MULTIPLE ORGAN FAILURE DIAGNOSIS USING ADVERSE

EVENTS AND NEURAL NETWORKS

Alvaro Silva Paulo Cortez Manuel Santos

Hospital Geral de Santo Ant

onio DSI, Universidade do Minho DSI, Universidade do Minho

Porto, Portugal Guimar

aes, Portugal Guimar

aes, Portugal

Lopes Gomes Jos

e Neves

Inst. de Ci

encias Biom

edicas Abel Salazar DI, Universidade do Minho

Porto, Portugal Braga, Portugal

Keywords:

Intensive Care Medicine, Classiﬁcation, Clinical Data Mining, Multilayer Perceptrons.

Abstract:

In the past years, the Clinical Data Mining arena has suffered a remarkable development, where intelligent

data analysis tools, such as Neural Networks, have been successfully applied in the design of medical systems.

In this work, Neural Networks are applied to the prediction of organ dysfunction in Intensive Care Units. The

novelty of this approach comes from the use of adverse events, which are triggered from four bedside alarms,

being achieved an overall predictive accuracy of 70%.

1 INTRODUCTION

Scoring the severity of illness has become a daily rou-

tine practice in Intensive Care Units (ICUs), with sev-

eral metrics available, such as the Acute Physiology

and Chronic Health Evaluation System (APACHE II)

or the Acute Physiology Score (SAPS II), just to name

a few (Teres and Pekow, 2000). Yet, most of these

prognostic models (given by Logistic Regression) are

static, being computed with data collected within the

ﬁrst 24 hours of a patient’s admission. This will pro-

duce a limited impact in clinical decision making,

since there is a lack of accuracy of the patient’s con-

dition, with no intermediate information being used.

On the other hand, the Clinical Data Mining is a

rapidly growing ﬁeld, which aims at discovering pat-

terns in large clinical heterogeneous data (Cios and

Moore, 2002). In particular, an increasing attention

has been set of the use of Neural Networks (connec-

tionist models that mimic the human central nervous

system) in Medicine, with the number of publications

growing from two in 1990 to ﬁve hundred in 1998

(Dybowski, 2000).

The interest in Data Mining arose due to the rapid

emergence of electronic data management methods,

holding valuable and complex information (Hand

et al., 2001). However, human experts are limited and

may overlook important details, while tecniques such

as Neural networks have the potential to solve some

of these hurdles, due to capabilities such as nonlinear

learning, multi-dimensional mapping and noise toler-

ance (Bishop, 1995).

In ICUs, organ failure diagnosis in real time is a

critical task. Its rapid detection (or even prediction)

may allow physicians to respond quickly with therapy

(or act in a proactive way). Moreover, multiple organ

dysfunction will highly increase the probability of the

patient’s death.

The usual approach to detect organ failure is based

in the Sequential Organ Failure Assessment (SOFA),

a diary index, ranging from 0 to 4, where an organ

is considered to fail when its SOFA score is equal or

higher than 3 (Vincent et al., 1996). However, this

index takes some effort to be obtained (in terms of

time and costs).

This work is motivated by the success of previ-

ous applications of Data Mining techniques in ICUs,

such as predicting hospital mortality (Santos et al.,

2002). The aim is to study the application of Neu-

ral Networks for organ failure prediction (identiﬁed

by high SOFA values) of six systems: respiratory, co-

agulation, liver, cardiovascular, central nervous and

renal. Several approaches will be tested, using differ-

ent feature selection, training and modeling conﬁgu-

rations. A particular focus will be given to the use of

daily intermediate adverse events, which can be auto-

matically obtained from four hourly bedside measure-

ments, with fewer costs when compared to the SOFA

score.

The paper is organized as follows: ﬁrst, the ICU

clinical data is presented, being preprocessed and

transformed into a format that enables the classiﬁca-

401

Silva Á., Cortez P., Santos M., Gomes L. and Neves J. (2004).

MULTIPLE ORGAN FAILURE DIAGNOSIS USING ADVERSE EVENTS AND NEURAL NETWORKS.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 401-408

DOI: 10.5220/0002623804010408

 SciTePress

tion task; then, the neural models for organ failure

diagnosis are introduced; next, a description of the

different experiments performed is given, being the

results analyzed and discussed; ﬁnally, closing con-

clusions are drawn.

