=Paper=
{{Paper
|id=None
|storemode=property
|title=TiEE – The Telemedical ILOG Event Engine: Optimizing Information Supply in Telemedicine
|pdfUrl=https://ceur-ws.org/Vol-1028/paper-03.pdf
|volume=Vol-1028
|dblpUrl=https://dblp.org/rec/conf/bir/Meister13
}}
==TiEE – The Telemedical ILOG Event Engine: Optimizing Information Supply in Telemedicine==
https://ceur-ws.org/Vol-1028/paper-03.pdf
TiEE – The Telemedical ILOG Event Engine:
Optimization of Information Supply in Telemedicine
Sven Meister1,*, Sven Schafer1, Valentin Stahlmann1
1
Fraunhofer Institute for Software and Systems Engineering, Dortmund, Germany
{sven.meister,sven.schafer,valentin.stahlmann}@isst.fraunhofer.de
Abstract. Ongoing problems in healthcare supply make it imperative to estab-
lish telemedicine as a feasible concept to ensure the quality of medical treat-
ments and reduce costs. With TiEE, the Telemedical ILOG Event Engine,
Fraunhofer ISST fosters the research to process streams of vital signs in tele-
medical scenarios using Complex Event Processing (CEP) in an Information
Logistics (ILOG) manner. ILOG means to reduce the amount of information by
preprocessing existing data and to route high-grade decision at the right time to
the right person. As basic building blocks we developed the concepts of tele-
medical events (TE), Telemedical ILOG Listener (TIL) and TIL-Profiles on top
of the event processing engine Esper [20–23]. TiEE analyzes the incoming pa-
tient specific streams of telemedical events and tries to detect relevant trend pat-
terns. In a second step these got aggregated to higher level decisions.
Keywords: CEP, Telemedicine, Decision Support, Trend Recognition
1 Introduction
The continuous measuring of vital signs results in an unmanageable amount of data
and furthermore information that could be deduced. Physicians therefore complain
about the problem of information overload, which means that the information pro-
cessing requirements exceed the information processing abilities [12]. Investigating
solutions for solving the problem of information overload in telemedical scenarios at
first means to reduce the amount (quantity) of data. Second, the time for acquiring
information has to be reduced by distributing the right information, at the right time to
the right place.
To cope with the problem of information overload in telemedical scenarios of con-
tinuous monitoring of vital signs we investigated the Telemedical ILOG Event Engine
(TiEE). TiEE is based upon scientific outcomes of the following two research areas:
Information Logistics (ILOG) and Complex Event Processing (CEP). The former is
always related to the metaphor of transporting the right information at the right time
to the right place [6, 14]. The latter enables one to process so called events in real-
time by filtering, aggregating and transforming them into more complex events. Why
is CEP an appropriate technology for ILOG processing in telemedicine? Every vital
sign monitored by a telemedical application is some kind of event. Such an event
could be related to additional information like the time of generation or the value of
the vital sign itself. By analyzing all data and information related to such an event,
under consideration of the history and order, we are able to reduce the amount of
irrelevant vital signs. Furthermore we can aggregate events to higher order decisions,
so called complex events.
Thus, the purpose of the paper is to show how information demand can be mapped
on concepts of event processing to optimize information supply in telemedical scenar-
ios by reducing the amount of information overload. In the following we will give an
overview of the state of the art in ILOG and CEP. Furthermore we’ll discuss the basic
concepts we investigated to implement TiEE, which are telemedical events, Telemed-
ical ILOG Listener (TIL) and Telemedical ILOG Listener Profiles (TIL-Profile). At
the end we’ll show first evaluation results based upon two different use cases.
2 Related Work
TiEE is focused on the two research areas complex event processing and information
logistics to foster a fast, on-time processing of telemedical values. Basic definitions
and concepts of CEP were developed and defined by Luckham, Chandy and Bates [2,
3, 18]. Citing them, an event is „an object that is a record of an activity in a system“.
A detailed overview about open questions and the current state of research in CEP is
discussed in the Dagstuhl Seminar on „Event Processing“ in 2010 [4]. In [17, 25]
Lowe and Weber present ongoing work on applying CEP to health data using the
event processing engine Esper in the context of the Stride project „Stanford Transla-
tional Research Integrated Database Environment”. One basic open question is how to
cope with the problem of heterogeneity of medical data to achieve an overall pro-
cessing.
The need for information logistics is caused by an increasing number of situations
of information overload. According to Wilson [27, 28] information overload express-
es „that the flow of information […] is greater than can be managed effectively“.
