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Impact of Culture on Individual Well-being
PierLuigi Sacco - Professor of Cultural Economics, IULM University (Milan)
Enzo Grossi - Advisor Padiglione Italia EXPO 2015
ISPRA , 11 June 2015
Outline • The meaning of cultural participation (PGS)
• Culture & Health: scientific background (EG)
• An Italian research on the impact of culture on wellbeing: general description( PGS)
• How to quantify wellbeing: PGWBI (PGS/EG)
• Use of artificial neural networks to predict wellbeing (study 1) (EG)
• Use of artificial neural networks to define interaction scheme among factors examined (study 2)(PGS)
• Conclusion and future steps (PGS/EG)
What do we mean by culture?
• There is a basic difference between culture in the socio-anthropological sense (values, belief systems, transmitted habits) and culture as a purposeful attempt at creating new systems of meanings and new patterns with aesthetic value
• Culture in the socio-anthropological sense has an increasing relevance in medical practice (e.g. the Culture and Health manifesto published on Lancet, 2014), but it has mainly to do with accounting for existing sources of behavioral, perceptual, cognitive diversity etc.
• This ‘ample’ notion of culture is by far the most considered in inter-disciplinary scientific approaches, and is taken by many as the only one of real interest
Beyond socio-anthropology: Culture as a conscious expressive activity
• In our perspective, culture is of interest in that it entails a purposeful engagement in expressive and creative activities, which may rely upon received traditions but also require the conscious involvement of individuals
• In this sense, level of cultural participation is the object of choice and not an ascriptive characteristic (however socially conditioned)
• We can therefore study how culture in this specific sense affects other domains, such as health, innovation, or social cohesion, as a result of a certain level of individual and collective participation and not merely as a reflex of inherited socio-economic conditions
A taxonomy of culture
• Core cultural sectors: Visual arts, Performing arts, Museums and heritage
• Cultural industries: Cinema, Music, Publishing, Radio-TV, Videogames
• Creative industries: Design (incl. Fashion and Crafts), Architectural Design, Communication
• Digital platforms
• Community arts?
• Outdoor leisure (e.g. gardening) and sports?
Active vs. passive participation
• There is a basic distinction between active and passive cultural participation: participating to a certain activity as the audience or as the player
• This distinction is blurring down with the advent of digital social platforms, where the role of the player and that of the audience are seamlessly interchanging
• Both forms of participation are controlled by activation costs of various nature, which are often mis-conceptualized or overlooked
• Rather than a binary concept, participation tends to be a fuzzy one
Passive participation
• Tends to happen in prescribed ways (cultural protocols)
• Often has a social dimension • Can be regulated by the market or by community
affiliation • Is linked to positive social stigma • Is often encouraged as a positive human right • Is often mediated by cultural institutions • Level of participation is often easily measurable
with a natural scale
Active participation
• Is much more context-sensitive than passive participation, and may happen in radically different ways with different effects
• May imply less chances of social interaction than passive participation for non-professionals
• Is often self-produced and self-supported • Is not necessarily linked to positive social stigma • Is not necessarily socially encouraged and is not
necessarily hosted by cultural institutions in the case of non-professionals
• Level of participation may often be ambiguous in terms of measurement
Health impacts
• It is much easier conceptually to evaluate the health impacts of passive participation due to the existence of socially accepted participation protocols
• In the case of active participation, lack of socially pre-existing protocols requires to create them in order to make experiences relatively comparable
• On the other hand, active participation in principle seems even more promising in terms of emotional involvement, capability building, self-expression, etc.
The (apparent) paradox of participation
• On the other hand, there is preliminary evidence that, quite counter-intuitively, shows that passive cultural participation seems to have a bigger impact on psychological wellbeing than active one
• Such result is however largely due to the ambiguities in defining active participation, which often leads to compare passive experiences with a strong social component with active ones that are basically a-social
• This kind of comparisons make sense for given levels of social interaction and exposure, social recognition, implied activation costs, etc.
A two-sided research strategy
• In this pioneering phase, the best possible strategy is likely to be two sided
• On the one hand, evaluating the health impact of passive participation on the basis of a widely shared, easily communicable framework
• On the other hand, starting to define a tentative collection of active participation protocols on the basis of a strong logical framework that describes effectively the spectrum of possibilities and validating them through specific field studies
Expected developments
• Once properly conceptualized, it is likely that active participation will result even more rewarding than passive one in terms of psychological wellbeing
• It will also fit naturally into socially evolving modes of interaction and identity building
• It will have to develop new forms of social perception and legitimation
• It will mandate profound transformations in the structure and mission of cultural institutions
• …But for the moment we will mainly focus on passive forms
STUDY 1
STUDY 2
Culture & Health: scientific background
BMJ 1996;313:1577-1580 (21 December)
Unequal In Death
Attendance at cultural events, reading books or
periodicals, and making music or singing in a
choir as determinants for survival: Swedish
interview survey of living conditions
Lars Olov Bygren, Boinkum Benson Konlaan,
Sven-Erik Johansson
,
Leisure time cultural participation
• People rarely engaged in cultural activity, broadly defined, had an odds of 1.57 for all- cause mortality compared to people often engaged.