2 MATERIALS AND METHODS

2.1 Clinical Data

In this work, a part of the EURICUS II database

(www.frice.nl) was adopted which contains data

related to 5355 patients from 42 ICUs and 9 Euro-

pean Union countries, collected during a period of 10

months, from 1998 to 1999. The database has one en-

try (or example) per each day (with a total of 30570),

being its main features described in Table 1:

• The ﬁrst six rows denote the SOFA values (one for

each organ) of the patient’s condition in the previ-

ous day. In terms of notation, these will be denoted

by SOF A

d−1

, where d represents the current day.

• The case mix appears in the next four rows, an in-

formation that remains unchanged during the pa-

tient’s internment, containing: the admission type

(1 - Non scheduled surgery, 2 - Scheduled surgery,

3 - Physician); the admission origin (1 - Surgery

block, 2 - Recovery room, 3 - Emergency room, 4

- Nursing room, 5 - Other ICU, 6 - Other hospi-

tal, 7 - Other sources); the SAPSII score (a mortal-

ity prediction index, where higher values suggest a

high death probability) and the patient’s age. Fig-

ure 1 shows the frequency distributions of these at-

tributes.

• Finally, the last four rows denote the intermediate

outcomes, which are triggered from four monitored

biometrics: the systolic Blood Pressure (BP); the

Heart Rate (HR); the Oxygen saturation (O2); and

the URine Output (UR). A panel of EURICUS II

experts deﬁned the normal ranges for these four

variables (Tables 2 and 3). Each event (or critical

event) is deﬁned as a binary variable, which will be

set to 0 (false), if the physiologic value lies within

the advised range; or 1 (true) else, according to the

time criterion.

Before attempting modeling, the data was prepro-

cessed, in order to set the desired classiﬁcation out-

puts. First, six new attributes were created, by slid-

ing the SOF A

d−1

values into each previous exam-

ple, since the intention is to predict the patient’s con-

dition (SOF A

) with the available data at day d

(SOF A

d−1

, case mix and adverse events). Then,

the last day of the patient’s admission entries were

discarded (remaining a total of 25309), since in this

cases, no SOF A

information is available. Finally,

the new attributes were transformed into binary vari-

ables, according to the expression:

0 , if SOF A

< 3 (false, no organ failure)

1 , else (true, organ dysfunction)

(1)

2.2 Neural Networks

In MultiLayer Perceptrons, one of the most popular

Neural Network architectures, neurons are grouped

into layers and only forward connections exist

(Bishop, 1995). Supervised learning is achieved by

an iterative adjustment of the network connection

weights (the training procedure), in order to minimize

an error function, computed over the training exam-

ples (cases).

The state of a neuron (s

) is given by (Haykin,

1999):

= f(w

i,0

j∈I

i,j

× s

) (2)

where I represents the set of nodes reaching node

i, f the activation function (possibly of nonlinear

nature), w

i,j

the weight of the connection between

nodes j and i (when j = 0, it is called bias); and

= x

, . . . , s

= x

, being x

, . . . , x

the input

vector values for a network with n inputs.

i,0

i,j

Hidden Layer

Input Layer

Output Layer

Figure 2: A fully connected network with 3 inputs, 2 hidden

nodes, 1 output and bias connections.

All experiments reported in this work will be con-

ducted using a neural network object oriented pro-

gramming environment, developed in JAVA.

Fully connected Multilayer Perceptrons with bias

connections, one hidden layer (with a ﬁxed num-

ber of hidden nodes) and logistic activation functions

(f(x) =

1+e

−x

) were adopted for the organ failure

classiﬁcation (Figure 2). Only one output node is

used, since each organ system will be modeled by a

different network. This splitting is expected to facili-

tate the Neural Network learning process. Therefore,

ICEIS 2004 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

402

ADMTYPE

Frequency

1.0 1.5 2.0 2.5 3.0

0 5000 10000 15000

ADMFROM

Frequency

1 2 3 4 5 6 7

0 2000 4000 6000 8000

SAPSII

Frequency

0 20 40 60 80 100 120

0 1000 3000 5000 7000

AGE

Frequency

20 40 60 80 100

0 1000 2000 3000 4000

Figure 1: The case mix histograms.

Table 1: The clinical data attributes.

Attribute Description Domain Values

respirat Respiratory {0, 1, 2, 3, 4}

coagulat Coagulation {0, 1, 2, 3, 4}

liver Liver {0, 1, 2, 3, 4}

cardiova Cardiovascular {0, 1, 2, 3, 4}

cns Central nervous system {0, 1, 2, 3, 4}

renal Renal {0, 1, 2, 3, 4}

admtype Admission type {1, 2, 3}

admfrom Admission origin {1, 2, . . . , 7}

sapsII SAPSII score {0, 1, . . . , 160}

age Patients’ age {18, 19, . . . , 100}

NBP Number of daily BP events and critical events {0, 1, . . . , 28}

NHR Number of daily HR events and critical events {0, 1, . . . , 26}

NO2 Number of daily O2 events and critical events {0, 1, . . . , 30}

NUR Number of daily UR events and critical events {0, 1, . . . , 29}

the predicted class (P

) for the k example is given the

nearest class value:

0 , if s

k,o

< 0.50

1 , else

(3)

where s

k,o

denotes the output value for the o output

node and the k input example.

