Advances in the Decision Making for Treatments of Chronic Patients

Using Fuzzy Logic and Data Mining Techniques

M. Domínguez

, J. Aroba

, J. G. Enríquez

, I. Ramos

, J. M. Lucena-Soto

and M. J. Escalona

Web Engineering and Early Testing (IWT2) Research Group, University of Seville, Av. Reina Mercedes s/n, Seville, Spain

Departamento de Tecnologías de la Información, Escuela Técnica Superior de Ingeniería, University of Huelva,

Carretera Huelva-Palos de la Frontera s/n, 21819 La Rábida-Palos de la Frontera, Huelva, Spain

Hospital Universitario Virgen del Rocío, Avda. Manuel Siurot s/n - 41013 Seville, Spain

Keywords: HIV, Virological Events, Prefurge, Unsupervised Learning.

Abstract: Virological events in HIV-infected patients can rise with no apparent reason. Therefore, when they appear,

immunologists or medical doctors do not know whether they will produce other future virological events or

they will entail relevant clinical consequences. This paper presents the results of applying Prefurge to HIV-

infected patients’ clinical data, with the aim of obtaining rules and information about this set of clinical

trials data that will relate these kinds of virological events.

1 INTRODUCTION

Data mining (extracting hidden and predictable

information from big databases) has a huge potential

to help companies get meaningful information from

their databases. Tools based on this technology

allow predicting future tendencies and behaviours,

focused on this stored knowledge. Automated

prospective analyses, which are obtained through

this technology, go further than the knowledge

provided by classical support to decision making

tools. These new tools can answer some questions

that will take us long time to response, if they do not

exist. Such tools explore the databases looking for

hidden patterns, extracting predictable information

that cannot be found by means of classical

mechanisms (lessons learned or traditional tools)

utilized by an expert.

Prefurge (Aroba, 2003) is a data mining tool

dealing with fuzzy clustering, whose methodology is

based on Sugeno and Yasukawa’s (Sugeno and

Yasukawa, 1993) proposal. Such proposal has been

modified and adapted to be able to apply it to multi-

parametric quantitative databases with more than a

unique output variable. Therefore, Prefurge fits in

the context of Unsupervised Learning, which means

that it does not completely need a tag that

determines a record class. This is an important

detail, since an expert executes this process using

experience-based criteria that highly influence the

obtained rules.

In this paper, we apply Prefurge to a medical

database of Virgen del Rocío University Hospital

(composed of approximately 300,000 clinical trials

from 5,000 patients who are being treated at this

hospital) with the aim of reducing costs and the time

spent on their treatments as well as improving their

quality of life.

This paper has been structured as follows: in

section 2 (Related Works), we show some works

where Prefurge was successfully used in different

knowledge areas; in section 3 (HIV Patients), we

reflect on the motivation that make us elaborate this

paper and we describe patients’ parameters and the

context where this paper is developed; in section 4

(Intelligent Data Analysis), we study the used

database and its pre-processing in order to apply

Prefurge, together with the analysis of the obtained

results. Finally, section 5 states conclusions and

future work to keep on working on this research line

and applying Prefurge to different patients.

2 RELATED WORKS

Since it was developed as a result of a PH-thesis,

325

Domínguez M., Aroba J., G. Enríquez J., Ramos I., M. Lucena-Soto J. and J. Escalona M..

Advances in the Decision Making for Treatments of Chronic Patients Using Fuzzy Logic and Data Mining Techniques.

DOI: 10.5220/0004969503250330

In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 325-330

ISBN: 978-989-758-027-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Prefurge (Aroba, 2003) has allowed discovering

new and very relevant information (and sometimes

unknown), using datasets coming from several and

diverse contexts. All the obtained results have been

validated by experts from different contexts who

have confirmed the veracity and importance of the

qualitative information generated by this tool. Some

of the results obtained have led to publications in

indexed journals of first level, such as (Aroba et al.,

2008), (Gegúndez et al., 2008), (Grande et al.,

2010a), (Grande et al., 2010b), (Francisco de Toro et

al., 2011).

