KNOWLEDGE DISCOVERY FOR PERVASIVE AND

REAL-TIME INTELLIGENT DECISION SUPPORT

IN INTENSIVE CARE MEDICINE

Filipe Portela

, Pedro Gago

, Manuel Filipe Santos

, Alvaro Silva

, Fernando Rua

José Machado

, António Abelha

and José Neves

Centro Algoritmi, Universidade do Minho, Guimarães, Portugal

Instituto Politécnico de Leiria, Leiria, Portugal

Unidade de Cuidados Intesnivos, Centro Hospitalar do Porto, Porto, Portugal

Departamento de Informática,Universidade do Minho, Azurem, Guimarães, Portugal

Keywords: Pervasive, Decision support process, Real-time, Clinical data, Data acquisition, Intensive care medicine.

Abstract: Pervasiveness, real-time and online processing are important requirements included in the researchers’

agenda for the development of future generation of Intelligent Decision Support Systems (IDSS). In

particular, knowledge discovery based IDSS operating in critical environments such of intensive care,

should be adapted to those new requests. This paper introduces the way how INTCare, an IDSS developed

in the intensive care unit of the Centro Hospitalar do Porto, will accommodate the new functionalities.

Solutions are proposed for the most important constraints, e.g., paper based data, missing values, values out-

of-range, data integration, data quality. The benefits and limitations of the approach are discussed.

1 INTRODUCTION

In the future, the decision-making process and the

form how the people make decisions should take

into account new requirements like pervasiveness,

real-time and online processing. This paper explores

how these requirements can be integrated in the

critical health care arena in order to improve the

decision process. The work is part of a major

project: INTCare - an IDSS for intensive care units.

The Intensive Care Units (ICU) are considered

critical units where each decision needs to be correct

and performed very carefully. The existence of a

high number of data sources difficult, the data

dissemination and the decision making process by

the doctor. In the ICUs exist many electronic

platforms with patient information, systems which

collect vital signs and other systems that contains lab

results, medical proceedings, etc., however still exist

much information that were manually registered in

the paper format. This is a common problem on the

ICUs and is essentially associated to the nursing

records (Lyerla et al., 2010). The creation of

intelligent agents (De Turck et al., 2007);

(Wooldridge, 1999) allow, perform some automatic

tasks, continuously and in real-time. However the

process automation has some restrictions, due to

problems that arise during tasks execution like

incorrect values, null values, missing identification

and others. In order to resolve these problems we

develop a platform for monitoring the patient data

electronically with a total control of values by the

humans. The main goal of this paper is to present

architecture to ensure that all the clinical data that

normally is used in the decision-making process is

accessible electronically, enabling a pervasive and

real-time approach. Beyond this chapter, a

background will be presented on the ICU, the data

acquisition process and the major problems detected.

Chapter IV presents the platform developed in order

obtain the decision variables in real-time and make

them available in electronic and in online mode.

Chapter V is dedicated to the data quality, and

presents some forms of data processing to increase

the data quality. Chapter VI makes a brief

presentation of the intelligent agents that were used

in the KD process. Finally, the results related to the

data quality will be presented and discussed.

Conclusions and future work cease the paper.

241

Portela F., Gago P., Santos M., Silva A., Rua F., Machado J., Abelha A. and Neves J..

KNOWLEDGE DISCOVERY FOR PERVASIVE AND REAL-TIME INTELLIGENT DECISION SUPPORT IN INTENSIVE CARE MEDICINE.

DOI: 10.5220/0003677002410249

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2011), pages 241-249

ISBN: 978-989-8425-81-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 BACKGROUND

2.1 Intensive Medicine Environment

Intensive Medicine (IM) is a critical area where

anything can fail, because the itensivists are dealing

with human lives in particularly and weakened

conditions. ICU uses IM to treat their patients and is

a critical environment because have some complex

health care situations (Bricon-Souf and Newman,

2007), the activities occurring in it are sometimes

adverse, dangerous and tiring the various organ

systems of the patient may be affected at the same

time (Apostolakos and Papadakos, 2001) and this is

a challenging for the that operate in this

environment.