• People often going to each of concerts, museums, museums of art or galleries, or the cinema, had low mortality compared to people rarely attending.
Discounts
• In the observational studies differences in age, gender, social background, education, income, social network, smoking, physical exercise, baseline health are taken care of.
• The effects of such differences have been discounted in the multivariate analyses of culture-health effects.
• The experiments are randomized and controlled.
Relative risks for mortality (95% confidence intervals) in proportional hazards models*
____________________________________________________________________________________
Age-sex control Multi-discount
Education (years): </=9 1 Reference 1 Reference
>12 0.57 (0.43 to 0.75) 0.93 (0.68 to 1.26)
Income: Low 1 Reference 1 Reference
High 0.60 (0.49 to 0.73) 0.73 (0.59 to 0.90)
Network Weekly contact 1.06 (0.93 to 1.22) 1.10 (0.96 to 1.26)
No friends 1 Reference 1 Reference
Disease Yes 2.28 (1.94 to 2.67) 2.10 (1.78 to 2.46)
No 1 Reference 1 Reference
Smoking: No smoking 1 Reference 1 Reference
>15 g/day+ 1.83 (1.53 to 2.18) 1.69 (1.42 to 2.02)
Exercise: Inactive 1 Reference 1 Reference
At least once a month 0.60 (0.50 to 0.72) 0.78 (0.65 to 0.94)
Reading: Rarely 1.41 (1.20 to 1.65) 1.05 (0.88 to 1.25)
At least once a week 1 Reference 1 Reference
Music-making Sometimes 0.77 (0.63 to 0.94) 0.89 (0.72 to 1.10)
Rarely 1 Reference 1 Reference
Attending cultural events:
Rarely (7-8 points) 2.38 (1.83 to 3.09) 1.57 (1.18 to 2.09)
Occasionally (9-12 points) 1.60 (1.24 to 2.08) 1.24 (0.95 to 2.55)
Often (>12 points) 1 Reference 1 Reference
___________________________________________________________________
____________________________________________ *Model 0 was adjusted for sex and age (in 10 year bands) with one variable at a time; model 1 was adjusted for sex, age (age span 16-74),
education, disposable income, social network, long term illness, smoking, exercise, reading books or periodicals, making music, and
attending cultural events.
12000 aged 16-74 followed 9 years
Leisure participation predicts survival: a population-based study in Finland
MARKKU T. HYYPPA¨ , JUHANI MA¨ KI, OLLI IMPIVAARA and ARPO AROMAA Department of Health and Functional Capacity, National Public Health Institute, 20720 Turku, Finland
Markku T. Hyyppä
Department of Health and Functional Capacity, National
Public Health Institute, 20720 Turku, Finland
Cancer in urban areas
• Risk of cancer mortality and the interaction between cultural participation index and residency among adults aged 25-74 (n = 9,011)
• _____________________________________________________________________________________
• Residency Urban Mid-size town Small Town & Rural
• Cultural participation HRa (CI)b HRa (CI)b HRa (CI)b • ___________________________________________________________________________
_______________________ • Crude model • Rare 6.47 (3.26-12.8) 4.39 (2.22-8.69) 4.14 (2.11-8.12) • Moderate 3.57 (1.87-6.84) 2.50 (1.29-4.83) 2.54 (1.32-4.91) • Frequent 1.00 (Reference) 2.30 (1.09-4.83) 2.45 (1.10-5.47) • • • Adjusted model e • Rare 3.23 (1.60-6.52) 2.22 (1.10-4.51) 2.11 (1.04-4.26) • Moderate 2.92 (1.52-5.62) 2.24 (1.14-4.38) 2.06 (1.05-4.05) • Frequent 1.00 (Reference) 2.46 (1.17-5.17) 2.23 (1.00-4.99) • ______________________________________________________________________________________ • Bygren LO et al. Arts and Health 2009;1:64-73
IPPOCAMPO
Fattori con influenza sulla funzione ippocampale
Neurogenesi ippocampale nell’adulto
Stress
Invecchiamento
Ambiente arricchito
Esperienze piacevoli
Esperienze spiacevoli
Attività fisica
Apprendimento e memoria;
orientamento spaziale
Female Hippocampus Vulnerability to Environmental Stress, a Precipitating Factor in Tau Aggregation Pathology : Journal of Alzheimer's Disease, vol. 43, no. 3, 2015 Sotiropoulos, Ioannis | Silva, Joana | Kimura, Tetsuya | Rodrigues, Ana Joao | Costa, Patricio | Almeida, Osborne F.X. | Sousa, Nuno | Takashima, Akihiko
Our findings provide new insights into the molecular mechanisms through which clinically-relevant precipitating factors contribute to the pathophysiology of AD. Our data point to the exquisite sensitivity of the female hippocampus to stress-triggered Tau pathology.
