Categorize, Cluster & Classify

The 3C Strategy Applied to Alzheimer's Disease as a Case Study

Alexis Mitelpunkt

, Tal Galili

, Netta Shachar

, Mira Marcus-Kalish

and Yoav Benjamini

1,2

Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel

The Sagol School for Neurosciences, Tel Aviv University, Tel Aviv, Israel

Keywords: Medical Informatics, Bioinformatics, Disease Profiling, Disease Signature, Categorization, Clustering,

Classification.

Abstract: Health informatics is facing many challenges these days, in analysing current medical data and especially

hospital data towards understanding disease mechanisms, predicting the course of a disease or assist in

targeting potential therapeutic options. Alongside the promises, many challenges emerge. Among the major

ones we identify: current diagnosis criteria that are too vague to capture disease manifestation; the

irrelevance of personalized medicine when only heterogeneous classes of patients are available, and how to

properly process big data to avoid false claims. We offer a 3C strategy that starts from the medical

knowledge, categorizing the available set of features into three types: the patients' assigned disease

diagnosis, clinical measurements and potential biological markers, proceeds to an unsupervised learning

process targeted to create new disease diagnosis classes, and finally, classifying the newly proposed

diagnosis classes utilizing the potential biological markers. In order to allow the evaluation and comparison

of different algorithmic components of the 3C strategy a simulation model was built and put to use. Our

strategy, developed as part of the medical informatics work package at the EU Human Brain flagship

Project strives to connect between potential biomarkers, and more homogeneous classes of disease

manifestation that are expressed by meaningful features. We demonstrate this strategy using data from the

Alzheimer's Disease Neuroimaging Initiative cohort (ADNI).

1 INTRODUCTION

Health informatics has the goal of discovering new

insights from the analysis of current available data.

These findings may help in understanding disease

mechanisms, predicting the course of a disease or

assist in targeting potential therapeutic options.

Upon analysing health data and especially hospital

data we face many challenges. Some of the major

challenges we identify in data mining of medical

data are:

1. Expert knowledge is valuable but current

diagnosis might be misleading. We believe that the

medical knowledge should not be ignored when

designing a data mining process. An important

example, referring to current Diagnosis Classes as

ground truth may be misleading: definitions change

over time due to new discoveries, new clinical and

research results, and new insights. Moreover,

diagnosis is usually a rough criteria, while the actual

clinical situation is more complex and sophisticated.

More generally, the understanding of measuring

tools and clinical processes is useful. Therefore we

would like to have a way to use the medical

knowledge and incorporate it into the data mining

process.

2. Compensatory mechanisms obscure the linkage

between biological markers (i.e. imaging, pathology,

genetics) and disease manifestation, making it more

difficult to discover. While always a problem in

medical research, in some fields of medicine you

could take a biological sample and the pathology

may in fact be the diagnosis. In Neurology and

Psychiatry finding the relation between a biological

marker and a disease manifestation is more complex

and difficult. Two people with the same brain

pathology, or brain images, do not necessarily share

the same clinical manifestation. It is not only the

complexity of the disease and inefficiency of the

marker, but the fact that the compensatory

mechanisms may differ from one person to the other.

3. Personalized solutions to heterogeneous

population. We seek ways to be able to tailor

treatment to each patient specifically. If we try to

566

Mitelpunkt A., Galili T., Shachar N., Marcus-Kalish M. and Benjamini Y..

Categorize, Cluster & Classify - The 3C Strategy Applied to Alzheimer’s Disease as a Case Study.

DOI: 10.5220/0005275705660573

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pages 566-573

ISBN: 978-989-758-068-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

find relations between a marker to a large

heterogeneous population (e.g. all dementia patients)

we would decrease the chance to find a marker that

is relevant only to a part of that population because

of decreased signal to noise ratio. We would

increase our chances if we could find homogeneous

sub-groups. Such sub-groups will also have a better

chance of unravelling interesting biological

processes.

4. Big Data: Big potential but increased chance of

capturing irrelevant markers. An inherent problem

of big data is the danger that a large proportion of

the few results selected to be interesting are actually

irrelevant and appear to be interesting by mere

chance due to the extensive search. In order to

address it, a methodological, well-founded selection

process has to be conducted.This is also the case

with Alzheimer’s disease (AD) data.

1.1 Alzheimer’s Disease

Alzheimer’s disease is the most common form of

dementia. The disease is characterized by the

accumulation of b-amyloid (Ab) plaques and

neurofibrillary tangles composed of tau amyloid

fibrils associated with brain cells damage and

neurodegeneration. The degeneration leads to

progressive cognitive impairment. There is currently

no known treatment, nor one that slows the

progression of this disorder. There is a pressing need

to find markers to both predict future clinical decline

and for use as outcome measures in clinical trials of

disease-modifying agents and foster the

development of innovative drugs (Weiner et al.