2.2 INTCare

INTCare is an IDSS that was developed to Intensive

Medicine, with the main goal predicts the organ

failure and outcome (Gago et al., 2006); (Manuel

Filipe Santos et al., 2011) in real time (Portela et al.,

2010). The system is being concluded and is been

tested in the ICU of the Hospital Santo António

(HSA) in Porto. The INTCare system is divided into

four subsystems: data acquisition, knowledge

management, inference and interface (Portela et al.,

2010) and uses intelligent agents to, for example,

automate the collection, processing and

transformation of data, and update the predictive

models in real-time, without the human intervention.

2.2.1 System Features

The number of INTCare features has been increasing

according new problems or situation that we find

and with the objective to create a most complete

system possible. In order to complete the KDD

process, the system attends some requirements

(Manuel Filipe Santos and Portela, 2011): Online

Learning; Real-Time; Adaptability; Data mining

models; Decision models; Optimization; Intelligent

agents; Pervasive; Accuracy; Safety; Privacy; Secure

Access from Exterior; User Policy.

2.2.2 System Requirements

In order to accomplish the features defined before,

the system has to have some important requirements

(Portela et al., 2010). These requirements were now

incorporated and are part of KDD process: 1) Fault

tolerance capacities; 2) Remove null and noisy data;

3) Ensure the patient identification; 4) Continuous

data acquisition; 5) Time restrictions for the data

acquisition and storage; 6) Digital data archive in

order to promote the dematerialization of paper

based processes; This paper will explain the

decisions and alterations performed.

2.3 Intelligent Agents

The intelligent agents used by INTCare and are

capable to performs autonomous actions, without the

human interaction, in order to meet its goals (Gago

et al., 2006). To implement this system, use of multi-

agent systems is fundamental, with this is possible

have various agents cooperates in order to manage a

variety of problems (Foster et al., 2005); (Machado,

2006). INTCare uses intelligent agents to perform

some actions that make the system work through

autonomous actions that perform some essential

tasks. These tasks support some system modules:

Data Acquisition, Management of Knowledge,

Inference and Interface. The flexibility and

effectiveness of such systems depend on the agents

and the interactions between them.

2.4 Pervasive Health Care

Pervasive HealthCare (PHC) appears with the

objective to resolve some problems in the Health

Care like the data quality and the data access.

Varshney (U. Varshney, 2007, 2009) defines PHC as

Health for all, anytime and anywhere by removing

restrictions such as location and time, increasing the

coverage and quality of health care. However the

problem is how we can do it? Is it viable in a critical

environment, like is Intensive Care? These questions

arises, for though the PHC have the potential to

reduce costs, improve service quality and facilitate

the treatment of the patients also faces many

technical hurdles and administrative (Upkar

Varshney, 2003) as resistance to change and

significant changes in systems and technologies.

The problem with this type of scenario is that the

information is not always available when it is

required that sometimes prevents the physician to

make the best decision for the patient, this happens

because there also a lot of information in paper.

Solution arises as the possibility of providing all

necessary information electronically and the

creations of decision models that help the doctor

make the best decision in real time.

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3 ICU – DATA ACQUISITION

Data acquisition is a central issue for a pervasive

intelligent decision support in order to enable a way

of work in real-time. A considerable number of

different data sources exist that should be integrated

in the ICU data acquisition process.

3.1 Data Sources

This processed has essentiality in five data sources:

Vital Signs: Contains all vital signs collected by the

sensors connected to the patient. This data are

collect by the bedside monitors, e.g. Blood Pressure,

SPO2, Respiratory and Frequency Rate, etc.

Electronic Health Record (EHR): Has all

information about the patient at admission e.g.

patient identification (PID), age, name, sex,

admission from, admission type, comorbidities, etc.

Drugs System: This system is responsible to control

the patient therapeutic plan. For each patient, it

contains a detailed plan with, the drugs prescription,

dosages, administration dates, etc.

Lab Results: Every day patients make lab analysis

the results will be collected and processed by a

laboratory. It contains all the values for each patient:

oximetry, clinical chemistry, and hematology.

Electronic Nursing Record (ENR): Allow to have

all information about the patient admitted in the ICU

in electronic format e.g. fluid balance, medical

scores, therapeutics plans, events, etc.

3.2 Data Acquisition Process

The Data acquisition process has been modified in

last two years. In the start (2009) more than 80% of

data were register in paper or were not accessible

electronically, of these, only 30% were stored offline

(manually) in database.

Now in 2011 we can have all data in electronic

format, and these, can be registered, validated or

consulted in real-time and automatic or manual

form. After we collect the data, the processing and

transformation process begin; largely of this tasks

will perform automatic by the intelligent agents.