Before feeding the Neural Networks, the data

was preprocessed: the input values were standard-

ized into the range [−1, 1] and a 1-of-C encoding

(one binary variable per class) was applied to the

nominal attributes (non ordered) with few categories

MULTIPLE ORGAN FAILURE DIAGNOSIS USING ADVERSE EVENTS AND NEURAL NETWORKS

403

Table 2: The event time ranges.

Event

Suggested Continuously Intermittently

Range Out of Range Out of Range

BP (mmHg) 90 − 180 ≥ 10

≥ 10

in 30

O2 (%) ≥ 90 ≥ 10

≥ 10

in 30

HR (bpm) 60 − 120 ≥ 10

≥ 10

in 30

UR (ml/hour) ≥ 30 ≥ 1 hour

Table 3: The critical event time ranges.

Critical Suggested Continuously Intermittently Event

Event Range Out of Range Out of Range Anytime

BP (mmHg) 90 − 180 ≥ 60

≥ 60

in 120

BP < 60

O2 (%) ≥ 90 ≥ 60

≥ 60

in 120

O2 < 80

HR (bpm) 60 − 120 ≥ 60

≥ 60

in 120

HR < 30 ∨ HR > 180

UR (ml/hour) ≥ 30 ≥ 2 hours ≤ 10

(SOF A

d−1

, admtype and admfrom). For example,

the admtype variable is fed into 3 input nodes, accord-

ing to the scheme:

1 → −1 −1 1

2 → −1 1 −1

3 → −1 −1 1

At the beginning of the training process, the net-

work weights are randomly set within the range [-1,1].

Then, the RPROP algorithm (Riedmiller, 1994) is ap-

plied, due to its faster convergence and stability, be-

ing stopped when the training error slope is approach-

ing zero or after a maximum of E epochs (Prechelt,

1998).

2.2.1 Statistics

To insure statistical signiﬁcance, 30 runs were applied

in all tests, being the accuracy estimates achieved us-

ing the Holdout method (Flexer, 1996). In each sim-

ulation, the available data is divided into two mutu-

ally exclusive partitions, using stratiﬁed sampling: the

training set, used during the modeling phase; and the

test set, being used after training, in order to compute

the accuracy estimates.

A common tool for classiﬁcation analysis is the

confusion matrix (Kohavi and Provost, 1998), a ma-

trix of size L × L, where L denotes the number of

possible classes (domain). This matrix is created by

matching the predicted (test result) and actual (pa-

tients real condition) values. When L = 2 and there

are four possibilities (Table 4): the number of True

Negative (TN), False Positive (FP), False Negative

(FN) and True Positive (TP) classiﬁcations.

From the matrix, three accuracy measures can be

deﬁned (Essex-Sorlie, 1995): the Sensitivity (also

Table 4: The 2 × 2 confusion matrix.

↓ actual \ predicted → negative positive

negative T N F P

positive F N T P

known as recall and Type II Error); the Speciﬁcity

(also known as precision and Type I Error); and the

Accuracy, which gives an overall evaluation. These

metrics can be computed using the following equa-

tions:

Sensitivity =

T P

F N+T P

× 100 (%)

Specif icity =

T N

T N+F P

× 100 (%)

Accuracy =

T N+T P

T N+F P +F N +T P

× 100 (%)

(4)

3 RESULTS

3.1 Feature Selection

Four different feature selection conﬁgurations will be

tested, in order to evaluate the input attribute impor-

tance:

A - which uses only the SOF A

d−1

values (1 vari-

able).

B - where all available input information is used

(SOF A

d−1

of the corresponding organ system, the

case mix and the adverse events, in a total of 9 at-

tributes);

C - in this case, the SOF A

d−1

is omitted (8 vari-

ables); and

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404

D - which uses only the four adverse outcomes.

Since the SOFA score takes costs and time to obtain,

in this study, a special attention will be given to the

last two settings.

In the initial experiments, it was considered more

important to approach feature selection than model

selection. Due to time constrains, the number of hid-

den nodes was set to round(N/2), where N denotes

the number of input nodes (N = 5, N = 21, N = 16

and N = 4, for the A, B, C and D setups, respec-

tively); and round(x) gives nearest integer to the x

value.