A segmented software cost estimation model

based on fuzzy clustering is proposed in (Aroba et

al., 2008). The use of fuzzy clustering allows

obtaining different mathematical models for each

cluster and also enables that the items of a project

database can contribute with more than one cluster,

while preserving constant time execution of the

estimation process. The results of an evaluation of a

concrete model using the ISBSG 8 project database

are reported, yielding better figures of adjustment

than its crisp counterpart.

An identification method for a class of dynamic

system, known as piecewise affine system, is

presented in (Gegúndez et al., 2008). Such systems

are composed of a set of affine maps which relate

inputs and outputs.

The aim of the proposed method

is to obtain a model of the system from a set of

input-output data. The method uses a process of

fuzzy clustering in order to obtain a subset of

representatives from the original data set, so

reducing the amount of information to be processed,

while retaining the significant information from the

original data and minimizing the effect of noise on

such data.

A qualitative fuzzy model is proposed in (Grande

et al., 2010a) to help characterizing and interpreting

the behaviour of arsenic in a complex system

submitted to acid mine drainage processes (Tinto

river). The conclusions present that the factors that

most directly control the presence of total dissolved

arsenic are temperature and rainfall, and therefore,

pH.

The main objective of (Grande et al., 2010b) is to

characterize the behaviour of arsenic conditioned by

the presence of other elements which are

characteristic of acid mine drainage pollution, both

in generating milieu and receiving milieu of the

Tinto river. The paper analyzes the different

behaviours arsenic assumes at both sites, as well as

its relationships with the rest of the elements.

Fuzzy logic tools have been also used in others

sanitary contexts, such e.g. Computer-Aided

Diagnosis (CAD) of the paroxysmal atrial

fibrillation (Francisco de Toro et al., 2011), where

this fuzzy logic tool helped other evolutionary

methodologies, generating rules that can be easily

used by medical specialists.

3 HIV PATIENTS

Antiretroviral treatments (ART) against HIV have

reached a high success rate in infected patients. The

goal of ART is to eliminate the viral particle in

plasma limiting its infectivity (viral suppression).

HVI RNA, quantified by real time RT-PCR, in

plasma (viral loads) and CD4+ T cell count in

peripheral blood are the most important response

marks to ART.

Periodic monitoring is necessary, once viral

suppression is achieved. In fact, positive viral loads

events are detectable. Depending on the duration and

viral load concentration of these events they may

have clinical implications. The most common events

are blips, generally defined as single, low-level viral

load measurements that are followed by a return to

virologic suppression. Obviously, the increased

sensitivity of the technique has led to an increased

frequency of blips. Nowadays, RT-PCR real time

technique is able to detect 20 copies/ml (COBAS

AmpliPrep/COBAS

TaqMan® HIV-1 Test). The

causes and clinical implications of blips have been

debated. Firstly, some blips may be associated to

biological variability and test variability (±0.5 log

)

(Bryan et al., 2011). Although some studies

associate blips occurrences with a higher frequency

of relapse (Greub et al., 2002), most studies have

confirmed that blips do not result in increased risk of

virologic or clinical failure (García-Gasco et al.,

2008), (Nettles et al., 2005).

Since we have a large number of samples, it

would be useful to establish whether blips relate or

not to subsequent clinical changes in patients.

4 INTELLIGENT DATA

ANALYSIS

4.1 Source Data

Virgen del Rocío University Hospital has provided

us with the data collection that we are going to

process. Such data sampling contains around

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300,000 clinical trials of more than 5,000 patients

that are being treated at this hospital.

These data are recorded in a spreadsheet whose

rows include the following type of data:

 Date of the clinical trial.

 Clinical History Identification (CHI), a number

that uniquely identifies each patient.

 Age of the patient.

 Clinical Trial Request (CTR), a number that

exclusively identifies each request for one or

more clinical trials.

 Clinical Trial Code (CTC), a number that

individually identifies each clinical trial.

