REAL-TIME INTELLIGENT DECISION SUPPORT IN

INTENSIVE MEDICINE

Filipe Portela, Manuel Santos, Marta Vilas-Boas

Departamento de Sistemas de Informação, Universidade do Minho, Guimarães, Portugal

Fernando Rua, Álvaro Silva

Hospital de Santo António, Porto, Portugal

José Neves

Universidade do Minho, Departamento de Informática, Braga, Portugal

Keywords: Real-time, Knowledge Discovery in Databases, Intensive Care, INTCare, Intelligent Decision Support

Systems.

Abstract: Daily, a great amount of data that is gathered in intensive care units, which makes intensive medicine a very

attractive field for applying knowledge discovery in databases. Previously unknown knowledge can be

extracted from that data in order to create prediction and decision models. The challenge is to perform those

tasks in real-time, in order to assist the doctors in the decision making process. The Data Mining models

should be continuously assessed and optimized, if necessary, to maintain a certain accuracy. In this paper

we present the INTCare system, an intelligent decision support system for intensive medicine and the way it

was adapted to the new requirements. Some preliminary results are analysed and discussed.

1 INTRODUCTION

Intensive care units (ICU) are a particular

environment where a great amount of data related to

the patients’ condition is daily produced and

collected. Physiological variables such as heart rate,

blood pressure, temperature, ventilation and brain

activity are constantly monitored on-line (Mahmoud

2003). Due to the complex condition of critical

patients and the huge amount of data, it can be hard

for physicians to decide about the best procedure to

provide them the best health care possible. The

human factor can lead to errors in the decision

making process; frequently, there is not enough time

to analyze the situation because of stressful

circumstances; furthermore, it is not possible to

continuously analyze and memorize all the data

(Pereira et al. 2007) .

The INTCare project is financially supported by FTC

(PTDC/EIA/72819/2006).

Care of the critically ill patients requires fast

acquisition, registering and availability of data

(Gardner et al. 1991). Accordingly, rapid

interpretation of physiological time-series data and

accurate assessment of patient state is crucial to

patient monitoring in critical care. The data analysis

allows supporting decision making through

prediction and decision models. Algorithms that use

Artificial Intelligence (AI) techniques have the

potential to help achieve these tasks, but their

development requires well- annotated patient data

(Ying, Silvers and Randolph 2007, Morik 2003).

We are developing a real-time and situated

intelligent decision support system, called INTCare

whose main goal is to improve the health care,

allowing the physicians to take a pro-active attitude

in the patients’ best interest (Santos et al. 2006,

Gago et al. 2006).

INTCare is capable of predicting organ failure

probability, the outcome of the patient for the next

hour, as well as the best suited treatment to apply.

Portela F., Santos M., Vilas Boas M., Rua F., Silva Á. and Neves J..

REAL-TIME INTELLIGENT DECISION SUPPORT IN INTENSIVE MEDICINE.

DOI: 10.5220/0003098200440050

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2010), pages 44-50

ISBN: 978-989-8425-30-0

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

To achieve this, it includes models induced by

means of Data Mining (DM) techniques (Santos et

al. 2006), (Gago and Santos 2008, Gago, Silva and

Santos 2007, Silva et al. 2003, Silva et al. 2004).

This paper is organized as follows. Section 2

presents some background relating to Intelligent

Decision Support Systems (IDSS), Knowledge

Discovery in Databases (KDD) and intensive

medicine. In section 3 it is presented the INTCare

system, focusing on its features, the information

architecture and the latest DM models developed.

Section 4 and 5 conclude this paper, presenting a

discussion, a conclusion and pointing to future work.

2 BACKGROUND

2.1 Intelligent Decision Support

Systems

According to Turban (Turban, Aronson and Liang

2005), a Decision Support System (DSS) is an

interactive, flexible and adaptable information

system, developed to support a problem solution and

to improve the decision making. These systems

usually use AI techniques and are based on

prediction and decision models that analyze a vast

amount of variables to answer a question.

The decision making process can be divided in

five phases: Intelligence, design, choice,

implementation and monitoring (Turban et al. 2005).

Usually it is used in the development of rule based

DSS (Arnott and Pervan 2004). However, these DSS

are not adaptable to the environment in which they

operate. To address this fault, Michalewicz

(Michalewicz et al. 2007) introduced the concept of

Adaptive Business Intelligence (ABI). The main

difference between this and a regular DSS is that it

includes optimization that enables adaptability. An

ABI system can be defined as “the discipline of

using prediction and optimization techniques to

build self-learning decisioning systems. ABI

systems include elements of data mining, predictive

modelling, forecasting, optimization, and

adaptability, and are used to make better decisions.”

(Michalewicz et al. 2007).