Like we can see in Figure 1 are four, the data

sources that contain information to the knowledge

process, this data will be acquired and processed

according each agent task. Finally this process is

responsible for the processing and transformation

data to the INTCare system according the variables

required to the data mining. The main objective of

autonomous acquisition process is processing the

patient data in the moment that were collected and

validate it according the values defined in ICU.

This operation will increase the data quality

stored in the database, allow the calculation of

critical events and prepares the data to create the

best decision models.

3.3 Data Acquisition Problems

The number of problems associated to automatic

data acquisition process is high, complex and can

compromise all decision making process. These

problems can have different natures, like, humans,

techniques, informatics, environments, etc.

The most common problems are: sensors

disconnected, data collected without PID, missing

values and values with noise and system failures. In

order to mitigate this problems, were defined the

requirements that need to be addicted to the system.

Figure 1: ICU Data Acquisition architecture.

4 NURSING RECORDS - ENR

After made a questionnaire we concluded that

normally the decision is based in the vital-signs

results, the registers presented in the Paper Nursing

Record (PNR). Normally, proceedings, therapeutics,

and events are also used in the decision models;

however some of these data was not available in

real-time. Moreover different platforms and formats

should be considered. The decision process is not

made instantly, this limitation stems from problems

like the data access time, different platforms with the

necessary data and the format how the data are

stored, i.e., the data aren't clear (pdf format, difficult

to analyse the entire hospital stay, dubious values).

Paper based registers, and the acquisition of

wrong values is the main problems of the ICU and is

the biggest obstacle for the creation of prevision and

decision models. Based on the information collected

and on a selection of the most important variables

KNOWLEDGE DISCOVERY FOR PERVASIVE AND REAL-TIME INTELLIGENT DECISION SUPPORT IN

INTENSIVE CARE MEDICINE

243

for the decision making process, a platform was

created to allow the automatic data monitoring. PNR

has been substituted by the ENR. ENR is a touch-

screen system interoperating with all the information

systems used in the ICU. Contains patients’ data and

allows for a higher number of functionalities to

record, store and validate the values collected in the

environment, in an online and real-time way.

4.1 Systems Interoperability

ENR encompasses a lot of data provided from other

systems: EHR, Medical Systems, BM and Lab

Results. This facilitates the nurses and doctor work,

because now they know where is the information

that they need to care the patient. With this option

there are no longer islands of information, passing

all the clinical and patient data to be accessible in

the same space.

4.2 Main Features

Besides the integration some other features are

available with the introduction of ENR in the ICU.

In order to enable the KDD process, the ENR

contains a set of features to streamline the process:

 Collect information about patient process;

 Get correct vital signs values, therapeutic and

drugs plans, Lab Results and medical request;

 Allow manual fluid balance registers, manual

medical and nursing recording and validation of

values and record of adverse events;

 Automatic calculation of medical scores;

 Store all validated data in database.

4.2.1 Patient Clinical Data Logbook

An important benefit in the use of this platform is

the mode the clinical data are collected and

presented for each day. The ENR is based on a grid

divided in 24 hours, presenting for each hour the

values of the selected variables, e.g., vital signs,

drugs and schedules events. All monitoring data that

are scheduled to some variables appear in the ENR

automatically. The data will be collected and

inserted by the agents. this data can be also stored

manually by the nurses, near the patient, in the

moment that the things happen. The nurse chooses

the event that happened, from a set of predefined

events, in that moment and this automatically appear

in another table with the description and the time of

the event. For the decision process, the medical staff

can consult the patient historical registers, only

choosing the date before. The base of the grid is the

same, changing only the values and variables

collected in the chosen day.

Figure 2: Knowledge Discovery Process in ICU.

4.2.2 Lab Results (LR)

The Lab Results are not under the nurse’s control in

the ICU, because they are ordered by the doctors and

executed by the labs. The clinical results appear in

the ENR according to the laboratory work. This

platform has a singular form to present the lab

results in a grid. With this solution is possible to

compare all the results by patient grouped by the

clinical exam class and date. This facilitates the

decision process because it makes possible the

analyses of the results evolution by patient and by

variable during all stay.

4.2.3 ICU Scores

In the ICU is used a set of scores, especially pain

scores, Glasgow (Jones, 1979), SOFA (Vincent et

al., 1998) and SAPS (Le Gall et al., 1993) to predict

some clinical problem or know the real situation of

the patient. The introduction of ENR associated with

the electronic acquisition and registering of all data,

allow for the automatic scores calculation.