2013).

The definite diagnosis of AD requires

histopathologic examination, but commonly the

diagnosis of AD is based on clinical criteria. In

2013, an updated criteria was published in the

Diagnostic and Statistical Manual of Mental

Disorders 5-th edition (DSM - 5) (American

Psychiatric Association 2013). The role of

laboratory and imaging investigations is mainly to

exclude other diagnoses. Some studies suggest that

certain biomarkers including levels of tau protein

(Sonnen et al. 2008), beta-amyloid protein

(Sunderland et al. n.d.), ApoE (Gupta et al. 2011),

may have predictive value for AD in healthy and in

patients with minimal cognitive impairment (MCI).

These may also aid in distinguishing AD from other

forms of dementia, and may identify subsets of

patients with AD at risk for a rapidly progressive

course. However, the role of these measurements in

clinical practice has not been established.

Brain imaging using magnetic resonance imaging

(MRI) is part of the diagnostic process for dementia.

It is mainly used to exclude other possible diagnosis

rather than AD for the condition. In some studies it

has been postulated that a decreased volume of

certain brain areas is related to AD but contradicting

studies found a general process of volume reduction

with aging. Functional brain imaging with [18F]

fluorodeoxyglucose positron emission tomography

(FDG-PET), functional MRI (fMRI), perfusion

MRI, or perfusion single photon emission computed

tomography (SPECT) reveals distinct regions of low

metabolism and hypo perfusion in AD. These areas

include the hippocampus, the precuneus (mesial

parietal lobes) and the lateral parieto-temporal

cortex. Clinical studies suggest that FDG-PET may

be useful in distinguishing AD from frontotemporal

dementia, but this result have not become a standard

for diagnosis.

1.2 The Alzheimer’s Neuroimaging

Initiative (ADNI) Research

The ADNI research was conceived at the beginning

of the millennium as a North American multicenter

collaborative effort funded by public and private

bodies (Weiner et al. 2013). Much of the current

research focuses on one or two specific and

promising biomarkers such as Magnetic Resonance

Imaging results (Evans et al. 2010), FDG-PET

imaging (Langbaum et al. 2010) or CSF biomarkers

(Tosun et al. 2011).

The combined-biomarkers methods are often

based on various machine learning algorithms:

(Kohannim et al. 2010) implemented the support

vector machines (SVM) tool in order to classify AD

and MCI patients. (Hinrichs et al. 2011) tried to

predict conversion from MCI to AD using a multi

kernel learning framework. (Zhang et al. 2013)

presented a three steps methodology: feature

selection using multi-task feature learning methods,

data fusing using kernel-based multimodal-data-

fusion method and finally training a support vector

regression.

As part of the medical informatics sub-project of

the European flagship the Human Brain Project,

we've created an approach that deviates from these

lines of research in four major ways addressing the

challenges mentioned above.

First, we avoid using the available diagnosis as the

ultimate truth, but do embody the current medical

knowledge into the data and analysis process.

Second, we create new diagnosis classes that are

created by analyzing the clinical data. These classes

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are more homogeneous in terms of the disease

manifestation. Third, we use these new diagnosis

classes as targets for the biomarkers exploration.

Fourth, we use measurements as the false discovery

rate to lower the chance of irrelevant findings.

In this paper we demonstrate the proposed

approach on a limited part of ADNI data, and on a

limited part of the available information about each

subject. This is but a first step in a longer effort that

will include evaluation and further adaptations,

before expanding to the vast problem of using

hospital data on a grand scale.

2 DATA AND METHODS

2.1 Data

In the preparation of this article we used the

ADNIMERGE table, extracted from the

ADNIMERGE R package (version 0.0.1), which are

obtained from the Alzheimer’s Disease Neuro-

imaging Initiative (ADNI) database (adni.loni.usc.

edu).

To date, over 1500 adults, ages 55 to 90, have been

recruited to participate in the research. We only used

baseline data of ADNI 2 and ADNI Go, out of which

796 subjects had no missing values on the clinical

measurements.