As it is known, predictive models’ performance

tends to degrade over time, so it is advantageous to

include model re-evaluating on a regular basis so as

to identify loss of accuracy (Gago and Santos 2008)

and enable their optimization.

There is a particular type of DSS, the real-time

DSS. Ideally, the later includes adaptive behaviour,

supporting the decision making in real-time.

To achieve real-time DSS, there is a need for a

continuous data monitoring and acquisition systems.

It should also be able to update the models in real

time without human intervention (Santos et al.

2006). In medicine, most systems only use data

monitoring to support its activities, without

predictive behaviour and with poor integration with

other clinical information.

2.2 Knowledge Discovery

from Databases

KDD is one of the approaches used in Business

Intelligence (BI). According to Negash (Negash and

Gray), BI systems combine data gathering, data

storage, and knowledge management with analytical

tools to present complex and competitive

information to planners and decision makers. KDD

is an interactive and nontrivial process of extracting

implicit and previously unknown and potentially

useful and understandable information from data

(Frawley, Piatetsky-Shapiro and Matheus 1992).

The KDD process is divided in 5 steps:

Selection, pre-processing, transformation, data

mining and interpretation/evaluation (Fayyad,

Piatetsky-Shapiro and Smyth 1996). This process

starts with raw data and ends with knowledge.

The automation of the knowledge acquisition

process is desirable and it is achieved by using

methods of several areas of expertise, like machine

learning (Gago et al.). The knowledge acquisition

takes advantage of KDD techniques, simplifying the

process of decision support (Gago and Santos).

Knowledge discovery is a priority, constantly

demanding for new, better suited efforts. Systems or

tools capable of dealing with the steadily growing

amount of data presented by information system, are

in order (Lourenco and Belo 2003).

2.3 Intensive Medicine

Intensive medicine can be defined as a

multidisciplinary field of the medical sciences that

deals with prevention, diagnosis and treatment of

acute situations potentially reversible, in patients

with failure of one or more vital functions (Silva

2007). These can be grouped into six organic

systems: Liver, respiratory, cardiovascular,

coagulation, central nervous and renal (Hall,

Schmidt and Wood 2005).

ICU are hospital services whose main goal is to

provide health care to patients in critical situations

and whose survival depends on the intensive care

(Ramon et al. 2007), (Rao and T. 2003). In the ICU,

REAL-TIME INTELLIGENT DECISION SUPPORT IN INTENSIVE MEDICINE

the patients’ vital signs are continuously monitored

and their vital functions can be supported by

medication or mechanical devices, until the patient

is able to do it autonomously (Ramon et al. 2007).

Clinical intervention is based on the degree of

severity scores like the SOFA (Sequential Organ

Failure Assessment) score, that allow the evaluation

of the patient’s condition according to a predefined

set of values (Vincent et al. 1996).

The assessment of these severity scores are based

on several medical data acquired from bedside

monitors, lab results and clinical records.

2.4 Real-time

A system that aims to support decision making must

analyze many parameters and output in short real-

time and consider online monitoring (Morik,

Brockhausen and Joachims). It is known that in the

ICU setting, there is a huge amount of noisy, high

dimensional numerical time series data describing

patients. Consequently, such systems must go

beyond classical medical knowledge acquisition,

since they have to handle with high dimensional data

in real-time.

Data acquisition in real-time implies the need for

a system responsible for collecting the relevant data

to the DSS. This process can be divided in two

phases: monitoring and acquisition and storage.

Initially, the required data (variables) for the project

is identified for further being monitored by sensors

or other technology. Subsequently, data is acquired

and stored in DB. This is a critical phase, for

technical, human and environment factors are

involved and may condition the quality of the data

acquired by a gateway, for example, and its storage

on a DB. Usually, the monitoring is continuous and

there is a small percentage of failures. Although they

may occur, they are relatively easy to correct. The

biggest problem occurs in the communication

between the monitoring system and the storage

system.

In conclusion, monitoring in real-time is relatively

easy; usually, problems arise in the data storage

process.

3 THE INTCARE SYSTEM

INTCare is an IDSS for intensive medicine that is

being developed in the ICU of the Hospital Santo

António (HSA) in Porto, Portugal. It makes use of

intelligent agents (M. F. Santos M.F.) (Abelha et al.)

that are capable of autonomous actions in order to

meet its goals (Gago et al. 2006), (Jennings 2000).

3.1 System Features

In order to model the information for KDD

processing, the system attends some requirements:

Online Learning. The system acts online, i.e., the

DM models are induced using online data in

opposition of an offline approach, where the data is

gathered and processed afterwards;

Real-time. The system actuates in real-time, for the

data acquisition and storing is made immediately

after the events take place to allow that decisions are

taken whenever an event

occurs;

Adaptability. The system has the ability to,

automatically, optimize the models with new data

when needed. This information is obtained from

their evaluation results;

Data Mining Models. The success of IDSS

depends, among others, on the acuity of the DM

models, i.e., the prediction models must be reliable.

