Engineering, 2013, 5, 584-589
http://dx.doi.org/10.4236/eng.2013.510B120 Published Online October 2013 (http://www.scirp.org/journal/eng)
Recursive Fuzzy Predictions of Future Patient Paths to
Support Clinical Decision Making in ICU
A. Zeghbib1, M. Mahfouf1, J. J. Ross2, G. H. Mills2, G. Panoutsos1, M. Denai3, S. Suzani1
1Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, UK
2Northern General Hospital, S heffield, UK
3Teesside University, Middlesbrough, UK
Received 2013
ABSTRACT
In this paper, we propose a new architecture that combines prediction and decision-making in the form of a hybrid
framework aimed at providing clinicians with transparent and accurate maps, or charts, to guide and to support treat-
ment decisions, and to interrogate the clinical patients’ course as it develops. These maps should be patient-specific,
with options displayed of possible treatment pathways. They would suggest the optimal care pathways, and the shortest
routes to the mo st efficient ca re, by predicting clinical progress, testing the ensuing suggestions against the developing
clinical state and patient condition, and suggesting new options as necessary. These maps should also mine an extensive
database of accumulated patient data, modelled diseases, and modelled patient-responses based on expert-der ived rule s.
These individualized hierarchical targets, which are implemented in order to prevent life-threatenin g illnesses, will also
have to “adapt” to the patient’s altering clinical co ndition. Therap ies that support one system can destabilize others and
selecting which specific suppo rt to pr ioritize is an uncertain p rocess, the pr ioritizatio n of whic h can var y between cli ni-
cal experts. Whilst clinical therapeutic decisions can be made with some degree of anticipation of the “likely” outcome
(based on the experts’ opinion and judgment), treatment is essentially rooted in the present, and is dependent on ana-
lyzing the current clinical condition and available data. The recursive learning approach presented in this paper , allows
decision rules to predict the possible future course, and reflects back derived information from such projections to the
present time and thus support proactive clinical care rather than reactive clinical care. The proposed framework for such
a patient map supports and enables an optimized choice from available options and also ensures that decisions are based
on both the available evidence and a database of best clinical practice. Preliminary results are encouraging and it is
hoped to validate the approach clinically in the near future.
Keywords: Proactive Treatment, Clinical Dec ision, Intensive-Care, Patient-Paths, Physiological Map
1. Introduction
Preliminary studies on physiological patient state classi-
fication and patient map elaboration consists of: a) col-
lecting patients’ data from each subsystem, such as the
heart, the lungs, the renal system, etc.; b) the Laboratory
results; c) and medications. Such clinical information
allows the identification of the current patient state, but
does not provide information about the future patient
state. [1,2] are study examples that consider the set of
vital parameters from the cardio-vascular system (CVS)
and respiratory system. For the respiratory and gas ex-
change systems there are many different models devel-
ope d to de al with ga s exc hang e in t he lun gs [3,4]. Hence,
other approaches have been developed as simulators to
describe the ventilation and the gas exchange interactions
[5,6] . For a lumped system o f arterial, tiss ue, venous and
pulmonary compartme nts, the SOPAVent ( Simulation of
Patient under Artificial Ventilation) model has also been
developed to simulate the exchange of O2 and CO2 i n t he
lungs and tissues togethe r with their transport through the
circulatory system [7,8]. Further improvements of the
original, SOPAVent model, have also been included to
develop a none-invasive model structure as well as a
continuously updated model to improve patient-specific
model performance and improve the prediction accuracy.
A mor e rece nt stud y [9] combined a model of respiratory
mechanics, a model of the human lung absolute resistiv-
ity and a 2-D finite-element mesh of the thorax to simu-
late EIT image reconstr uction during mec hanical ventila-
tion.