2.2 Pre-processing

As part of the pre-processing we dropped clinical

measurements (CM) that had near one correlation

with another CM. In Addition, we removed from the

analysis the CM EcogSPTotal and the CM

EcogPtTotal as they are both derived from some of

the other sub measurements. In order to reduce

skewness of some of the CMs, log transformations

(for ADAS13, EcogPtMem), logit transformations

(for EcogPtDivatt, EcogPtVisspat, EcogSpDivatt,

EcogSpVisspat, MMSE, MOCA) and inverse

transformations (for CDRSB, EcogPtLang, EcogPt

Organ,EcogPtPlan, EcogSpLang, EcogSpOrgan,

EcogSpPlan, FAQ,Ravlt.prec.forgetting) were

utilized. The following CMs needed no

transformation: RAVLT.forgetting, RAVLT.

immediate and RAVLT.learning. Six new CM were

defined to be the difference between the transformed

patients' report and the partner's report on certain

everyday cognition (Ecog) variable. Finally, all the

variables were scaled to have mean 0 and a variance

of 1.

2.3 The 3C Strategy Stages

2.3.1 Categorization of Variables

Categorization was done using expert medical

knowledge.

(1) The first category is the disease diagnosis

variable as assigned in the ADNI database. This

assigned diagnosis has five levels: Cognitively

Normal (CN), Significant Memory Concern(SMC),

Early Mild Cognitive Impairment(EMCI), Late Mild

Cognitive Impairment(LMCI), Alzheimer’s disease

(AD).

(2) The second category is of clinical

measurements that reflect the patient’s condition and

functionality of the patient. They encompass scores

of different cognitive and psycho-neurological tests

and ratings, according to clinical assessment and

patient’s or partners' report. This battery of cognitive

and functional assessment scores include: Clinical

Dementia Rating Sum of Boxes (CDR-SB),

Alzheimer's disease assessment scale (ADAS),

mini–mental state examination (MMSE), Rey

Auditory Verbal Learning Test (RAVLT), Family

history questionnaire (FAQ) Montreal Cognitive

Assessment (MoCA), Everyday Cognition (Ecog).

(3) The third category includes measurements of

potential biological markers, which were proposed

to have a predictive value for disease risk, for

deterioration, or for severity. These markers are

either proteins levels measured in the cerebrospinal

fluid (CSF) such as ApoE4 (Gupta et al. 2011) or

imaging data from different modalities: FDG-PET

(Walhovd et al. 2010), AV45 PET (Johnson et al.

2013), and MRI. These will be referred to as

potential biomarkers (PB).

2.3.2 Feature Selection and Clustering

In order to create clinical measurements based

classes that are medically easy to interpret, a feature

selection procedure was performed on all potential

clinical measurements. We used Random Forest, but

of course other methods may prove as useful or even

more. Out of 27 original CM, we chose to keep

those that reduced error-rate by 15% or more.

We then clustered the data based on the selected

subset of clinical measurements using k-means

algorithm (again, another algorithm could have been

used). In any such algorithm, the number of clusters

is a crucial parameter. We chose to combine

statistical information with medical perspectives.

According to the latter, there is a natural lower

bound to the number of clusters: the measured

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Figure 1: The 3C strategy flow chart.

Figure 2: Profiles plot (parallel coordinates) demonstrating

the values of the CM across classes.

clinical data should represent the different classes of

clinical manifestation including patients' medical

history, background, symptoms, etc. From the

literature (Shadlen et al. MD 2014) and knowledge

about dementia we know that within the clinical

spectrum that could be lines from Normal to AD

there are some sub-classes of patients. It is

reasonable to consider at least 8 subclasses of

disease manifestation:

1. AD with a rapid progression and dysfunction.

2. AD with slower progression course.

3. Cognitive Normal that will not develop dementia.

4. Cognitive Normal that will develop dementia.

5. MCI that will later develop AD.

6. MCI that will develop other irreversible cause (i.e.

FrontoTemporal Degeneration or vascular).

7. MCI that will develop other irreversible cause (i.e.

Dementia of Lewy Bodies - DLB).

8. MCI that will not deteriorate and will stay with a

stable impairment status.

This medical insight into the potential number of

classes was combined with the statistical point of

view, utilizing the gap statistic plot (Tibshirani,

Robert; Walther 2001).

2.3.3 Classifying with Potential Biomarkers

At this stage we classify the new diagnosis classes

using the set of potential biomarkers. In principle

this stage also consists of two parts. First, using

importance analysis by, say, random forests, a

promising subset of the biomarkers is selected.

Then, the final classification step is done using

hierarchical decision trees, or other rule based

analysis, utilizing the selected subset. This is

essential in order to give easy interpretation to the

diagnosis process. In the envisioned application to

hospital data the number of potential biomarkers

may increase to thousands, before incorporating

genomic information. Thus, the subset selection

stage may be crucial. In the current analysis we skip

this stage as the number of PB is small.