These models make it possible to predict events and

avert some clinical complications to the patients;

Decision Models. The achievement of the best

solutions depend heavily on the decision models

created. Those are based in factors like

differentiation and decision that are applied on

prediction models and can help the doctors to choose

the better solution on the decision making process;

Optimization. The DM models are optimized over

time. With this, their algorithms are in continuous

training so that increasingly accurate and reliable

solutions are returned, improving the models acuity;

Intelligent Agents. This type of agents makes the

system work through autonomous actions that

execute some essential tasks. Those tasks support

some modules of the system: Data acquisition, data

entry, knowledge management, inference and

interface. The flexibility and efficiency of this kind

of system emerges from the intelligent agents and

their interaction (Gago et al. 2006).

In order to accomplish these features, the system

has some requirements:

• Fault tolerance capacities;

• Processing to remove null and noisy data;

• Continuous data acquisition process;

• Time restrictions for the data acquisition and

storage;

• Online learning mode;

KMIS 2010 - International Conference on Knowledge Management and Information Sharing

• Digital data archive in order to promote the

dematerialization of paper based processes (e.g.,

nursing records);

• Database extension to accommodate the data

structures;

• Correct usage of the equipment that collects the

vital signs.

3.2 Information Architecture

Patient management is supported by complex

information systems, which brings the need for

integration of the various types and sources of data

(Fonseca, Ribeiro and Granja 2009).

In order to follow the requirements enumerated

above, an information model was drawn, regarding

the data acquisition module which includes three

types of information sources:

• Bedside Monitors (BM);

• Lab Results (LR); and

• Electronic Nursing Records (ENR).

All sources can produce information to the system

and that information can be used to develop

predicting models in Intensive Care (knowledge).

The development of an automated information

system for ICU has to be in harmony with the whole

information system and activities within the unit and

the hospital (Fonseca et al. 2009).

The first type of sources relates to data

acquisition from BM. This acquisition is in real-

time, the data is received by a gateway, and it is

stored on a DB table by an agent. Automatic

acquisition eliminates transcription errors, improves

the quality of records and allows the assembly of

large electronic archives of vital sign data (Fonseca

et al. 2009)

The second type of sources (LR) is the one that

contains the less frequent observations, because the

patient normally does this type of clinical analysis

once or twice per day, except in extraordinary

situations. With this method we can collect the data

related with some clinical analysis, such as: number

of blood platelets, creatinine, billirubin, SOFA

scores, partial pressure of oxygen in arterial blood

and fraction of inspired oxygen.

3.3 INTCare Sub-systems

Functionality

The INTCare System (Gago et al. 2006, Santos et al.

2006) is divided into five subsystems, represented in

Figure 1: data acquisition, data entry, knowledge

management, inference and interface. Figure 1

shows a model that is a part of INTCare system and

represents an evolution of two subsystems: data

acquisition and data entry.

Figure 1: The INTCare system.

This subsystem is responsible for all activities of

data acquisition and data store and will gather all

required data into a data warehouse (Santos M.F.

and J. 2009, Santos 2009, Santos et al. 2009). The

evolution of this architecture is prominent.

Formerly, most of the data was registered in paper

format, and it was necessary to manually put it in

electronic format, i.e., the information was rarely

stored in computers, except the information from the

BM, which was automatically collected and stored

in electronic format.

The new architecture (Santos M.F. and J. 2009,

Santos 2009, Santos et al. 2009) contemplates the

data acquisition from three sources and, regarding

the information input, it is done either automatically

(BM, LR) or automatically and manually (nursing

records). The adjustment made to the system was the

addition of one more data source and the creation of

two more agents that enable storing information in

the database (DB).

This modification is in course and it is the most

important, because it makes possible the data

acquisition in electronic and automatic mode for all

data sources through multi-agent system. Whit this

change, we will have all the necessary information

in electronic format for the DM models and the

decision support process, addressing the timing

requirements of critical tasks.

How These Subsystems Work. The first type of

data sources is the BM, which collects the patients’

vital signs (VS). The gateway is connected to the

monitors, reads the information and stores it on a

DB through the data acquisition agent. This agent

splits a HL7 (Hooda, Dogdu and Sunderraman)

message in two, one with the header information and

another with patient data. The second source is the

ENR (Santos M.F. and J. 2009). It was developed

with the objective of registering electronically the

REAL-TIME INTELLIGENT DECISION SUPPORT IN INTENSIVE MEDICINE

paper-based nursing records. With the ENR, the

medical and nursing staff can register various types

of data, like confirming if some therapeutic was

performed or not, and they may consult all the

present and past data about the patients. The last

type of data sources is the LR, which is controlled

by the clinical analyses agent that automatically

stores all the LB from the patients.