The goals for treating critically-ill patients in Intensi ve
Care Units are, of necessity, patient specific. A Clinical
Decision Support System to optimize a patient’s care
would ideally have the following features: 1) function as

A. ZEGHB IB ET AL.
585
a ‘virtual Star Chamber’ pooling all available expertise
of the clinical staff in that Unit; 2) use this wealth of
knowledge to derive current treatment for an individual
patient via data bases which can ideally be interrogated;
3) use data-mining tools to categorize the current patient
condition; 4) integrate current treatment protocols into
real-time care; 5) exploit a predictive function to model
and thus pred ict the clinical c ourse of the patien t into the
future resulting from this care; 6) Test, “off -line”, the
consequences of the current treatment actions; 7) Finally,
interrogate the generated data from the patient to check
conformity with the predicted outcome
In the Intensive Care environment, clinical decisions
are made to maintain patients’ physiological parameters
within acceptable (safe) ranges whilst treating or improv-
ing the underlying illne ss. Clinicians rely on their kno w-
ledge and experience to plan appropriate therapy rules.
These are applied to the developing clinical condition
and the outcomes revisited and alterations considered for
implementatio n.
Selecting the most appropriate treatment package from
differing options raises the possibility of potentially di-
verging and conflicting clinical decisions, or therapy
rules, and that selecting one path will engender a devel-
oping and necessarily diverging clinical course for that
patient. T his dilemma requires that t he choices should b e
clearly identified and that any model-based method must
be able to predict future consequences for each choice.
However, clinical decisions entail a degree of uncertainty
and do not have a clearly mapped outcome for the con-
sequences. If a clinician has a patient with a diagnosis
“D”, the therapeutic choices can be T1, T2, or T3. Ther-
apy “T1” may induce a complication and cause the pa-
tient to deteriorate. Therapeutic decision “T2” may im-
prove the state of t he patient, or may not chan ge it. “T3”
may treat t he diagno sis D, but entail colla teral da mage in
other organs. Thus the clinician faces considerable un-
certainty. A map detailing clear future outcomes in all
possibilities for patients’ anticipated clinical recovery
paths will be useful undoubtedly.
The decision-support map would provide clinicians
predicted pathways for multiple possible patient- states,
until the patient enters a final state of stability with nor-
malized values for all monitored physiological parame-
ters. This stable state would in effect represent, in terms
of dynamic systems, the so called equilibrium state.
In the framework proposed in this paper, each gener-
ated node of the path displays two types of information.
The first describes the current values of physiological
parameters as concept variables, and the second de-
scribes the drug that causes these concept variables to
evolve according a certain trend. The physiological pa-
rameters are calculated by reflecting the connective inte-
ractions of the variables within each node and between
the nodes, which thus functions as part of a dynamical
biological system. Thus, the interaction between clinical
concepts should keep a stable equilibrium within time or
continuous until reach the equilibrium cycle, depending
on the initial patient state and the expert knowledge da-
ta-base.
The displayed options of possible treatment pathways
support a clinical proactive decision making. This model
has two principle components: the first component is the
State Transition Predictor (STP), and the second is the
Patient Paths Network (PPN). The first component is an
expert knowledge data-base of the basic clinical rules for
different possible patient states based on a number of
clinical concepts to be observed. The number of clinical
concepts is determined by a possibility that a clinical
outcome would in effect take place. The remainder of
this paper will be organized as follows: Section 2 will
detail the “recursive” concept as well as the associated
algorithms of the physiological patient map. Section 3
will explain how the cause-effect relationship within pa-
tient physiological parameters can be represented via a
fuzzy cognitive map. Finally, conclusions relating to the
proposed study together with future research directions
will be given in Se ction 4.
2. Physiological Patient Map
In order to populate the physiological patient map, we
reconceptualise clinical conditions from general terms
such a s “critical”, “stable” “mild”, etc.; via fuz z y l i ng ui s-
tic terms describing the values of the physiological pa-
rameters. Each parameter is described in one of three
fuzzy linguistic terms: Low (L), Medium (M), and High
(H). For m clinical concepts (i.e. physiological parame-
ters), the nodes (i.e. states) of p atient paths are expressed
as follows:
Let us consider a vector of m variables:
( ) { }
,1 ,
,?
kk km
yty y
(1)
( )
{ }
, 1,Fuzzy Linguistic Terms
,,,:state index
kim
y
LMHk
=
∈
We argue here that there are two components (Mod-
ules) of our Physiological patient map: state transition
predictor (STP) and patient path network (PPN), see
“Figure 1”. The next sections will expand on these two
modules:
2.1. State Transition Predictor (STP)
The STP has two inputs, the first input is represented by
clinical concepts,
()
k
yt
, and the second input is repre-
sented by drugs, i.e.