Art, culture
Physical health,
longevity
Psychological
wellbeing
Mental health
Stress relief
Culture and health
Protection from
chronic
degenerative
diseases
Edonic and
eudaimonic
experience
Well-Being and culture: a lack of knowledge
• Very few studies have investigated the impact of cultural participation on the QOL and well-being of individuals.
• Of the studies that look specifically at the relationship between cultural participation and QOL just one found evidence of a substantial contribution, and this was in a sample of committed musicians.
• The other studies either found no effect on the QOL of subjects, or evidence of a very small contribution to QOL.
• In short, this is an area of research in its infancy: there are very few studies and those that exist have limitations.
Culture partecipation and wellbeing
• A growing number of populations-based studies are depicting a major role of culture partecipation in improving wellbeing and other important outcomes.
• The results suggest a complex but reliable cause-effect relationship but further experimental and interventional studies are needed to establish this association definitively.
The impact of culture on wellbeing: an Italian study: general remarks
Research Aims • To explore the relation between the cultural dimension and
individuals, in this respect to investigate the possible correlation
between participation/consumption of culture and individual well-
being.
• Our hypothesis is based on the assumption that the participation or
consumption of different forms of culture, produces benefit in the
psychological well being of individuals, and in this respect forms of
culture presenting consumption mode based on the interaction with
others rather than in exclusive forms, are those having more
influence on the individual psychological well being.
Hypothesis
姜Cross-sectional survey to assess the quality and quantity of cultural
consumption and its relation with psychological well being in a representative
sample (n=1500) of community-dwelling Italians.
姜Multi-step random sampling method was adopted to draw a large representative
sample from the Italian population.
姜The universe, to which the National survey referred, were 49.2 million Italians of
all regions aged 15 years or more, stratified according to region and size of the
place of residence.
姜The sampling units were chosen in the following way: in the first stage, the
choice regarded the municipalities where the interviews were to be conducted, in
the second stage in each municipality an adequate number of electoral wards
were extracted at random so that various types of urban areas were represented
(e.g., central, suburban, outskirts and isolated houses).
姜Finally, names and addresses of the persons to be contacted were extracted at
random from the electoral lists of the areas selected in the second stage. Mean
scores for all items and the global summary measures were calculated according
to the established algorithm and weighted by gender, age and size of the
municipality in the percentages as established in the universe which the study
referred to.
Methods
Ranking of wellbeing determinants according to their effect size in Italy: state
of the art before our study
Rank variable
1 Diseases
2 Income
3 Age
4 Schooling
5 Gender
6 Job
7 Geography
Prevalence of cultural activities consumption and wellbeing: an italian
survey
WELLBEING DETERMINANTS
- Geography
- Urban/rural environment
- Gender
- Age
- Schooling
- Civil status
- Income level
- Diseases presence
Wellbeing assessment: PGWBI
Cultural consumption assessment: ad hoc
questionnaire covering 16 kind of activities
Jazz music concerts
Classic music concerts
Opera/ ballet
Theatre
Museums
Rock concerts
Disco dance
Paintings exhibitions
Social activity
Waching sport
Sport practice
Romances reading
Cinema
poetry reading
Local community development
Materials and Methods
How we measure wellbeing?
• By asking right questions • With a well designed
instrument
THE INSTRUMENT: PGWBI PSYCHOLOGICAL GENERAL WELL-BEING
INDEX
Harold J Dupuy, PhD,1984
“The Psychological General Well-Being Index (PGWBI) was
developed for the purpose of providing an index that could be
used to measure self-representations of intrapersonal affective
or emotional states reflecting a sense of subjective well-being
or distress.”
Measures the subjective perception of wellbeing.
22 items. Each item has six possible answers scores from 0 a 5)
Total index goes from 0 to 110. The highest the better wellbeing..
