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Eurosense 2010 repères


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Eurosense 2010 repères

  1. 1. 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 – France 2 Repères, 9 rue Rougemont, 75009 Paris – France 3 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 Experienced sensations have been structured into “tell us the story of your last digestive concern”… minor digestive trouble. attributes based on following requirements : SYMPTOM DESCRIPTION - one-dimensional (a single sensation) On-line interactive survey - accurate / non ambiguous • Experienced sensations (40 items list) - combining imagery & concrete description Focus on the last trouble to increase accuracy of the • Digestive concern illustration (12 pictures) - based on layman words description. • Etiology (supposed causes) - uses the most frequent words / expressions • Impact (Emotional & social consequences; >> examples : • Questionnaire items related to the personal frequency / pain) “I burp” experiences of digestive troubles extracted from a • Context “I have gurgling. It's turning inside me, as if nothing is in the right previous Qualitative Survey SOLUTIONS & EXPECTATIONS place” … USER PROFILING (demographics; IBS detection) data analysis & results 1-Identifying groups of experienced sensations likely to happen together 2-Identifying groups of consumers = The symptoms concerned by the same combinations of symptoms Bayesian Clustering of Consumers (BayesiaLab Data Clustering) Automatic Bayesian Learning (BayesiaLab EQ algorithm) [2] Latent Class Model, in which consumer clusters are connected to the symptoms. Discovering probabilistic relations between symptoms NO a-priori relation to be defined: focus on what consumers really feel. EM algorithm to determine the parameters of the model. •Conservative model[3] : compromise between the data fit and the structural complexity Pseudo-random walk between 2 and 20 to determine the number of clusters. •10-fold cross-validation to ensure the robustness of the structure. Stomach rock Stomach rock Swollen tummy Swollen tummy 6% hard hard Cluster 12 10% IIcan’t evacuate my bowels I’m constipated My belly is hard My belly is hard Skin of my tummy is tensed IIhave a full stomach have a full stomach can’t evacuate my bowels I’m constipated Cluster 11 Skin of my tummy is tensed Quite painful to touch stomach 9% Quite painful to touch stomach IIfeel full Emptying my bowels is difficult feel full Emptying my bowels is difficult Cluster 10 10% Knots in my intestine Knots in my intestine Tummy is going to explode Tummy is going to explode Can cause haemorrhoids Evacuation is painful Can cause haemorrhoids Evacuation is painful Cluster 9 6% Cluster 6 Intestine is in spasm Intestine is in spasm 7% Stagnation within my tummy Stagnation within my tummy Need to relieve the pressure Need to relieve the pressure Spasms inside me Spasms inside me There is food in my stool, not fully There is food in my stool, not fully 5% Cluster 7 Stomach is blowing up like a balloon digested digested Cluster 8 Stomach cramps Stomach cramps Stomach is blowing up like a balloon 7% As if my stool has fermented inside me As if my stool has fermented inside me It stretches my tummy, like contractions It stretches my tummy, like contractions Cluster 5 IIhave gurgling have gurgling Food doesn’t go through me Food doesn’t go through me 7% 12% IIget a pricking inside me get a pricking inside me Cluster 3 Small hair bubbles inside of me Small hair bubbles inside of me It is burning inside my tummy It is burning inside my tummy Going to the toilet is almost Going to the toilet is almost explosive Cluster 4 explosive Colour variation: 11% purity of the cluster Acid in me so that’s burning IIhave often smelly wind have often smelly wind Really urgent need to go to the toilet Really urgent need to go to the toilet Acid in me so that’s burning (the darker, the purer) IIburp burp IIfeel full of wind feel full of wind IIoften have gas often have gas Large amount of stool Large amount of stool Cluster 2 10% Positioning: I’ve got acid reflux I’ve got acid reflux probabilistic proximity Body feels really heavy Body feels really heavy Regurgitation in the throat Regurgitation in the throat IIcannot help but pass wind cannot help but pass wind Cluster 1 11 groups of symptoms were identified. 12 groups of consumers emerged, each concerned with a specific symptoms profile = a digestive trouble Each group can be summarized with a latent variable, Each group of consumers can be interpreted with a probabilistic symptom profile, which can be interpreted in a probabilistic way. for an easy and compact communication. Each cluster is characterized by a specific & contrasted combination of perceived symptoms. ACID REFLUX 33% of consumers are Yes EXAMPLE Symptom did not occur 67.0% concerned with acid reflux symptoms (a priori probability) ACID REFLUX Symptom occurred 33.0% + + + + +: significant If for example, consumer X answers… difference I’ve got acid reflux -----------------No Yes against a priori Probability that probability Acid in me so that's burning ------Yes consumer X is affected Regurgitation in the throat---------Yes by acid refluxis 81%. I burp -------------------------------No GURGLING + + + C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 conclusions OTHER SYMPTOMS … This approach provides an accurate & useful description of minor digestive concerns in the UK pop. The 12 clusters are fully interpretable by Bayesian networks allowed identifying digestive troubles based on symptom outlooks gastroenterologists & physiologically relevant. combinations without any a priori. Despite the gap between the medical vision and the layman representation, this approach allows the connection of consumer They are used by R&D teams to translate consumer Probabilistic reasoning and graphical perceptions to the expert physiological knowledge. needs into new researches & clinical studies. models help communication. This link provides a complementary understanding of the human body [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.