ILOG is viewed as a research area to deliver the right information, in the right format
at the right time to the right place and is partially used for information filtering or
with context-models to optimize communication in the healthcare domain [7, 15, 26].
A broad overview of the state-of-the art research in information logistics is given by
Haftor et al. [9] by analyzing 102 scientific publications. The link between CEP and
ILOG is given by Chandy [4] by mentioning that „Disseminating and distributing is
also about getting the right information to the right consumers at the right time.“.
Alternative approaches to the usage of CEP and ILOG could be found in the re-
search area of data streams. They could be used for real-time processing of data like
shown within STREAM [1]. Geesen [8] gives an introduction how data streams could
be used to process high frequent data in the area of Ambient Assisted Living. All
approaches cope with the problem that data or information isn’t standardized, devel-
oped concepts are not that modular and they don’t take ILOG into account. Therefore
many papers in both research areas give an outlook on using events and event based
processing for real time data optimization.
3 TiEE – The Telemedical ILOG Event Engine
The reduction of information overload in telemedical scenarios requires to process
telemedical information in the sense of aggregation, filtering as well as analysis of
causal and temporal relationships. There are three basic requirements for the pro-
cessing of vital signs in telemedical scenarios one should take into account [21]:
Sensors for measuring vital signs act in a highly distributed manner. Every type,
e.g. blood pressure or blood sugar concentration, is a result of a single sensor or
telemedical application. The prospective solution should be able to process tele-
medical values from different sources to achieve an overall monitoring and deci-
sion making. This requires an overall description of a telemedical value, in sense of
a vital sign, as well as methods to modular process different types of telemedical
values depending on the actual medical situation of a patient.
Monitoring of vital signs in telemedical scenarios produces a high amount of data
which has to be processed in real-time. Not every single vital sign represents an
important medical situation. The relevance depends upon the temporal ordering as
well as the coincidence of different types of vital signs. So, a solution has to aggre-
gate a set of those to higher order decisions.
The delivery of those decisions mentioned above should be done in an intelligent
way. So, a derived decision should be transported at the right time to the right
place, e.g. prior to a physician a notification should be emitted to a family member.
With TiEE, the Telemedical ILOG Event Engine, Fraunhofer ISST investigated
methods for event based processing of vital signs in telemedical scenarios considering
the requirements mentioned above. We’ll start by giving a broad introduction to the
information logistics processing within TiEE. Afterwards we’ll describe the three
basic concepts telemedical event, TIL and TIL-Profile in more detail.
3.1 Information Logistics Processing
We started our discussion about information overload in telemedical scenarios by
stating that it is not possible for a physician to deduce relevant information out of a
stream of thousands of vital signs. Thus, realizing a support for clinical decision mak-
ing one has to reduce the amount of data and deduce higher level, relevant infor-
mation. Delivered relevant information should fulfill a given information demand,
thus a physician needs a possibility to express its demand.
With TiEE we investigated a graphical demand modeling approach (Demand Mod-
eling Language) upon the Clinical Algorithm Standard (CAS) [24] which is well
known in medicine. Within CAS we have five elements (see Fig. 1): a start node
(oval), a condition (rhombus), an activity (rectangle), a terminal node (circle) and a
connector (arc). A condition is the evaluation of a given expression, like pulse higher
than 150. The logical AND concatenation is modeled by writing conditions from left
to right and the OR concatenation is realized by writing conditions down. The result
of the evaluation of a set of conditions could be the terminal node, thus nothing will
happen, or a red or green colored activity. An activity symbols the necessity to inform
about a relevant situation. Relevance is divided into critical (red) and uncritical
(green) situations.
Measure of Measure of
blood pressure yes pulse yes c1 yes a1
Start: Patient with
obesity c2 yes a2
Measure of
weight yes c3 yes c4 yes a4
a3
Fig. 1. Demand modeling using the Clinical Algorithm Standard.
Regarding the figure shown above we have a patient coping with obesity. He has to
measure weight, pulse and blood pressure. Condition c2 is only valid in case that the
pulse isn’t measured otherwise condition c1 is valid which would lead to action a1 or
a terminal node.
The graphical model has to be transformed into processable data structures, thus
we defined a set of structures upon the Extended Backus-Naur Form (EBNF) in such
a way that the Demand Modeling Languages ⊂ is a subset of an Event
Processing Language and further can be mapped to the extending concepts like TILs
and TIL-Profiles. Below we sketched up the EBNF specification of the formalize
demand and afterwards in Table 1 examples according to Fig. 1.
demand = “DEMAND” (condition | condition “” condition) CRLF ;
“ACTION” activity “ELSE“ activity “;“ CRLF ;
Table 1. Expressions for condition c1, action a1 and the demand d1 according to the example
shown in Fig. 1.