THE PSYCHOLOGICAL GENERAL WELL-BEING INDEX
PGWBI COMPLEXITY
• Items Dominions
• Items as questions or as statements
• Scoring orientation
• Scoring as intensity and or frequency
ANXIETY DEPRESSION
VITALITY
POSITIVE
WELLBEING SELFCONTROL
Items dominions
GENERAL
HEALTH
Positive wellbeing
No distress
Moderate distress
Severe distress Classical psychology
Behavioral psychology
Positive psychology
60
70
80
90
100
Italy, population survey
• Starting from large data bases of Italian general population collected in different surveys we have analyzed the contribution of 22 items of PGWBI scale on the total score by means of multiple linear regression analysis with stepwise procedure.
• R2 represents the total variance explained by regression
PGWBI SHORT: items selection
1
0,75
0,83
0,860,89
0,91
0,60
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Item
20
Item
7
Item
21
Item
5
Item
6
Item
18
Item
2
Item
17
Item
15
Item
4
Item
19
Item
11
Item
12
Item
8
Item
10
Item
3
Item
13
Item
9
Item
22
Item
16
Item
14
Item
1
R2
PGWBI SHORT : items selection
Dimensione
Posizione
nel
questionario
Contenuto della domanda
Ansia 5
Ha sofferto di stati di tensione o perché
aveva i nervi a fior di pelle?
Depressione 7 Mi sono sentito scoraggiato e triste.
Positività e benessere 20 Mi sono sentito allegro e sereno.
Autocontrollo 18
Mi sono sentito emotivamente stabile e
sicuro di me stesso.
Salute in generale -- --
Vitalità
6
Quanta energia o vitalità ha avuto o ha
sentito di avere?
21
Mi sono sentito stanco, esaurito, logorato o
sfinito.
PGWBI SHORT: Items selezionati
feature No.
Average S.D I.C
Gender
Female 779 74.82 18.23 73.53-76.1
Male 721 80.96 16.62 79.74-82.17
Age(years)
15-17 48 85.1 12.97 81.33-88.86
18-20 93 78.81 15.86 75.55-82.08
21-24 79 78.49 15.44 75.03-81.94
25-29 62 79.72 12.79 76.47-82.97
30-34 150 79.49 18.33 76.54-82.45
35-39 102 79.73 15.64 76.66-82.8
40-44 142 77.65 17.32 74.77-80.52
45-49 128 77.69 17.18 74.68-80.69
50-54 138 76.81 18.23 73.75-79.88
55-64 318 76.5 18.76 74.43-78.57
65-74 167 76.82 17.74 74.11-79.53
75-100 73 72.73 24.02 67.13-78.34
Income
<= 1.000 € 193 71.13 21.95 68.01-74.25
1.001-1.500 € 299 77.7 17.17 75.74-79.65
1.501-2.500 € 361 78.71 16.77 76.97-80.44
> 2.500 € 265 80.03 14.03 78.33-81.73
doesn't state 382 78.72 18.28 76.88-80.56
PGWBI
feature No.
Average S.D I.C
Job
entrepreneur 100 80.96 16.14 77.76-84.16
Manager 22 84.45 17.46 69.76-88.36
Teacher 74 77.99 16.56 74.15-81.83
Employee 261 78.32 15.65 76.41-80.23
Artisan 29 78.26 18.39 71.27-85.26
Blue collar 191 79.2 18.68 76.27-84.69
Farmer 9 69.67 27.44 26.01-113.33
housekeeping woman 192 74.8 18.78 72.13-77.48
retired 362 77.32 19.28 75.33-79.32
unemployed 61 70.27 21.12 64.86-75.68
Student 189 79.41 14.98 77.26-81.55
Missing 10 87.27 20.8 72.39-102.15
Civil status
Single 429 79.48 16.18 77.94-81.01
Married 938 77.54 18.08 76.38-78.7
Widow 90 72.44 20.35 68.18-76.7
Divorced 43 76.83 17.12 71.56-82.1
Schooling and education
University degree 195 78.35 15.66 76.01-80.69
High school 1240 78.92 15.29 76.2-81.64
Primary school 165 72.23 18.47 63.59-80.88
PGWBI
feature No.