A mechanism of calculation was developed in

the ENR for each target score. Every time a new

value arrives to the variables included in the score,

the ENR Agent verifies which the worst value of the

day is and according to the range of each target

score it assigns a punctuation to be calculated in the

final. The final result only will be calculated when

all variables of the score were obtained. If some

variable wasn't collected automatically, the nurses

can introduce the result manually. The calculation of

scores will be explained in the next chapter.

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5 ICU – DATA QUALITY

The quality of data is very important for a good

decision-making process. The data quality interferes

directly in the success of to the knowledge discovery

process that occurs in the ICU. This process starts

with the data stored in database collected from the

Vital Signs, EHR, Drugs System, Lab Results and

Events. All information collected will be stored in

database. Dependent of the variables collected and

the targets defined, the data will be validated and

prepared according the processing and

transformation rules.

The data resulting of this process is the real data

collected in ICU about the patient. This data will be

available through ENR and is prepared to be used by

DM techniques ( Santos and Portela, 2011); (Villas

Boas et al., 2010) and create knowledge to the

INTCare System. This process is represented in the

Figure 2. The ENR collects the data that was

automatic acquired, stored in database, processed

and transformed. The resulting data from the

processing tasks will be the real data and are these

that will be showed by ENR. The ENR also is a data

sources and also is subject to this process of quality.

5.1 Data Processing Process

All data collected will be processed by the agents

that execute the tasks and rules defined for each

variable / source. The data processing process is part

of a set of INTCare requirements:

a) Fault Tolerance Capacities and Continuous

Data Acquisition Process. For each value collected,

system working or record expected, the agent will

process the information received in database and if

anything not in agreement with the expected, a

message will be sent. Depending the type of fail,

exist some predefined solution tasks: If the failure

was in gateway, the agent send a message to the

gateway and it will restart instantly, if the failure

was in the LR or in drugs system a force refresh of

the system will be done, finally if something fail in

ENR system, the last record stored in database will

be loaded.

b) Processing to Remove Null and Noisy Data.

This process will be constantly running and has two

types of operations. The first type consist in a

constant looking for null records, if the line stored in

database contains null values, this will be delete.

The second type is responsible for autonomous

validation of the data collected automatic and in real

time from bedside monitors (BM) according the

range values defined to ICU, i.e. if the vital signs

values collected were correct and were possible in

the ICU environment the agent will validate the

results and store the data in a table with the real data.

Table 1 shows the minimum and maximum values

attributed to the vital signs variables that will be

used to Data Mining. The objective is eliminating

the data that has noise, or incorrect values. An

example of noise values is the temperature, because

when the sensor is disconnected from the patient the

temperature collected is from the environment (~24º

to 27º) and not from the patient (~35º to 42º). The

final validation will be always done hourly by the

nursing staff through the ENR functionalities. With

this option if a value is out of range but is true, the

nurse can rectify that and make that value real,

registering this in the database.

Table 1: ICU Vital Signs Range.

Vital Sign Min Max

Blood Pressure (BP) 0 300

SPO2 40 100

Temperature (Temp) 30 45

Respiratory Rate (RR) 0 40

Heart Rate (HR) 0 250

c) Ensure the Patient Identification in Records.

Put the patient id in the monitors, is a manual

process, and because of that is a process that are in

constant fails. The objective is tried to turn this

process autonomous, without any type of human

intervention. The first solution founded was put a

RFID tag in the patient that will identify them in the

system, but this was an expensive solution and slow

to implement. In order to resolve this problem a

more fast possible we decide create a task that

verify, in the EHR, the patients admitted to ICU and

have a null date of discharge then, it sees the number

of the patient bed that came in the HL7 message sent

by gateway and compares with the values present in

the patient admission EHR table. After find a match,

the pre-processing agent will substitute the in the

message the null PID by the patient id present in the

EHR. This make it an autonomous process, which

runs every moments that arrives a new message to

the table, it is a before insert operation.

d) Time Restrictions for the Data Acquisition

and Storage. With the objective that data processing

will be done with the most recent data acquired, a set

of auto operations is scheduled, tasks like the auto

save, auto refresh, auto validation of data, etc. If any

operation can't be executed and if the agents don’t

receive any type of data to process and the

responsible agent will send an alert message

identifying the problem and possible solutions. Each

KNOWLEDGE DISCOVERY FOR PERVASIVE AND REAL-TIME INTELLIGENT DECISION SUPPORT IN

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245

individual agent (vital signs acquisition, pre-

processing agent, ENR, and Lab Results) is

responsible for the operation of their tasks group.

e) Digital Data Archive in Order to Promote the

Dematerialization of Paper based Processes. The

solution founded to this requirement involves a set

of agents, tasks and electronic process that can be

executed automatic and controlled by the humans.