 Clinical Trial Description, which can be one of

the following: Viral Load, Lymphocyte Count,

CD3+T-cells, CD4+T-cells or CD8+T-cells.

 Clinical trial result.

 Comments on the results.

Every single clinical trial and its results have been

previously anonymised to protect the health privacy

of each patient.

Data Preparation

The given data collection needs an early processing

with the aim of correcting each wrong, void or out-

ranged detail, so we removed each invalid row and

setting a maximum threshold of detectable viral load

in 20 copies/mL, although DHHS defines virologic

suppression below the limit of assay detection

between 20 and 75 copies/mL, in order to maximize

the number of detected blips. (Aroba, 2003) These

corrections make a right input for Prefurge, and

reduce the data sampling to 250,000 clinical trials

and 2,500 distinct patients.

Data Generation

There are some data that are susceptible of being

sets of antecedents or consequences and therefore,

they can be processed by Prefurge.

However, they are not the only data we want to

process, but we are also interested in the connection

among these virology events (U.S. Dep.,

www.hhs.gov):

 Virologic Suppression: Two or more confirmed

HIV RNA levels (copies/mL) in a row below

the limit of assay detection.

 Virologic Blip or Blip: After a virologic

suppression, an isolated detectable HIV RNA

level (copies/mL) that is followed by a return

to a virologic suppression.

 Incomplete Virologic Response: Two

consecutive plasma HIV RNA (copies/mL)

confirmed positive levels.

 Persistent low-level viremia: Three or more

consecutive plasma HIV RNA (copies/mL)

confirmed positive levels.

 Virologic failure: A high detectable HIV RNA

level (over 1,000 copies/mL in this study).

 Time between each trial: Number of days spent

from a clinical trial to the next one.

These data have been calculated from the Viral

Load (VL) results for each patient, and added to the

initial data set, in order to be processed by Prefurge

in the same way that the other data.

It seems that calculated data are not going to

provide new information, as they are calculated in

terms of the frequency of changes on viral load

values. Therefore, it is not direct related information.

4.2 Application of Data Mining

Techniques

Prefurge (Predictive Fuzzy Rules Generator)

It is important to note that the graphic output

provided by Prefurge enables an easy interpretation

of the fuzzy rules in a natural language. As an

example, Figure 1 shows a rule generated by

Prefurge. In rule (a) of Figure 1, the fuzzy set

assigned to each parameter is represented by a

polyhedron. The parameter values are represented on

the x axis of each fuzzy set, and the value of

membership to a cluster on the y axis. This fuzzy

rule would be interpreted as follows: “IF Age is

small and VL is bigger or equal to the average

THEN Blips are very small”

Figure 1: Example rule.

Due to the discrete nature of some parameters

that we will process, fuzzy sets generated in some

rules, represent discrete ranges of values, instead

quantitative values such as big or small.

Experiments Design

Data input must have a specific structure to be able

to be processed by Prefurge. This concrete data input

has been structured by creating an initial spreadsheet

whose columns contain an antecedent or a

consequence.

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Differentiating useful data is the first step

involving ignoring the useless data; thus, we will

only use clinical related data:

 Date. It is necessary to calculate the days spent

from one to the subsequent virologic event.

 Age. It is a clinical parameter.

 Result. It refers to the main clinical parameters

results on the clinical trials:

o Viral Load.

o Lymphocyte Count.

o CD4+T-cells

 Virologic blip or ‘blip’.

 Incomplete virologic response.

 Persistent low-level viremia.

 Virologic failure.

 Time between each trial (TBT), which can be

another useful parameter.

Some data can take very different ranges in their

values so they have been normalized, when needed.

Experiments with New Parameters

There are a lot of parameters that we can use as

input to Prefurge, but which of them are really

interesting? Which of them should we use? To

answer these questions we must start with the most

basic parameter, Viral Load, since it constitutes the

source of any forward virologic event. We can use

Viral Load as the first and main antecedent of any

rule.

Moreover, a blip directly depends on Viral Load,

so we can use it as the first consequence of a rule.