( ) { }
,1 ,
,,:drug vector dimension
kk kn
xt xx n=
(2)

A. ZEGHB IB ET AL.
586
The outputs are the new clinical concepts formulated
also via fuzzy linguistic terms based on the previous two
vector inputs x(t) and y(t). Hence, the output vector will
be as fo l lows:
( )( ) ( )
( 1),
k kk
YtFxtyt+=
. (3)
Thus the input matrix
( )( )
,xt yt
 ′′


generates a new
output vector as follows:
( ) { }
{ }
,1 ,
, 1,,1,
1, ;
,.
kkk Mj
kjMjkk m
YtY Y
Y yy
=
+=
=


(4)
The dimension, Mj, of the victor
( )
1
k
Yt+
depends
on the state i ndex, k. T he stat e transition predictor (STP)
represents our data-base of expert knowledge.
2.2. Patient Path Network (PPN)
This module has the specific task of memorising all the
predicted states from the initial recorded state until the
final predicted stable state and builds the network of all
the generated states. Furthermore, the simulation run of
this module is completed to extract all possible outcome
paths that the patient clinical state may follow, “Figure
2”.
The nodes of each path indicate the physiological pa-
rameter values in fuzzy linguistic terms and the drugs
that should be administrated in order to reach the next
transition state. Here we consider the recording of only
four physiological parameters; (in an intensive care en-
vironment of a patient with cardio-respiratory system
failure): Cardiac Output (CO) is low (L), Mean Arterial
Pressure (MAP) is low (L), mean airway pressure (PaO2)
Figure 1 . Fuzzy linguistic ter ms ; Low, Me diu m, a n d Hi g h; of t he four phy s iolog ic al par a me te r s ( cl ini c al c o nce pt s) C O, M A P,
PaO2, and CVP. The x-axis represents real values of these parameters.
Figure 2 . Phys iological patient map (PPM). T he re are two module s.
02468 10 12 14 16 1820
0
0. 5
1
Low Medium High
CO(L/min)
CO
050100 150
0
0. 5
1
Low Medium High
MAP(mmHg)
MAP
051015 20 25 30
0
0. 5
1
Low MediumHigh
PaO2(mm Hg)
PaO 2
0 1 2 3 45 678910
0
0. 5
1
Low Medium High
CVP ( mmHg)
CVP
State State
𝑌(𝑡+1)=𝐹(𝑥(𝑡),𝑦(𝑡))
Paths

A. ZEGHB IB ET AL.
587
is low (L), and Central Venous Pressure (CVP) is high
(H). Thus the recorded patient initial state was indicated
in the model (see “Figure 3”) as [LLLH]. The physio-
logical patient map model in this case will generate the
successive possible states of this patient in the form of
paths network. The procedure is only stopped under two
conditions: the first is when all the newly created states
(nodes) reach the stable state [MMMM] or [HMMM],
the second condition relates to when the newly state
(node) has already been created, similarly to the case
illustrated by “Figure 3”, i.e. the state [MMMH].
Patient paths (trajectories) network nodes indicate the
physiological parameter values in fuzzy linguistic terms
and the drug input that should be administrated to reach
the next transition state. There are five types of drugs
used by the model to generate this paths-based network;
N: noradrenaline, D: dobutamine, G: GTN, F: fluid, O:
oxygen, and F: Fluid. These drugs are described also via
two fuzzy linguistic terms: “p: positive” to increase the
drug, and “l: less” to decr ease the drug dosage. Fr om t he
top-node of the initial recorded patient state, the model
progresses recursively creating successor nodes and
paths until reaching the equilibrium state of each patient
path.
In the particular (realistic) example of “Figure 3”, the
patient map includes 12 possible physiological patient
paths generated with this model. Section 3 will expand
on the physiological parameters evolution within a spe-
cific path, in order to explain the successive nodes
created from the model and their convergence to the sta-
ble state (eq uilib rium).
