A Bayesian network model to segment consumers

based on their minor digestive concerns

Pierrick Rivière1, Fabien Craignou2, Peter Whorwell3, Helen Carruthers3

1 Sensory and Behavior Science, Danone Research, RD 128, 91767 Palaiseau – France2 Repères, 9 rue Rougemont, 75009 Paris – France3 Department of Medecine, University Hospital of South Manchester, Manchester, M23 9LT UK

context & objectives � Beyond sensory pleasure and nutritional intake, food can also integrate health functionalities like improving digestive health. This expectation is not negligible : 60% of English

women claim to suffer from minor digestive disorders! Most of them do not consult health care professionals but try alternative solutions, including food. Designing a product to match these expectations requires an accurate knowledge of the troubles experienced by consumers, to complement the existing medical knowledge. Minor digestive troubles cannot be accessed in a straight-forward manner as naïve consumers don’t have the medical knowledge nor the ability to describe/name their digestive troubles[1]. Nevertheless, consumers can translate what they feel when experiencing digestive troubles.

� The objective is to build a precise typology of minor digestive troubles based on perceived & experienced sensations described by consumers.

methodology� 1000 English women nationally representative declaring minor digestive trouble.

� On-line interactive survey

� Focus on the last trouble to increase accuracy of the description.

• Questionnaire items related to the personal experiences of digestive troubles extracted from a previous Qualitative Survey

“tell us the story of your last digestive concern”…

SYMPTOM DESCRIPTION

• Experienced sensations (40 items list)

• Digestive concern illustration (12 pictures)

• Etiology (supposed causes)

• Impact (Emotional & social consequences; frequency / pain)

• Context

SOLUTIONS & EXPECTATIONSUSER PROFILING (demographics; IBS detection)

“tell us the story of your last digestive concern”…

SYMPTOM DESCRIPTION

• Experienced sensations (40 items list)

• Digestive concern illustration (12 pictures)

• Etiology (supposed causes)

• Impact (Emotional & social consequences; frequency / pain)

• Context

SOLUTIONS & EXPECTATIONSUSER PROFILING (demographics; IBS detection)

Bayesian networks allowed identifying digestive troubles based on symptom combinations without any a priori.

Probabilistic reasoning and graphical models help communication.

This approach provides an accurate & useful description of minor digestive concerns in the UK pop.

The 12 clusters are fully interpretable by gastroenterologists & physiologically relevant.

They are used by R&D teams to translate consumer needs into new researches & clinical studies.

Despite the gap between the medical vision and the layman representation, this approach allows the connection of consumer perceptions to the expert physiological knowledge.

This link provides a complementary understanding of the human body

conclusions

data analysis & results

33.0%Symptom occurred

67.0%Symptom did not occur

ACID REFLUX33% of consumers are

concerned with acid refluxsymptoms (a priori probability)

If for example, consumer X answers…

I’ve got acid reflux -----------------NoAcid in me so that's burning ------YesRegurgitation in the throat---------YesI burp -------------------------------No

Probability that consumer X is affected

by acid refluxis 81%.

Cluster 5

Cluster 2

Cluster 4

Cluster 1

7%

7%12%

10%

11%

Cluster 3

7%

Cluster 7

10%

Cluster 66%

5%Cluster 8

Cluster 9

9%Cluster 10

10%Cluster 11

6%

Cluster 12

ACID REFLUX

+: significant difference against a priori probability

Yes

GURGLING

Yes

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12

1-Identifying groups of experienced sensations likely to happen together = The symptoms

Automatic Bayesian Learning (BayesiaLab EQ algorithm)

�Discovering probabilistic relations between symptoms [2]

NO a-priori relation to be defined: focus on what consumers really feel.

•Conservative model[3] : compromise between the data fit and the structural complexity

•10-fold cross-validation to ensure the robustness of the structure.

2-Identifying groups of consumers concerned by the same combinations of symptomsBayesian Clustering of Consumers (BayesiaLab Data Clustering)

� Latent Class Model, in which consumer clusters are connected to the symptoms.

�EM algorithm to determine the parameters of the model.

�Pseudo-random walk between 2 and 20 to determine the number of clusters.

� 12 groups of consumers emerged, each concerned with a specific symptoms profile = a digestive trouble

� Each group of consumers can be interpreted with a probabilistic symptom profile, for an easy and compact communication. Each cluster is characterized by a specific & contrasted combination of perceived symptoms.

� 11 groups of symptoms were identified.

� Each group can be summarized with a latent variable, which can be interpreted in a probabilistic way.

+ ++ +

+ ++

I have a full stomachI have a full stomach

Quite painful to touch stomachQuite painful to touch stomach I feel fullI feel full

Food doesn’t go through meFood doesn’t go through me

Stagnation within my tummyStagnation within my tummy

I can’t evacuate my bowelsI can’t evacuate my bowels

Emptying my bowels is difficultEmptying my bowels is difficult

I’m constipatedI’m constipated

Evacuation is painfulEvacuation is painfulCan cause haemorrhoidsCan cause haemorrhoids

There is food in my stool, not fully digested

There is food in my stool, not fully digested

As if my stool has fermented inside meAs if my stool has fermented inside me

Going to the toilet is almost explosive

Going to the toilet is almost explosive

Really urgent need to go to the toiletReally urgent need to go to the toilet

Large amount of stoolLarge amount of stool

Body feels really heavyBody feels really heavy

I cannot help but pass windI cannot help but pass wind

I feel full of windI feel full of wind

I have often smelly windI have often smelly wind

I often have gasI often have gas

Need to relieve the pressureNeed to relieve the pressure

It is burning inside my tummyIt is burning inside my tummy

I get a pricking inside meI get a pricking inside me

Stomach is blowing up like a balloonStomach is blowing up like a balloon

Tummy is going to explodeTummy is going to explode

Acid in me so that’s burningAcid in me so that’s burning

I’ve got acid refluxI’ve got acid refluxI burpI burp

Regurgitation in the throatRegurgitation in the throat

I have gurglingI have gurgling

Small hair bubbles inside of meSmall hair bubbles inside of me

Spasms inside meSpasms inside me

Stomach crampsStomach cramps

Knots in my intestineKnots in my intestine

Intestine is in spasmIntestine is in spasm

My belly is hardMy belly is hard Skin of my tummy is tensedSkin of my tummy is tensed

Stomach rock hard

Stomach rock hard

Swollen tummySwollen tummy

It stretches my tummy, like contractionsIt stretches my tummy, like contractions

EXAMPLE

OTHER SYMPTOMS …

outlooks

- one-dimensional (a single sensation)- accurate / non ambiguous- combining imagery & concrete description- based on layman words - uses the most frequent words / expressions

� Experienced sensations have been structured into attributes based on following requirements :

>> examples : “I burp”“I have gurgling. It's turning inside me, as if nothing is in the right place”…

[1] R. Monrozier, A. Bonnet, I. Boutrolle, N. Boireau. (2009).Toward a consumer typology of health concerns. An application to minor digestive disorders. Poster - 8th Pangborn Sensory Symposium[2] J. Pearl and S. Russel, 2000 "Bayesian networks" , UCLA Cognitive Systems Laboratory, Technical Report (R-277), November 2000. In M.A. Arbib (Ed.), Handbook of Brain Theory and Neural Networks, Cambridge, MA: MIT Press, 157-160, 2003.[3] Friedman N., Goldszmidt M., “Learning Bayesian networks with local structure ”, Proc. of the 12th Conf. on Uncertainty in Artificial, Morgan Kaufmann, 1996.

Colour variation:purity of the cluster(the darker, the purer)

Positioning:probabilistic proximity