Table 1: Cross-classification table of originally assigned

diagnosis vs clinical classes.

2.4 Algorithms and Software

Analyses were performed using R (R Core Team

n.d.) . For assessing the importance of the clinical

measurements (as a preparation to the Clustering

stage) we used the classification method of the

{randomForest} R package (Liaw & Wiener 2002).

Importance was measured as the marginal loss of

classification accuracy for each variable by

randomly permuting it in the test (out of bag)

validation set. For clustering we used the R package

{cluster} (Maechler et al. 2013), using the gap

statistic to choose the number of clusters. The gap

statistic was based on 100 bootstarps and calculated

for up to 20 clusters. Clustering was done using k-

means with 10 iterations at most, based on the

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Hartigan and Wong algorithm. Classification and

regressions tree (CART) was constructed using the

{rpart} R package, the tree was constructed with the

minimal possible number of observations for a split

set to zero, minimal number of observation in a leaf

set to zero, and with a 10-fold cross validations for

tuning the complexity parameter. Scatter plot matrix

was produces using the {psych} R package (Revelle

2010).

3 RESULTS

3.1 Categorization of Variables

Categorization yielded one variable of assigned

diagnosis with 5 different diagnosis values, 27

clinical measurements and 10 potential biomarkers.

3.2 Clustering

3.2.1 Selection of Clinical Measurements

Out of 27 potential CM, we chose to keep the 7 CM

that reduced error-rate of predicting the assigned

diagnosis by 15% or more.

3.2.2 Clustering of Clinical Measurements

The 7 selected CM were clustered using k-means

with varying number of means and the gap statistics

plot for aid in the choice of the number of clusters.

The first local maxima of the gap statistics above the

clinically determined lower bound was chosen to

indicate that 10 clinically determined classes are

needed. In order to discuss the meaning of the newly

created classes we present their cross-classification

with the assigned diagnosis in Table 1, a profiles

plot in Figure 2 and a CART decision tree in figure

Classes 1, 3 contain nearly all the participants

with an assigned diagnosis of AD. Class 3 might be

a class of more severe AD cases (see minimal

average level on all coordinates of the profile plot).

From this plots we also see that Class 1 members

score higher on EcogPtLang and EcogPtMem than

those classified to 3. Classes 4,5 and 10 hold the

majority of patients whose assigned diagnosis is CN.

It is interesting that while these classes have a very

small amount of patients with different diagnoses

they were still separated to three classes based on

their clinical manifestation. Class 4 has the highest

“MMSE” and a low “CDRSB” scores, which points

to a group of clinically normal participants, but the

score in "ECogPtLang” is lower than other classes

which could mean that these participants are more

concerned of their personal observation of language

difficulties. Classes 6 and 7 include normal and

mildly affected participants (sharing the same

branch in the decision tree), but differ from each

other especially in their patients' "ADAS13" scores.

Another group of classes is 2,8,9 in which patients

are distributed almost uniformly but their disease

manifestation differ from one another (though they

all seem to have a progressive disease but not to the

level which qualifies as AD). Inspecting the decision

tree representing the classes (figure 4), we can see

that “FAQ” feature had much influence on the

clustering: the classes with low “FAQ” are 1,3,9

meaning those patients would be likely to have a

progressive disease. Further down on the right

branch of the tree, class 3 has a low “MMSE” score

and class 1 has low score on “ECogPtLang”

representing the interference of language impairment

of the patient’s life in his own perception. Walking

down the left branch of the tree the first split sends

down the right branch all patients with a “CDRSB”

score of over 0.33, this by definition of the inclusion

criteria will not allow normal participants in that

branch. Clusters 2,8,9 occupy that branch of the

CART decision tree.

This decision tree gives the possibility to

determine rules and to explain to the physician the

way the classes where created from the data. The

first two branches divide the participants into

"Normal" and "Not Normal". This division of the

data is done using 2 variables: “FAQ” and

“CDRSB”, that are related to disease state

definitions. Then, both branches use the level of

“MMSE” to create a separation within each branch

between normal and AD affected. In a lower and

fine distinction the next junction divides them to a

class of participants according to ECogPtLang value

meaning that the participant feels at least

occasionally that his language ability is worse than it

was 10 years earlier.

3.3 Classification

The potential biomarkers used to classify subjects to

the ten clinically relevant classes consist of “ApoE”,

“AV45”, “FDG”, “Entorhinal”, “Fusiform”,

“Hippocampus”, “ICV”, “Midtemp”, “Ventricles”,

and “Whole-brain”. Interpretation of this step could

be done using the classes’ description shown above.