The commonly used 2/3 and 1/3 partitions were

adopted for the training and test sets (Flexer, 1996),

while the maximum number of training epochs was

set to E = 100. Each input conﬁguration was tested

for all organ systems, being the accuracy measures

given in terms of the mean of thirty runs (Table 5).

The A selection manages to achieve a high per-

formance, with an Accuracy ranging from 86% to

97%, even surpassing the B conﬁguration. This is

not surprising, since it is a well established fact that

the SOF A is a adequate score for organ dysfunction.

Therefore, the results suggest that there is a high cor-

relation between SOF A

d−1

and SOF A

When the SOF A index is omitted (C and D), the

Accuracy values only decay slightly. However, this

measure (which is popular within Data Mining com-

munity) is not sufﬁcient in Medicine. Ideally, a test

should report both high Sensitivity and Speciﬁcity val-

ues, which suggest a high level of conﬁdence (Essex-

Sorlie, 1995). In fact, there seems to be a trade-

off between these two characteristics, since when the

SOF A values are not present (Table 5), the Sensi-

tivity values suffer a huge loss, while the Speciﬁcity

values increase.

3.2 Balanced Training

Why do the A/B selections lead to high Accuracy

/Speciﬁcity values and low Sensitivity ones? The an-

swer may be due to the biased nature of the organ

dysfunction distributions; i.e., there is a much higher

number of false (0) than true (1) conditions (Figure

3).

One solution to solve this handicap, is to balance

the training data; i.e., to use an equal number of true

and false learning examples. Therefore, another set

of experiments was devised (Table 6), using random

sampling training sets, which contained 2/3 of the

true examples, plus an equal number of false exam-

ples. The test set was composed of the other 1/3 posi-

tive entries. In order to achieve a fair comparison with

the previous results, the negative test examples were

randomly selected from the remaining ones, with a

distribution identical to the one found in the original

dataset (as given by Figure 3).

The obtained results show a clear improvement in

the Sensitivity values, specially for the C conﬁgu-

ration, stressing the importance of the case mix at-

tributes. Yet, the overall results are still far from the

ones given by the A selection.

3.3 Improving Learning

Until now, the main focus was over selecting the cor-

rect training data. Since the obtained results are still

not satisfactory, the attention will move towards bet-

ter Neural Network modeling. This will be achieved

by changing two parameters: the number of hidden

nodes and the maximum number of training epochs.

Due to computational power restrictions, these factors

were kept ﬁxed in the previous experiments. How-

ever, the adoption of balanced training leads to a con-

siderable reduction of the number of training cases,

thus reducing the required training time.

Several experimental trials were conducted, using

different combinations of hidden nodes (H = 4, 8,

16 and 32) and maximum number of epochs (E =

100, 500 and 1000), being selected the conﬁguration

which gave the lowest training errors (H = 16 and

E = 1000). These setup lead to better results, for all

organ systems and accuracy measures (Table 6).

To evaluate the obtained results, a comparison

with other Machine Learning classiﬁers was per-

formed (Table 7), using two classical methods from

the WEKA Machine Learning software (Witten and

Frank, 2000): Naive Bayes - a statistical algorithm

based on probability estimation; and JRIP - a learner

based on ”IF-THEN” rules.

Although presenting a better Accuracy, the Naive

Bayes tends to emphasize the Speciﬁcity values, giv-

ing poor Sensitivity results. A better behavior is

given by the JRIP method, with similar Sensitivity and

Speciﬁcity values. Nevertheless, the Neural Networks

still exhibit the best overall performances.

4 CONCLUSIONS

The surge of novel bio-inspired tools, such as Neural

Networks, has created new exciting possibilities for

the ﬁeld of Clinical Data Mining. In this work, these

techniques were applied for organ failure diagnosis

of ICU patients.

Preliminary experiments were drawn to test several

feature selection conﬁgurations, being the best results

obtained by the solely use of the SOFA value, mea-

sured in the previous day. However, this score takes

much more time and costs to be obtained, when com-

pared with the physiologic adverse events. Therefore,

another set of experiments were conducted, in order

MULTIPLE ORGAN FAILURE DIAGNOSIS USING ADVERSE EVENTS AND NEURAL NETWORKS

405

Table 5: The feature selection performances (in percentage).