Average S.D I.C
Diseases
No disease 489 83.17 15.23 81.82-84.52
1 disease 360 79.94 16.01 78.28-81.60
2 diseases 264 77.03 18.43 74.79-79.26
3-5 diseases 342 70.90 17.57 69.03-72.77
>5 diseases 45 58.18 22.45 51.43-64.92
Geography
North 696 79.34 17.71 78.02-80.66
Centre 293 78.04 17.12 76.07-80.00
South 511 75.47 17.91 73.92-77.03
Culture
No consumption at all 93 65.4 22.42 60.75-70.04
from 1 to 25 per year 448 74.2 17.72 72.55-75.85
from 26 to 103 per year 467 80.14 15.88 78.70-81.59
over 100 per year 380 81.61 16.18 79.97-83.24
PGWBI
Cinema
0
10
20
30
40
50
60
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
theatre
0
10
20
30
40
50
60
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Opera/ ballett
0
20
40
60
80
100
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Classic music concerts
0
20
40
60
80
100
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Paintings exibitions
0
10
20
30
40
50
60
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Museums
0
10
20
30
40
50
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Novels readings
0
5
10
15
20
25
30
35
0 1_2 3_5 6_0 >10
number/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Poetry books
0
10
20
30
40
50
60
70
80
0 1_2 3_5 6_0 >10
Books/year
72747678808284868890
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Disco
0
10
20
30
40
50
60
70
80
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Sport practice
0
10
20
30
40
50
0 1_2 3_5 6_0 >10
times/year
72747678808284868890
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Sport watching
0
10
20
30
40
50
60
70
0 1_2 3_5 6_0 >10
times/year
74
76
78
80
82
84
86
88
90
0 years 1_2 year 3_5 year 6_10
year
>10 year
well being index
% responders
Change in PGWBI in relation to cumulative cultural
consumption (n=3000). Red line average value of PGWBI
in the overall population (=77.94)
medie pgwbi per classi
60
65
70
75
80
85
0 1-6 7-15 16-26 27-44 45-67 68-106 107-157 158-308 >308
classi
pu
nte
gg
io m
ed
io p
gw
bi
• In order to highlight the variance of each item in relation to the well being, a sub-sample of 2006 subjects from the whole sample was created, satisfying these conditions:
• A. subjects with a PGWB Index lower than 70 (n=973);
• B. subjects with a PGWB Index higher than 85 (n=1033)
• The reasons which have driven the creation of the sub-sample are related to the fact that a linear correlation index between independent variables and target variables were extremely low, no R2 was found. This element gave a further rational to employ potent non linear approximation like artificial neural networks.
• The first evaluation has been driven in order to define the distribution of the independent variable in the two classes (<70 ; >85), and results are shown in table 3
wellbeing
1 cinema 16 miocardial_infarction 31 migraine 46 semi_urban
2 theatre 17 heart_failure 32 gastritis 47 low_income
3 opera_ballett 18 diabetes 33 menopause 48 average_income
4 classic_music 19 angina 34 obesity 49 high_income
5 painting_exibition 20 cancer 35 kidney_diseases 50 Income_no_information
6 museums 21 allergy 36 liver_diseases 51 south
7 romance_books 22 arthritis 37 multiple_sclero1s 52 Centre
8 poetry_book 23 low_back_pain 38 thyroid_diseases 53 North
9 disco 24 blindness 39 Colitis 54 male
10 sport_practice 25 lung_diseases 40 osteoporosis 55 female
11 rock_concerts 26 skin_diseases 41 divorced 56 unemployed
12 jazz_concerts 27 depression 42 age 57 retired
13 sport_watching 28 anemia 43 schooling 58 blue_collar
14 social_activity 29 anxiety 44 urban_area 59 white_collar
15 hypertension 30 osteoarthritis 45 rural_area 60 student
Which variables and how influence wellbeing?
Artificial neural
networks
Use of artificial neural networks to predict wellbeing
Artificial Neural Networks • Artificial Neural Networks (ANNs) are one of the most advanced fields in Artificial Intelligence (AI).
• ANNs are mathematical algorithms are able to “understand” the complex and non linear correlation between series of data and a particular outcome.
• ANNs are powerful tools to compute every kind of continuous functions (linear or non linear)
Scientific Background
Architecture of classical Neural Network
BACK PROPAGATION
HIDDEN
OUTPUT
INPUT (n)
. . . . . 1 2 3 4 5 6 n 7 8
. . . . . 1 2 3 4 5 n
. . . . . 1 2 3 n
Data are processed in a parallel way
Artificial Neural Networks
Artificial neural networks are one of the best example of “artificial intelligence”.
These systems tend to adapt themselves along the time to the problem on study without applying prespecified programs. They are able to modify their internal structure in relation to a function objective.
HIDDEN
OUTPUT
INPUT (n)
. . . . . 1 2 3 4 5 6 7 8
. . . . . 1 2 3 4 5 n
. . . . . 1 2 3 n
n
What is the utility of neural networks?