The objective of this requirement is processing all

Fluid balance, nursing records, medical scores,

according the panel tables defined in ICU with this

we hope ensure the actuality of tables, ensure that

the data will be processed to data mining models i.e.,

all manual register types will be transformed in

online and electronic records that can be done in real

time by the humans in the moment that the situation

happens or value need to be collected. This

requirement was the base of development of the

entire ENR, intelligent agents, interoperability

between systems and other types of operations.

5.2 Data Transformation Process

To the development of prevision models in real-time

using Data Mining (DM) techniques and basing that

in the offline results obtained in the past (Gago et

al., 2007); (Á. Silva et al., 2008) is necessary

implement a set of automatic operations that

facilitate the calculation of critical events and

medical scores.

a) Critical Events. A critical event happened when

a patient have values out of normal range for a

determinate time. Table 2 is the base to the

calculation of critical Events function and presents

three types for the calculation of critical events. The

calculation of critical events is a consequent task of

the processing to remove null and noisy data. For

this, this operations only will be used for the values

pre-validated by the system, i.e., the calculation is

done with base in the maximum range defined for

ICU, the normal range and the time for an event be

critical (A. Silva et al., 2006).

First is verified if the value is normal or critical,

next, will calculated the time of the event and stored

in the critical events table, an event identification.

Table 2: ICU Critical Events Range.

 BP O2 HR Urine

NormalRange 90−180≥90% 60−120≥30

CriticalEv.a≥1h≥1h≥1h≥2h

CriticalEv.b≥1hin2h≥1hin2h≥1hin2h –

CriticalEv.c <60 <80 <30V>180 ≤10

After received a row in the database, the agent

will read the variable id, and will compare the result

collected with the values present in Table 2 for that

variable. If the value is out of normal range the row

will be update and the type of result will be defined

with critical (1) if the values is out of normal range

and are worse than values defined for the critical

event c, the row will be assigned to a critical event,

will be update with number 2 and will be copied to

table ICU_CEVENTS. The function 3 shows how

the data collected will be tagged according the

critical state (0,1,2). To calculate the other two types

of critical events will be used the sum functions to

calculate the time that a value was out of normal

range.

b) Medical Scores. There exist three types of

scores that is used in ICU: MEWS (Gardner-Thorpe,

Love, Wrightson, Walsh, & Keeling, 2006), SAPS

(Le Gall, et al., 1993), SOFA (Vincent, et al., 1998).

For each score a set of transformations operation

will be done. The operations are similar for all

scores. The SOFA scores is used in DM models and

will presented that transformation. The value is

allocated according to the score punctuation table.

Using the number of points associated, the final

score will be calculated and the result will be

inserted in the scores table. The results obtained are

collected automatically.

Table 3: SOFA Table (respiratory organ).

System VariabletotheCalculation Score

Respiratory

System

PaO

/FiO

(mmHg)

<400 1

<300 2

<200andmechanicallyventilated 3

<100andmechanicallyventilated 4

6 ICU – AGENTS

In an ICU data can be acquired from a different

number of sources. In our architecture we propose

several data acquisition agents with clear boundaries

and responsibilities. Moreover, given the paramount

importance of data quality for data mining a data

quality agent responsible for detecting errors in the

data was devised. Indeed, data acquisition in the

ICU is error prone as, for instance, sensors may be

displaced as patients move. The agents included in

the data acquisition module are:

A Gateway agent (a

gat

)

connected to the bedside

monitors. Operating in real-time, this agent captures

the vital signs data from the bedside monitors (BM).

KMIS 2011 - International Conference on Knowledge Management and Information Sharing

246

Vital Signs Acquisition agent is in charge of

receiving and parsing the HL7 messages sent by the

Gateway. Other than that real time parsing this agent

also stores the information in the database tables.