Organ

A B C D

Acc. Sen. Spe. Acc. Sen. Spe. Acc. Sen. Spe. Acc. Sen. Spe.

respirat 86.3 72.4 90.2 86.2 70.0 90.8 77.9 4.4 98.8 77.6 1.8 99.4

coagulat 97.4 68.8 98.7 97.3 59.6 99.0 95.8 4.6 99.9 95.7 0.0 100

liver 98.3 68.6 99.1 98.3 60.2 99.4 97.3 7.6 99.9 97.3 0.0 100

cardiova 94.2 84.1 96.3 94.2 84.0 96.3 82.8 7.5 99.0 82.2 0.5 99.8

cns 95.7 92.7 96.4 95.7 92.3 96.4 83.5 23.4 97.1 81.6 0.4 99.9

renal 95.5 71.3 97.8 95.3 66.6 98.1 91.4 5.7 99.7 91.1 0.3 100

Mean 94.6 76.3 96.4 94.5 72.1 96.7 88.1 8.9 99.1 87.6 0.5 99.96

Acc. - Accuracy, Sen. - Sensitivity, Spe - Speciﬁcity.

Table 6: The balanced C, D and C improved performances (in percentage).

Organ

C D C (improved)

Acc. Sen. Spe. Acc. Sen. Spe. Acc. Sen. Spe.

respirat 61.3 66.4 59.8 67.1 41.1 74.5 63.3 70.4 61.3

coagulat 67.6 66.8 67.7 73.7 41.5 75.1 70.0 72.0 69.9

liver 70.0 71.6 70.0 66.9 36.5 67.8 72.5 77.3 72.4

cardiova 65.9 62.5 66.7 68.2 37.9 74.8 69.1 66.3 69.8

cns 73.6 63.9 75.7 66.8 36.3 73.7 75.2 72.2 75.8

renal 67.8 65.6 68.0 73.2 37.6 76.6 71.9 70.5 72.0

Mean 67.7 66.2 68.0 69.3 38.5 73.8 70.3 71.5 70.2

Acc. - Accuracy, Sen. - Sensitivity, Spe - Speciﬁcity.

Table 7: The classiﬁers performances for the C selection balanced data (in percentage).

Organ

Naive Bayes JRIP Neural Networks

Acc. Sen. Spe. Acc. Sen. Spe. Acc. Sen. Spe.

respirat 73.5 25.2 87.3 62.8 61.9 63.0 63.3 70.4 61.3

coagulat 83.3 24.8 85.8 67.8 62.4 68.0 70.0 72.0 69.9

liver 70.8 54.3 71.2 75.7 73.7 75.7 72.5 77.3 72.4

cardiova 73.4 33.4 82.0 66.6 70.3 65.8 69.1 66.3 69.8

cns 76.3 41.3 84.2 77.6 74.4 78.3 75.2 72.2 75.8

renal 76.8 45.6 79.9 69.1 68.5 69.2 71.9 70.5 72.0

Mean 75.7 37.4 81.7 69.9 68.5 70.15 70.3 71.5 70.2

Acc. - Accuracy, Sen. - Sensitivity, Spe - Speciﬁcity.

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406

5000 10000 15000 20000 25000 0

respirat

coagulat

liver

cardinova

cns

renal

Figure 3: The organ failure true (in black) and false (in white) proportions.

to improve the use of the latter outcomes. First, the

training sets were balanced to contain similar propor-

tions of positive and negative examples. Then, the

number of hidden nodes and training epochs was in-

creased. As the result of these changes, an improved

performance was gained, specially in terms of sensi-

tivity.

A ﬁnal comparison between the SOFA score and

the proposed solution (the C improved setup), still fa-

vors the former, although the Sensitivity values are

close (being even higher for the C conﬁguration in

the coagulation and liver systems). Nevertheless, it

is important to stress the main goal of this work: to

show that is it possible to diagnose organ failure by

using cheap and fast intermediate outcomes (within

our knowledge this is done for the ﬁrst time). The re-

sults so far obtained give an overall accuracy of 70%,

which although not authoritative, still back this claim.

In addiction, the proposed approach opens room for

the development of automatic tools for clinical de-

cision support, which are expected to enhance the

physician response.

In future research it is intend to improve the perfor-

mances, by exploring different Neural Network types,

such as Radial Basis Functions (Bishop, 1995). An-

other interesting direction is based in the use of alter-

native Neural Network training algorithms, which can

optimize other learning functions (e.g., Evolutionary

Algorithms (Rocha et al., 2003)), since the gradient-

based methods (e.g., the RPROP (Riedmiller, 1994))

work by minimizing the Sum Squared Error, a target

which does not necessarily correspond to maximiz-

ing the Sensitivity and Speciﬁcity rates. Finally, it is

intended to enlarge the experiments to other ICU ap-

plications (e.g., predicting life expectancy).

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