Neural networks are able to solve
problems of high complexity not
amenable by traditional statistics,
especially when non linear
relationships dominate and when
there is poor comprehension of
underlying interacting factors.
Artificial neural networks
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Complex Artificial Dynamics
VS
Complex Real Dynamics
Non linear equations interact with
experimental data within computer
and influence each other. Hidden rules
spontaneously emerge bottom-up
DATA
THEORY
Time
Best case for ANNs
Neural Network scheme
Inputs
Weights
Output
Independent
variables
Dependent
variable
O2 85%
105 Blood
pressure
Hearth rate 98
.6
.5
.8
.2
.1
.3 .7
.2
Hidden
layer
Death
plausibility
90%
S
S
.4
.2 S
(……)
Prediction
Weights
Training process with ANNs
Input
Artificial
Neural
Network Output
Comparison
Real
example
Weights
evolution
Training phase
Training is an iterative process, where the
data are repeatedly presented to
the network, and training incrementally
improves the model to match the data
more closely as ANNs learn from their own
errors.
Severe validation protocol
Fitting model after
open training
Preliminary
conclusion
Blind testing of
trained model
Definitive
conclusion
50% target 2
50% target 1
Random split Subjects
Target 2
Subjects
Target 1
Whole
data set
50% target 2
50% target 1
• No limitation in the amount of data processed
• No limitation in the different nature of data processed
• No limitation in the degree of complexity of data
processed
• Horizontal rather than vertical view of the data set
• Bottom - up computation: models are data driven
• Interactions among different factors are easily picked-up
• Internal validity of modelling ensured with validation
protocols
• Inference takes place at individual level
Paradigms shift introduced by ANNs in medicine
Machine learning systems
Classical
statistics
86.9%
13.1%
Popularity of statistical
modeling techniques in
medicine
Source: PubMed
What Artificial Neural Networks can do
• Handle simultaneously a very high number of variables irrispectively to their underlying non linearity.
• Build up models taking into account outlayers and interactions among variables
• Riproduce the dynamic interaction of multiple factors allowing the study of complexity
• In other words discover the hidden truth
Artificial Adaptive Systems Tree
Artificial Adaptive Systems
Artificial Neural Networks Evolutionary Systems
From Data to
(optimal) Rules
From Parameters, Rules,
or Constraints to (optimal) Data
Population Oriented
Genetic Algorithms
Genetic Programming
Natural Algorithm
Evolution Strategies
Swarm Intelligence
etc..
Associative Memories
x = f (x,w*), wii = 0
Auto-Poietic ANN
y(n+1) = f (x,y(n),w*)
Space or Time
Prediction / Classification
Function
Approximation
(Value Estimation)
Classifications
(Patterns Recognition)
- Multinomial
- Binomial
Intelligent
Data Mining
C.A.M.
Dynamics
Scenarios’
Simulation
Patterns
Reconstruction
Natural
Clustering
Data
Preprocessing
Self
Classification
Topographic
Mapping
Multi-
Dimensional
Scaling
Supervised ANN
y = f (x,w*)
Goal: linear and non
linear optimization
Algorithm Oriented
Simulate Annealing
Direct Search
Local Search
etc..
1.E+001.E+031.E+061.E+091.E+121.E+151.E+181.E+211.E+241.E+271.E+301.E+331.E+361.E+39
2 4 8 16 32 64 128
N3 2N
Computational time
No. permutations
1 year
Rome foundation
Human race appear
1000
One million
109
1012
1015
1039
1021
N3 2n
1 second
1 day
N=50
Big Bang
N=100
No. variables
The problem of variables selection
• Medical data set usually contain huge amount of information, collected often for administrative rather than scientific reasons.
• Neural networks, being universal approximators, typically employ all available variables to create a predictive model, independently from function linearity.
• If some of the variables are actually reflecting “white noise” rather than true information, the potential of ANNs generalization capability, i.e. the correct classification of new records in the testing phase is markedly reduced.
• There are however no clues how to identify a priori the variables containing the true information for the problem under study.
• So one of the reasons of the limited success of neural networks in medicine is that, paradoxically, their potence is too high.