ID Expression
c1 bloodpressure.BP1 = INCREASING AFTER pulse.BP1 = INCREASING
a1 INFORMATION Critical increase of blood pressure and pulse. Please stay in contact with the
patient.;
RELEVANCE 80;
CTITICALITY 90;
RECIPIENT ID1 | Servicemitarbeiter | JMS | service_in | DIRECT;
D1 DEMAND c1 ACTION a1 ELSE NIL;
Now, the above expressed and formalized demand has to be mapped on the concepts
of TILs and TIL-Profiles bearing the Event Processing Language used within TiEE in
mind. At first, an incoming telemedical event will be processed by a patient-specific
TIL-Profile, like described in chapter 3.4. Afterwards a set of TILs is trying to detect
characteristic patterns within the stream of telemedical events, so called Complex
Trend Pattern Events (CTPE). A CTPE is an abstraction of a set of telemedical events
(see also Fig. 2) and characterizes the progression of the measured values. Based
upon the research of Charbon et al. [5, 10] we distinguish five basic types of trend
pattern: slope, slope reverse, saltus up and saltus down steady.
pulse
weight
Fig. 2. Abstraction of incoming telemedical events by building intervals in Terms of CTPEs.
Thus, we derive an abstraction, the pattern, from a set of underlying measurements to
reduce the amount of data and cope with the problem of information overload. The
derived pattern is described using different types of parameters, e.g. the statistical
spread or the amount of increase/decrease. As a basic feature of the TIL concept one
can use any kind of algorithm for trend calculation as long as it fulfills the formal
definitions of a TIL. CUSUM (cumulative sum) or ARIMA (autoregressive integrated
moving average) based approaches are examples for processing time related data.
In summary, every TIL derives CTPEs for a specific telemedical event, i.e. a type
of vital signs, and forwards them up to the referring TIL-Profile. Now, this TIL-
Profile has to detect higher order, demand fulfilling patterns within the set of for-
warded CTPEs. By using the formalized demand the processing within a TIL-Profile
is organized as follows:
Trends of same underlying types of vital signs: The repeated increase, decrease etc.
of a set of vital signs could be abstracted to a single trend pattern.
Trends of different underlying types of vital signs: It is obvious that there is a rela-
tion between weight and blood pressure in cases of cardiac decompensation. A
TIL-profile has to detect the increase of both during a given time window and de-
rive a new abstraction, emitting a new trend pattern.
Upon rules registered in the TIL-Profile, information logistics decisions are made to
generate and send relevant information to a person.
3.2 Telemedical event and HL7 Telemedical Event Format
Within telemedical scenarios you’ll find a lot of different sensors from various manu-
facturers to measure vital signs. The overall processing of vital signs using TiEE re-
quires some concept to standardize the input. While TiEE is based upon the idea of
complex event processing, every vital sign should be interpreted as an event. There-
fore we defined the term telemedical event as a measurement of a telemedical value
and an instance of a telemedical event type, formatted in the HL7 Telemedical Event
Format [20]. The HL7 Telemedical Event Format is a message format we investigat-
ed to achieve an interoperable transportation of Telemedical Events. The refinement
of this format is done by combining elements of the HL7 standard with such from the
IEEE 11073 standards. HL7 is a widespread international standard for data exchange
in the healthcare sector [11]. All HL7 V3 data types are based upon the HL7 Refer-
ence Information Model (RIM). In turn IEEE 11073 is a family of standards to har-
monize the output of sensors using the IEEE 11073 Domain Information Modell
(DIM) [13]. Using both standards we modeled a format that takes all attributes for
complex event processing and ILOG processing of vital signs into account.
Formally a telemedical event is an n-tuple ∶= ( , 7 ) where:
∶= ⋃ is the set of all telemedical events.
∈ is an event based upon a telemedical event type ∈ .
7 : → is a transformation into the HL7 Telemedical Event Format.
Two telemedical events are identical ≡ if and only if =
and 7 ( )= 7 . 7 is a function to transform a given
event into the HL7 Telemedical Event Format.
3.3 Telemedical ILOG Listener (TIL)
A TIL-Profile realizes a patient specific filtering of the incoming telemedical events.
The second step of filtering the high amount of events is done within the concept of a
TIL. Related to CEP a TIL is some kind of Event Processing Agent, specialized for
processing one type of telemedical values e.g. blood pressure events [23].
