings of the American

Medical Informatics Association Fall Symposium. 879

– 883.

Weng, C., Gennari, J.H., Fridsma, D.B. 2007. User-

centered semantic harmonization; A case study.

Journal of Biomedical Informatics 40: 353–364.

AN ONTOLOGICAL APPROACH TO REPRESENTING AND REASONING WITH TEMPORAL CONSTRAINTS IN

CLINICAL TRIAL PROTOCOLS