Furthermore, we can add any clinical parameter as

an antecedent, since we do not know if it will show

any extra result previously.

We can design the first experiment with this

information:

Experiment #1

 Antecedents: Age, TBT, Viral Load.

 Consequents: Blips.

Following the same way of thinking, we notice

that an incomplete virologic response needs a

previous blip to get raised; therefore we can use the

blips as antecedents of the consequent incomplete

virologic response.

Experiment #2

 Antecedents: Age, TBT, Viral Load, Blips.

 Consequences: Incomplete Virologic

Response.

Persistent low-level viremia appears when a blip

raises becoming an incomplete virologic response.

Thus, we can consider it as an antecedent of a

Persistent low-level viremia for a new experiment.

Experiment #3

 Antecedents: Age, TBT, Viral Load, Blips,

Incomplete Virologic Response.

 Consequents: Persistent Low-level Viremia.

These events may indicate that the treatment is

failing (Aldous and Haubrich, 2009), the virus is

mutating or the expression of proviral DNA

integrated in lately infected cells (García-Gasco et

al., 2008), whence a virologic failure can appear. In

this case, a virologic failure could be treated as a

consequence of the rest of the virologic events.

Experiment #4

 Antecedents: Age, TBT, Viral Load, Blips,

Incomplete Virologic Response, Persistent

low-level viremia.

 Consequences: Virologic Failure.

Experiment #1 Results

Figure 2: Experiment #1 Results: From top to bottom,

Rules #1, #2, #3, and #4.

Experiment #2 Results

Figure 3: Experiment #2 Results Rule #1.

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Experiment #3 Results

Figure 4: Experiment #3 Results: From top to bottom,

Rules #1 and #2.

Experiment #4 Results

Figure 5: Experiment #4 Results: From top to bottom,

Rules #1 and #2.

4.3 Analysis

The first step in the experiments results is to remove

the rules whose antecedents do not discriminate any

range of values. In that case, any antecedent value

can produce the consequence, so it does not provide

us with any useful information.

Experiment #1 Analysis

In Exp #1, Rule #2, Rule #3 and Rule #4 show a link

between Age and Blips, as the lowest age range

seems to be linked to more blips, but unfortunately

this relation is caused by the number of records in

that age range are the most in the sample; therefore

the number of blips detected is higher.

Experiment #2 Analysis

Rule #1 antecedent blips take all the possible values

in its range, so this rule does not reveal any useful

information.

Experiment #3 Analysis

Rule #1 and Rule #2 antecedents, such as Time

between trials, Viral Load, Age or Blips do not

provide any useful information because their values

have very similar ranges.

Nevertheless, Rule #1 shows how the absence of

any Incomplete Virologic Response antecedent is

related to the presence of Persistent low-level

viremia. Rule #2 shows how the presence of

Incomplete Virologic Response antecedent is also

associated with that presence of Persistent low-level

viremia. This demonstrates that Incomplete

Virologic Response is completely independent of

Persistent low-level viremia, as it can appear without

any related previous Incomplete Virologic

Response.

Experiment #4 Analysis

Rule #1 and Rule #2 show that higher Viral Load

values relate to less Virologic Failures, but a deeper

analysis of data shows that Rule #1 concerns to

patients who have null or very low values on their

Viral Load for a long time, that is showed as lower

Viral Load in Rule #1.

4.4 Learned Lessons

These results can show us that we must not stop

working on a set of data, even if it has been

processed many times in many ways; because when

we use non-classical statistical methods (such

Prefurge) we can take out important hidden new

information from the data set.

5 CONCLUSIONS AND FUTURE

WORK

One of the most important results rated by the expert

team has been meeting the independence between

the Incomplete Virologic Response and Persistent

low-level viremia. It allows researchers to focus

their efforts on other parameters or setting new

conditions to their experiments, trying to get

different results from these found right now. This

situation may even cause that other related research

projects using the parameters that have been

processed could be developed more quickly.