Artificial Organism Input Selection System (I.S. - Semeion ©)
I.S. System selects the best Input Variables Set
…
Parents Children
A1 A2
B1 B2
A1 B2
B1 A2
Evolution
New Population
…
Input selection system, Semeion
TWIST system
TWIST system is a special kind of artificial organism, and specifically an ensemble of two algorithms: T&T and I.S. previously described) Artificial Neural Networks are able to identify gene combinations (allelic variants) or proteins combinations that are likely to produce accurate predictions for a single individual, improving, not obviously, the results which can be obtained with the separate use of the two algorithms. T&T The “Training and Testing” algorithm (T&T) is an adaptive system based on a population of n ANNs managed by an evolutionary system ( Gen D) I.S. Input Selection algorithm, is an adaptive system, which is also based on the evolutionary algorithm GenD, and which is able to evaluate the relevance of the different variables of the dataset in an intelligent way managing a population of ANNs.
wellbeing distress
ROC AUC = 0.71
29 variables selected
by TWIST system
Preliminary conclusions
• The positive influence of cultural consumption on wellbeing is evident and significant
• In the complex interplay of personal and social determinants of subjective sense of wellbeing attendance at cultural events has a major role in balancing quality of life.
• Without culture the other major 7 main dimensions explain the 52% of the psycological well-being, with culture the 65%.
• Relevant correlation between cultural supply of a territory, consumption / participation and psychological well-being (comparison two local case; Siracusa (Sicily) and Bolzano (Alto Adige).
• Use of artificial neural networks to define interaction scheme among factors examined
Intelligent Data mining We need two things:
• An engine able to create dynamic interactions of all the variables one against all the others, i.e. many to many.
• A potent mathematical filter able to visualize the fundamental information emerging.
Fourth generation neural networks: Auto Contractive Map( Auto-CM)
• A new data mining mapping method able to find out connections among variables by means of an original mathematical approach.
• This method is based on an artificial neural network able to define the strength of the associations of each variable with all the others in the dataset emerging during dynamic interaction
• After the training phase, the weights matrix of Auto-CM represents the warped landscape of the dataset giving rise to a semantic connectivity map.
Features of semantic connectivity map generated by Auto-CM
• Linear and non linear associations are depicted
• Clustering takes place with explicit connections schemes rather than according to near borough.
• The complex dynamics of adaptive interactions is captured
• Very useful when there is poor a priori knowledge of associations ( e.g. genetic polymorphisms)
Auto Contractive Map
Auto Contractive Map (Semeion ©)
INPUT
HIDDEN
OUTPUT
Auto Cm : The Topology
Node
Input : N
Hidden: N
Output: N
Weights
Input - Hidden : N
Hidden - Output: NxN-N
Author: M Buscema, Semeion Research Centre
Auto Contractive Map – Learning Equations
a. Signal transfer from the Input to the Hidden:
(1)
N
vmm nis
i
h
i
)(1][][ where N = Number of Input Nodes
b. Adaptation of the connections )( niv through the iv trapping the
energy difference generated by the equation (1):
(2)
N
vmmv nih
i
s
ii
)(1][][;
(3) iii vvvnn
)()1(
;
c. Signal transfer from the Hidden to the Output:
(4) N
j
ji
h
ji nwmNet
)(,
][;
(5)
N
Netmm ih
i
t
i 1][][;
d. Adaptation of the connections wij through the wij trapping the
energy differences generated by the equation (5):
(6)
N
wmmw njit
i
h
iji
)(,][][
, 1 ;
(7) ][
,,, )()1(
h
jjijiji mwwwnn
* The value ][h
jm of (7) is used for proportioning the change of the connection
jiw , to the quantity of energy liberated by the node ][h
jm in favour of node
][t
im .
Legenda:
Input vector;
Hidden units;
Output vector;
Input-Hidden weights;
Hidden-Output weights;
Input number;
Constant (typically: );
, [1, 2,..., ];
Number of iteraction cycles;
N
C C N
i j N
n
x
h
y
v
w
,
Convergence Condition:
(8) lim 0, .i j in
w v C
[ ]
[ ]
[ 1] [ ]
[ ]
,
1
,
Signal Transfer and Learning:
(1) 1 ;
(2) 1 ;
(3) ;
(4) 1 ;
(5) 1 ;
(6)
n
ii i
n
ii i i i
n n
i i i
nNi j
i j
j
ii i
i j i
vh x
C
vv x h x
C
v v v
wNet h
C
Nety h
C
w h y
[ ]
,
[ 1] [ ]
, , ,
1 ;
(7) .
n
i j
i j
n n
i j i j i j
wh
C
w w w
Auto Cm : The Equations
the Lorentz
contraction
Auto Cm : The knowledge
The weights matrix represents the knowledge extracted by Auto CM
from the data.