In order to allow a straightforward data entry by

the nurses, the ENR Agent (a

enr

) was created

(Portela, Vilas-Boas, Santos, & Fernando, 2010).

This agent is able to save the data automatically or

as a response to a request from the nurse. With this

agent we are able to capture the clinical data from

the medical and nursing staff. The data recorded by

this agent includes the fluid balance, patient

proceedings, and other scores.

Working in close connection with the ENR

agent, the LR agent (a

) is responsible for capturing

the clinical data from the lab results. Every five

minutes this agent checks if there are new lab results

available. Whenever there are new lab results

available the LR agent gets them and stores the

information in the database before sending the

relevant information to the ENR agent. If we are to

use the collected data for decision support we must

make as sure as possible that it contains no errors. In

INTCARE architecture this is a task of the Pre-

Processing (a

) agent.

In an ICU setting it is easy to see sensors that

aren’t correctly placed. This happens mostly due to

patient movement, even if incorrectly placed most

sensors continue to collect data. Naturally this

means that some pre-processing is necessary in order

to avoid that such incorrect values reach the records.

Whenever this happens for a short period the pre-

processing agent replaces the incorrect values with

the average of the values collected prior and after the

error. If the error condition lasts for a long time the

agent replaces the values with zeros, indicating that

no valid values were collected. Finally, this agent

creates a valid medical record for the patient. It does

this by combining all the available values and

ensuring that those values make sense.

7 RESULTS

Until 2009 the doctors based their decisions on

information essentially in paper format or on data

that were accessible only in specific systems or

places. Nowadays, all of this information is available

in electronic format and can be accessed online and

in real-time, in a pervasive approach.

Table 4 shows the evolution of data sources and

the data access verified between 2009 and 2011. The

data source can be Paper (P), Electronic (E) or both

(P/E), i.e. some information are in paper and others

are in digital format. The chart 1 presents a

visualization of the percentage of data in each of

formats for those two years. Like we can verify in

2011, 100% of the data are electronic, acquired in

real-time and available online. This chart also

presents the percentage of processes without patient

ID (PID), that represents a decrease from 53 % in

2009 to 0 % in 2011, i.e., now all messages received

and data collected has PID.

Table 4: ICU Data Sources Format.

DataSources

Variables

2009 2011

Source Online Source Online

NursingRecord P E

√

VitalSigns P

E

√

DrugSystem

P

 E

√



E

√

PatientEHR

√



E

√

Fluidbalance P

E

√

Procedures P/E

E

√

LabResults P

E

√

PatientEvents P / E

E

√

Ventilations P

E

√

PatientScores P



E

√

ICUScores P

E

√

Chart 1: Data access Formats (%).

Chart 2: Data Quality (%).

The modifications introduced in the ICU

information systems and the implementation of data

processing tasks, the level of the data quality

increased significantly. Analyzing the example in

chart 2, we achieved benefits in the all variables

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247

presented, especially in the heart rate where more

than 75% were worst values, i.e., values that were

collected and were out of normal range predefined in

the ICU. The overall benefit is about 34% which

represents the volume of data collected ignored after

the pre-processing.

8 CONCLUSIONS AND FUTURE

WORK

This paper presented an approach to the KDD

procedure in order to enable a pervasive, online and

real-time processing of data in ICU. Such approach

brought improvements in the information

availability and consequently a more proactive

attitude by the doctors is facilitated. The doctors are

supported in their decisions anytime and anywhere.

In particular, data quality problems were completely

solved, e.g., monitored null values, values out of the

range and wrong patient ID. Finally, the quality of

decision making process has been significantly

increased. All the data (100%) used in the decision

process and in data mining models are reliable, i.e.

the values are in the range defined by ICU and the

doctors don't deal with dubious values. In the future

we will study the impact in the validity of data

mining models adding the data (decision variables)

obtained from this process, i.e. the therapeutics and

procedures. In order to control the failures, a

tolerance plan also will be created.

ACKNOWLEDGEMENTS

The authors would like to thank FCT (Foundation of

Science and Technology, Portugal) for the financial

support through the contract PTDC/EIA/72819/

2006. The work of Filipe Portela was supported by

the grant SFRH/BD/70156/2010 from FCT.

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KNOWLEDGE DISCOVERY FOR PERVASIVE AND REAL-TIME INTELLIGENT DECISION SUPPORT IN

INTENSIVE CARE MEDICINE

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