3. Physiological Parameters Evolution in
Cognitive Map
The cause-effect relationship within the patient physio-
logical parameters is represented as a fuzzy cognitive
map [10]. We argue that this graphical representation
may be the ideal tool for reasoning with uncertainty.
The path nodes include “9” co ncepts; fo ur “4” of which
are causal concepts representing the patient physiological
parameters. These concepts are, as is the case of in any
dynamic system, in permanent interactions within cause -
effect relationships. The valuations of these cause-effect
relationships; from the experts; are carried-out indirectly
through the drug concept values. The five “5” other con-
cepts are drugs; their edges are directed only in one di-
rection onto the physiological parameters, as illustrated
in “Figure 4”. These are the main considered variables
for building the physiological patient map.
The predicted evolution of physiological parameters
infers different possible patient trajectories in a clear
physiological map. T he illustra tion of one tra je ctory from
the trajectories map of “Figure 4” can help to get to grips
with the mechanism used by predicted clinical concepts
interactions to build the successive future patient out-
comes, as shown in “Figure 5”. The model operates ite-
ratively, because at each time interval the new concept
node values are predicted using the previous node con-
cept values.
Figure 3 . Network of physiological parameters an d diff erent possi bl e patient paths prediction.

A. ZEGHB IB ET AL.
588
Figure 4. Fuzzy cognitive map, within each node of the pa-
tient pat h, for the ca use-effect relationship of patient physi-
ological parameters and drugs. The drugs have unideric-
tional effects. Drug types used in this study are; N: nora-
drenaline, D: dobutamine, G: GTN, F: fluid, O2: oxygen,
and F: Fl ui d. T he map he re uses no de s ha pes r el at i ng to t he
concept type. Drug concepts are circles, physiological pa-
rameters are el ipses.
Figure 5 . Pat ient pat h evol ut ion f ro m the phy sio logi cal map .
This path has seven “7” physiological states (nodes). The
initial state is [LLLH] and the outcome state is [HMMM],
which is the final patient stable state. We observe the con-
tinous physiological parameters evolution in the same time,
parallel way, until the system reaches its equilibrium state.
The path chosen from the map of “Fig ur e 3” includes
seven “7” physiological states (nodes), the initial state
being [LLLH] and the outcome state [HMMM] being the
final predicted patient stable state. In this path all physi-
ological variables change continuously until the system
reaches its equilibrium state. The convergence of a phy-
siological dynamic system towards a stable state is only
possible because of the available expert knowledge re-
lating to the biolo gical s ystem b ehaviour. H o wever, these
paths can also diverge and it may be possible that they
will never reach the “equilibr ium” state.
4. Conclusion
The proposed physiological patient map model as de-
tailed in this study has ma ny advantages for the clinicians
to deal with patients in intensive care unit. Usually, clin-
ical decisions are based on clinical assessment and sub-
jective judgment of clinicians. The clinicians’ judgments
are only reactive actions because the usual existent sys-
tems of assessment cannot provide the possible outcome
set o f the future scenarios of ph ysiological patie nt states.
This model provides the clinicians transparent map, based
on their expert-knowledge, of future patient paths that
will be conveyed back to the current planning context in
order to support proactive clinical actions rather than just
reactive actions. This map allows, in terms of profes-
sional accountability, a real choice of the best decision
and also ensures that decisions are based on available
evidence. However as this model is based on the fuzzy
sets of clinical concepts, the increase of recorded physi-
ological parameters and drugs will make the physiologi-
cal patient map more complex and also raises the patient
paths number but it is believed that as long as the map
itself is systematically built, accuracy and most impor-
tantly transparency will still be conserved. Immediate
future plans will include validation of this map concept
on a simulated patient platform, but our “not so imme-
diate” research plan will include two vectors: the first
one is to introduce variations of the physiological para-
meters to test the generalization properties of the overall
framework as to its quality of control and to path con-
vergence; the second will provide this model with new
tools using probability and possibility theories both for
control and path selection. Both data and knowledge fu-
sion, especially in the case of information conflicts, will
also be a pertinent issue to resolve.
5. Ackno wledgements
The authors wish to acknowledge financial support for this
work fro m the UK-EPSRC under Grant EP/FO2889X/l.
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