Analysing the remaining results, we have

realized that we have not found any new relation

between some parameters studied and the

knowledge that we have of them up to now. The

causes of this gap perhaps may be explained by this

discrete nature of most parameters, since Prefurge is

a data mining tool designed for continuous and

numerical sets of data. However, we could even

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process discrete attributes (e.g. Blips) generating

some meaningful information, although its

interpretation is somehow different: antecedents and

consequences are no longer big or small values, but

discrete values as, for instance, zero blips, one blip,

two blips, and so on.

Despite these difficulties, this work lays the

foundations for developing a research line focused

on the use of Prefurge with HIV chronic patients,

which provides health researchers with tools and

methodologies to process this type of biohazardous

data. This possible solution will help scientists and

millions of people all over the world.

One of our most priorities regarding future work

will be testing all these experiments with another

algorithm to check how Prefurge really behaves. As

soon as we totally confirm that Prefurge is our best

tool, we can improve it with new functionalities

according to our main needs.

ACKNOWLEDGEMENTS

This research has been supported by MeGUS of the

Ministerio de Ciencia e Innovación and by NDTQ-

Framework project (TIC-5789) of Junta de

Andalucía, Spain.

REFERENCES

Aroba, J. Advances in the decision making in software

development projects. PhD thesis, University of

Sevilla, Spain, 2003.

Aroba,J., Cuadrado, J., Sicilia, M., Ramos, I., García, E.

2008. Segmented software Cost Estimation Models

based on fuzzy clustering. In Journal of Software and

Systems

Gegúndez, M., Aroba, J., Bravo, J. 2008. Identification of

piecewise affine systems by means of fuzzy clustering

and competitive learning. In Journal of Engineering

Applications of Artificial Intelligence

Grande, J., Andújar, J., Aroba, J., de la Torre, M. 2010a.

Presence of As in the fluvial network due to AMD

processes in the Riotinto mining area (SW Spain): A

fuzzy logic qualitative model. In Journal of

Hazardous Materials

Grande, J., Andújar, J., Aroba, J., Belrán, R., de la Torre,

M., Cerón, J., Gómez, T. 2010b. Fuzzy Modeling of

the Spatial Evolution of the Chemistry in the Tinto

River (SW Spain). In Journal of Water Resource

Management

U.S. Department of Health and Human Services

http://www.hhs.gov/

Aldous, J., & Haubrich, R. (2009). Defining Treatment

Failure in Resource-Rich Settings. Curr Opin HIV

AIDS, 459-466.

García-Gasco, P et al. Episodes of low-level viral rebound

in HIV-infected patients on antiretroviral therapy:

frequency, predictors and outcome. Journal of

Antimicrobial Chemotherapy (2008) 61, 699–704

M. Sugeno, T. Yasukawa. A Fuzzy-Logic Based approach

to qualitative Modeling. IEEE Transactions on Fuzzy

Systems, Vol.1.Pp.: 7-31, 1993.

Bryan R. Cobb, Jeffrey E. Vaks, Tri Do, Regis A. Vilchez-

Evolution in the sensitivity of quantitative HIV-1 viral

load tests. Journal of Clinical Virology 52S (2011)

S77– S82

Greub G, Cozzi-Lepri A, Ledergerber B, Staszewski S,

Perrin L, Miller V, Francioli P, Furrer H, Battegay M,

Vernazza P, Bernasconi E, Günthard HF, Hirschel

B,Phillips AN, Telenti A; Frankfurt HIV Clinic Cohort

and the Swiss HIV Cohort Study. Intermittent and

sustained low-level HIV viral rebound in patients

receiving potent antiretroviral therapy. AIDS: Volume

16(14), 27 September 2002, pp 1967-1969

Nettles et al. Intermittent HIV-1 Viremia (Blips) and Drug

Resistance in Patients Receiving HAART. JAMA,

February 16, 2005—Vol 293, No. 7

Francisco de Toro, Javier Aroba & Eduardo Ros (2011):

Computer-Aided Diagnosis of the Paroxysmal Atrial

Fibrillation: A Fuzzy-Evolutionary Approach. Applied

Artificial Intelligence, 25:7, 590-608

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