The PB decision tree (figure 4) distinguishes

primarily between the patients designated to class 3

concurrent with severely symptomatic disease. The

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first node uses "FDG" value as a criteria to split

those with severe AD from others. This coincides

with that FDG is a known marker of AD. Having

more homogenous classes of patients has the

potential of yielding better relation between a

biomarker and a specific class of disease

manifestation.

Figure 3: Decision tree representing the classes resulted

from the clustering step.

4 SIMULATION MODEL

In order to study and evaluate the suggested 3C

strategy under different settings we created a

simulation model. In addition to the evaluation of

the 3C strategy as a whole, and since various

algorithms might be used in each of the steps, a

simulation can be helpful in determining which

algorithms yield the best results. Different datasets

were created in order to simulate possible scenarios.

In the simplest dataset, 4 current diagnoses are

assigned to patients but one of which should actually

be decomposed into two different diseases.

Simulations were conducted in different levels of

noise. For every noise level several methods for

feature selection and clustering methods were

compared. Finally, the number of clusters (K) must

be determined by the user. For each of the clustering

algorithms there are a few possible criterions

designed to indicate a recommended number of

clusters. The clustering was then made both

according to the yield results and according to an

"oracle" answer with the real number of clusters.

The use of the Oracle answer was added in order to

avoid an influence of the wrong number K on the

clustering step itself.

Figure 4: Decision tree representing prediction of classes

from potential biomarkers.

5 DISCUSSION

The criteria currently in use according to DSM-5

(American Psychiatric Association 2013) for

diagnosis of AD relies on the clinical and functional

ability of the patient. Most biological exams, such as

imaging results, are mainly used to rule out other

possible diagnoses.

In our strategy we therefore differentiate between

variable which are descriptive of the patient’s

functional conditions and variables which are

collected in order to try and find possible disease

causes, the latter being targets for drug development

or surrogate markers for disease stage or trajectory.

The accuracy of a diagnosis depends on the

available knowledge at the time it was made and the

available knowledge at that moment. DSM V

presented ten etiological subtypes which did not

appear in prior editions. Other than the explicit link

to specific known etiologies, most of these subtypes’

criteria are largely similar to one another. However,

there are important and often subtle differences

between these disorders (American Psychiatric

Association 2013). We present an approach

separating patient to groups according to their

clinical data. Interestingly, our data also identifies 10

classes that might represent a more accurate

distinction of the patients compared with the 5

diagnosis criteria given by the ADNI protocol.

We do not claim that our findings present our

best current views on the problem. We are very

aware that this was but a sketch of strategy that

happened to offer some new insights. Further

exploration is needed on a few fronts: The use of the

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raw exams data instead of combined scores, adding

potentially important measurements, enlightenment

of the data by expert knowledge such as differing

questions to the different cognitive function domain

measured could all help in creating more subtle and

fine clusters of patient’s disease presentation. From

a statistical point of view, different clustering

procedures and/or different selection procedures

may yield better results under different settings, an

issue we have not started to address at all.

We believe that the attempt to predict from very

specific potential biomarker is futile. The route we

have taken is to predict more subtle disease

manifestation classes. Such a process needs further

exploration but has the potential to fit a small

biomarker arrow to the clinical bull’s eye.

In many studies and definitely in the ADNI

study a vast amount of measurable information is

collected. Is it enough? The tacit knowing held and

applied by proficient practitioners represents a

valuable form of clinical knowledge, which has been

acquired through experience, and which should be

investigated, shared, and contested (Malterud 2001).

In clinical work, tacit knowing constitutes an

important part of diagnostic reasoning and judgment

of medical conditions. We made an effort to

incorporate this knowledge into the process so that a

valuable aspect of analysis and interpretation of the

results could be added. Further exploration is needed

of both the data nuances and methods, before trying

to scale to the much harder problem associated with

regular hospital data. We do believe that the strategy

we have outlined in this work is capable of

achieving that.

ACKNOWLEDGEMENTS

**Data used in preparation of this article were

obtained from the Alzheimer’s Disease Neuro-

imaging Initiative (ADNI) database (adni.loni.usc.

edu). As such, the investigators within the ADNI

contributed to the design and implementation of

ADNI and/or provided data but did not participate in

analysis or writing of this report. A complete listing

of ADNI investigators can be found

at:http://adni.loni.usc.edu/wpontent/uploads/how_to

_apply/ADNI_Acknowledgement_List.pdf

The research leading to these results has received

funding from the European Union Seventh

Framework Programme (FP7/2007-2013) under

grant agreement no. 604102 (Human Brain Project).

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