Besides the operation of filtering, a TIL encapsulates methods to detect patterns of
interests in the stream of incoming events. Therefore different types of rules could be
instantiated. Thus, a TIL is a modular piece of concept to encapsulate algorithms
which are highly specialized to process one type of vital sign but is not specialized to
a patient. That enables an easy reuse.
Formally a Telemedical ILOG Listener is defined as an n-tuple as follows
:= ( , , , ):
∶= ⋃ is the set of all TILs.
etin: The event type on which all instances ( )= are based upon. Initial-
ly this is the telemedical event type ∶= .
ETout: Analogous to the definition of etin, ETout is a set of event types | |≥1
which are permitted to be emitted as output.
fin: The filter function is based upon a boolean function → ∶ ( , ).
Given to functions and it is imperative that ≡ are identical if and
only if ( )= ( ) that is, both functions relate to the
same type of vital sign. Two mutually different functions ⊓ = ∅ are dis-
joint.
VL: Every TIL consist of processing logic VL which is a set of rules in terms of:
: →( , ),
∶= | = : → ,
: → ,
Two TILs are identical ≡ if and only if ≡ , ≡ ,
≡ and ≡ .
3.4 Telemedical ILOG Listener –Profile (TIL-Profile)
Every medical situation represents an individual moment in lifetime. Thus, TiEE has
to offer functionalities for a patient specific processing of incoming telemedical
events, which very fast can be adapted to a new situation. Therefore we investigated
the term TIL-Profile. A TIL-Profile realizes a patient specific filtering of telemedical
events thus it reduces the amount of data. Afterwards the event is processed, depend-
ing on the type of vital sign, by one of the TIL’s (see section 3.3) connected to this
profile. For every type of vital sign one has to register one TIL. In the following the
output of the TIL’s is processed within the TIL-Profile by additional filtering, pattern
detection and transformation into higher order decisions. That means that a TIL-
Profile correlates the progression of different types of vital signs, e.g. blood pressure
and weight, detects a medical situation of relevance and derives a higher order medi-
cal decision.
Formally a Telemedical ILOG Listener Profile is defined as an n-tuple as follows
:=( , , , , ):
∶= ⋃ set of all TIL-Profiles.
etin: Thhe event type on which all iinstances ( )= are based upoon. Initial-
ly this is the telemeddical event typpe ∶= .
ETout: Analogous to o the definitionn of etin, ETouut is a set of event
e types | |≥1
which are permitted d to be emittedd as output.
fin: The filter functioon is based uppon a boolean n function → ∶ ( , ).
Given to functions and it is imperativve that ≡ are identiical if and
only iff ( )= ( ) that is, both functiions relate to the same
patientt. Two mutuallly different fuunctions ⊓ = ∅ arre disjoint.
TIL: The
T set of reg gistered TIL’ss ∶= ,⋯, in the TIL-Proffile where
| | ≥ 1, so at leasst one TIL hass to be registeered.
VL: Every
E TIL-Proofile consist oof processing logic VL which is a set oof rules in
terms of:
o
: →( , ),
∶= | = : → ,
: → ,
L-Profiles are identical
Two TIL ≡ if and only if ≡ ,
≡ , ≡ , ≡ and
d ≡ .
3.5 Arrchitectural insights
i into T
TiEE
nt processing engine Esperr which is
The architecture of TiiEE is based uupon the even
commonlly used in maany commerciial products. Fig.
F 3 gives a broad overvview about
the main components and
a concepts llike described
d in the past seections.
Fig. 3. TiEE archittectural overvieew based upon the
t event processing engine Essper.
Starting at the bottom we have some kind of vital sign sensors from different man-
ufactures. The sensors are connected to TiEE through Bluetooth HDP, supporting the
IEEE 11073 standards family. To achieve an overall processing a single vital sign is
interpreted as a telemedical event and will be encapsulated in the HL7 Telemedical
Event Format. All events get transported to the event channel ordered by time. Above
the channel we build the engine, introducing TIL-Profiles and TIL using Esper core
and Esper queries of the Esper engine.
To realize a patient specific filtering like required by the TIL-Profile we extended
Esper with the concept of PES, a patient-individual event stream. A PES formally
represents an individual event stream that supports and bundles all types of events of
one single patient. Thus there is a bijection between the patient and PES. According-
ly, any PES are pairwise different, in other words all PES are distinct, as each PES is
characterized by a different patient, ≠ ∀ , where ≠ . The con-
cept of a PES is based on the Variant Stream of Esper. All events of one single patient
will be redirected in separate PES, with the help of a filter criterion, which checks for
an individual patient identification. Likewise, a PES serves as the event source for all
TILs in one TIL-Profile.