All the data is stored in one DB and it can be

accessed by the medical staff through a computer.

The integrated data will be used by the INTCare

system to create prevision and decision models.

The DM agent belongs to the sub-system

knowledge management and it is in charge of

retrieving the required data to feed the DM models

and to train new models whenever their performance

becomes unsatisfactory.

3.4 Data Mining Models

3.4.1 Data Description

The data used to generate the DM models originates

from three distributed and heterogeneous sources:

LR, BM and paper-based nursing records, presented

and explained previously. Additionally, variables

containing the case mix (information that remains

unchanged during the patient’s stay - age, admission

from, admission type) were also considered. It was

also included some calculated variables: Critical

Events (CE), SOFA scores and a set of ratios

relating the previous variables to the patients’ length

of stay. The data was gathered in the ICU of HSA

and it was collected in the first five days of stay of

thirty two patients. The construction of the dataset

was not automatically, the data from the LB and

nursing records was manually registered, for the new

adjustments of the system regarding the data

acquisition and data entry were not developed at the

time the models were generated

3.4.2 Features Selection

For the prediction of the dysfunction/failure of each

organic system and outcome, three scenarios were

explored regarding the inclusion of the variables

mentioned above – M1, M2, and M3 – where

M1 = {Hour, Case Mix, CE}

M2 = {Hour, Case Mix, CE, Ratios}

M3 = {Hour, Case Mix, CE, SOFA}.

For each model, the techniques applied were

Artificial Neural Networks, Decision Trees,

Regression and Ensemble methods.

3.4.3 Results

Table 1 shows the best results achieved for

cardiovascular, respiratory, renal, liver, coagulation

and neurological systems and outcome in terms of

sensibility (i.e. percentage of failure and death

correctly classified as such) as well as the scenario

that produced the best results. The models were

developed for hourly prediction with the intent to

make predictions as fast as possible, in the patients

best interest.

Table 1: Sensibility of the DM models by system and

outcome.

System Sensibility (%) Scenario

Cardiovascular 93.4 M3

Respiratory 96.2 M2

Renal 98.1 M3

Coagulation 97.5 M2

4 DISCUSSION

In this paper we presented the INTCare system,

which is an IDSS for intensive medicine. It relies on

the KDD process and AI algorithms to apply DM

techniques for predicting outcomes that might

support the course of action of doctors’ decision.

Relying on intelligent agents, the system in divided

into five sub-systems (data acquisition, data entry,

knowledge management, inference and interface)

that guarantee its functionality.

Since its beginning, INTCare has evolved towards

using real-time and online clinical data so that the

predictions can be as accurate and as soon as

possible. As an IDSS, INTCare uses continuous data

monitoring and acquisition systems that make

possible for all information being available at the

right time. This allows doctors to have a proactive

attitude in patients’ care.

The development of an ENR allows the integration

of all necessary information regarding the patients’

condition to be collected and integrated in just one

application, which is a great gain in time and

performance for the medical staff operating in the

ICU. In addition to the patients’ vital signs, data

regarding their LR, procedures, medication, is also

available by the time it is generated.

Moreover, the INTCare system is designed to

address know issues of the ICU setting, such as

noisy, high dimensional numerical time series data

in real-time (Morik et al.), as well as the data

KMIS 2010 - International Conference on Knowledge Management and Information Sharing

acquisition in real-time, storage, integration and

rapid availability of all clinical information.

5 CONCLUSIONS AND FUTURE

WORK

The main concern in ICU is to avoid or reverse

organ failure, in order to preserve the patients’ lives.

The INTCare system is being developed for hourly

prediction of the patients’ clinical condition, i.e. the

prediction of dysfunction/failure of the organ

systems (cardiovascular, respiratory, renal,

coagulation and liver systems) and outcome. We

believe that, with this fine grained prediction, it will

be possible for the healthcare professionals to have a

timely intervention and a proactive attitude so that

worst complications for the patients may be avoided.

Further work will encompass the test of the DM

models generated so far, with online and real-time

data from the ICU of HSA, in order to guarantee

their accuracy or, in case their performance decays,

to optimize them. The models presented used data

manually entered and the next step is to use them

with the new adjustments of the system, i.e., online

and in real-time. Prediction, optimization and

adaptability are features that make INTCare an ABI

system, whose maid goal it to allow the medical

staff to make better decisions, at the right time and

place, improving quality in health care.

The integration with the various data sources and

with the rest information systems of the hospital has

been supported by the development of an ENR and

further related work include its test in the ICU and

subsequently, its optimization.

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