Furthermore we implemented methods and services to administrate TiEE. The de-
veloped services serve the purpose to modify the current system afterwards. So it is
possible to add or remove main components like TIL and TIL-Profiles. In more detail
the services allow to add, modify and remove patients, TILs and statements. Also, the
services provide information about the components within the system. All services are
designed as REST services and work with JSON objects.
4 Evaluation
The evaluation of TiEE is done upon two different data sets gained from two different
projects. The first project is FitPit, the Fitness Cockpit, a solution to optimize preven-
tive and rehabilitative trainings developed at Fraunhofer ISST [19]. Pulse and oxygen
saturation as well as weight and blood pressure will be measured at the beginning and
at the end of the training. In total we recorded 2450 measurements gained from 10
patients in terms of a long term measurement. The second project is the Vital Signs
Dataset of the University of Queensland [16]. They recorded over 10 vital sign pa-
rameters from 32 patients undergoing anesthesia. The data was recorded with a solu-
tion of 10ms. Thus, in total we have around 240.000 measurements per patient, de-
pending on the duration of the surgery. Now, before we can apply the datasets above,
we have to define the main questions that have to be evaluated using TiEE:
Question 1: Is TiEE capable of reducing the amount of incoming data and
process them according to the defined information demand?
Question 2: Is TiEE capable to process long-term trends as well as high fre-
quent data?
Question 3: Does the usage of TiEE reduce the overall implementation ef-
forts for an analytical infrastructure?
What we won’t try to answer at thiss point is, if TiEE
T is capablle of being beetter in de-
cision maaking compared to a physiccian. To answ wer the first qu uestion we staarted with
modelingg the informattion demand using CAS liike mentioned d before. Thuus, we can
prove thaat it is possiblle to map somme kind of forrmalized demaand to processsing rules.
To show the amount of data reducction we calcu ulated the perrcentage of C CTPEs per
Minute. Within
W the FiitPit scenario we had 1/115 520 CTPEs per minute, reespectively
one trendd within eight days. This veery low value is i caused by the long-term character-
n Fig. 4 we shhow the ratio of incomings events and firred activi-
istic of thhe use case. In
ties. Thuss, there is a reecognizable reeduction of daata or irrelevaant informatioon because
activities are only firedd according too a formalized information demand.
d
12
200000
10
000000
amount of events
8
800000
6
600000
1035381
4
400000
2
200000
0 1512
1 187
#Events #C
CTPE #Activityy
Fig. 4. Ratio between in
ncoming events,, generated CTPPEs and fired activities of one patient of
the Universsity of Queenslaand data set.
The highh-frequent dataa of the seconnd use case produces 8.3 CTPEs
C per miinute. It is
possible to
t optimize orr modify the ppercentage off CTPEs by co onfiguring thee underly-
ing algorrithm. Thus, we
w can also shhow that TiEE E is capable to
t process lonng-term as
well as high-frequent
h data. We alsso evaluated the performance of TiEE including
a algorithmiic processing of the incom
routing and ming events. The
T average dduration is
around 155ms per event. The third qquestion is imp plicitly proven
n by the usagge of TiEE
within tw
wo different sccenarios. We were able to reuse
r once deefined TILs wwithin both
scenarioss in terms of a repository. T
Thus, the only
y effort was to
o model the innformation
demand and
a map it to a TIL-Profilee. Furthermoree with TiEE a basic infrastrructure for
communiication I/O is already
a given . Summarizedd we can pointt out that TiEEE supports
an optimiization of info
ormation suppply by reducingg the amount of data.
5 Conclusion and
a Outloook
With TiE
EE, the Telem
medical ILOG Event Enginee, we investig gated a solutioon to cope
with one main problem
m in telemediccal scenarios: information overload.
o Espeecially the
continuous measuring of vital signs requires an intelligent reduction and processing of
data in real-time. Since a medical situation changes very often over time, TiEE uses
TIL-Profiles and TILs as a modular concept to facilitate the reuse of once developed
rules and algorithms. Like described above, trend detection is an important class of
algorithms for pattern recognition in streams of vital signs. At the moment TiEE is
based upon trivial calculations using the CUSUM method to detect critical increasing,
decreasing and stagnation of them. To show the technical feasibilities of TiEE we
executed a first evaluation of the conceptional and implementational insights. TiEE
need around 15ms per event and is capable to reduce the amount of date by generating
CTPEs. In the future we’ll start to evaluate also the medical evidence of TiEE.
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