The Auto CM final weights matrix can be transformed in different way:
a. Probabilistic Transformation:
w
,
, , ,
1,
1
(1) ; (2) 1; (3) ( | ) .N
i j
i j i i j i j i jNj
i j
j
wp P y p P y h p
w
b. A Linear or a Non Linear Distance Transformation:
,
, , ,(1) (2) i jwC
i j i j i jd C w d e e
c. Contractive Distance Transformation:
1
,
,
,
(1) 1 ;
1 .
i j
i j
i j
wF
C
F
[ ] 2
, , ,(2) ( ) ;R
Eucliden
i j i k j k
k
d x x [ ]
,[ ]
,
,
(3) .
Euclidean
i jAutoCM
i j
i j
dd
F
Auto Contractive Map – Contractive Factor
We have to assume each variable of the dataset as a
vector composed of the all its values. At this point,
the dynamic value of each connection between two
variables represents the local velocity of their mutual
attraction caused by their mutual vectors similarity:
more is the vectors similarity, more is their attraction
speed. When two variables are attracted by each
other, they contract proportionally the original
Euclidean space between them. The limit case is
when two variables are identical: the space
contraction should be infinitive and the two
variables should collapse in the same point.
We can extract from each weight of a trained
AutoCM this specific contractive factor:
1
,
,
,
1 ;
1 .
i j
i j
i j
wF
C
F
At this point, we are able to calculate the contractive
distance between each variable and the other,
modifying the original Euclidean distance with
a specific contractive factor:
[ ] 2
, , ,( ) ;R
Eucliden
i j i k j k
k
d x x
[ ]
,[ ]
,
,
.
Euclidean
i jAutoCM
i j
i j
dd
F
MST as the best visualisation filter
• The mathematical filter able to show the main connection scheme among variables, is Minimum Spanning Tree (MST) algorithm, as for example described by Kruskal (1956).
• MST increases the information load obtained by the map showing the energy minimisation state of the structure under study
• MST can be applied to the matrix of distances obtained with every kind of approach. Its use in medical field is very recent.
Molecola
All 16 of its Spanning Trees Complete Graph
A spanning
tree of a graph
is just a
subgraph that
contains all the
vertices and is
a tree.
A graph may
have many
spanning trees
Minimum Spanning Trees
The Minimum Spanning Tree for a given graph is the Spanning Tree of
minimum cost for that graph.
5
7
2
1
3
4
2
1
3
Complete Graph Minimum Spanning Tree
BIBLIOGRAPHY OF AUTO-CM SYSTEM
20 papers published in peer reviewed
journals
2012
1. Buscema M., Grossi E., The Semantic Connectivity Map: an adapting self-
organizing knowledge discovery method in data bases. Experience in Gastro-
oesophageal reflux disease, Int. J. Data Mining and Bioinformatics, Vol. 2, No. 4,
2008.
2. Buscema M., Grossi E., Snowdon D., Antuono P., Auto-Contractive Maps: an
Artificial Adaptive System for Data Mining. An Application to Alzheimer Disease,
in Current Alzheimer Research, 2008, 5, 481-498.
3.Buscema M, Helgason C, Grossi E, Auto Contractive Maps, H Function and
Maximally Regular Graph: Theory and Applications , Special Session on “Artificial
Adaptive Systems in Medicine : applications in the real world, NAFIPS 2008
(IEEE), New York, May 19-22, 2008.
4.Licastro F, Porcellini E, Chiappelli M, Forti P, Buscema M et al., Multivariable
network associated with cognitive decline and dementia, int Neurobiology of
Aging, Vol. 1, Issue 2, February 2010, 257-269.
5. Massimo Buscema and Pier L. Sacco, Auto-contractive Maps, the H Function,
and the Maximally Regular Graph (MRG): A New Methodology for Data Mining, in
V. Capecchi et al. (eds.), Applications of Mathematics in Models, Artificial Neural
Networks and Arts, Chapter 11, DOI 10.1007/978-90-481-8581-8_11, Springer
Science+Business Media B.V. 2010.
6. Federico Licastro, Elisa Porcellini, Paola Forti, Massimo Buscema, Ilaria
Carbone, Giovanni Ravaglia, Enzo Grossi, Multi factorial interactions in the
pathogenesis pathway of Alzheimer’s disease: a new risk charts for prevention
of dementia, Immunity & Ageing 2010, 7(Suppl 1):S4.
7.Enzo Grossi, Giorgio Tavano Blessi, Pier Luigi Sacco, Massimo Buscema,
The Interaction Between Culture, Health and Psychological Well-Being: Data
Mining from the Italian Culture and Well-Being Project, J Happiness Studies,
Springer, 2011
8. C Eller-Vainicher, V V Zhukouskaya,Y V Tolkachev, S S Koritko, E Cairoli, E
Grossi, P Beck-Peccoz, I Chiodini, A P Shepelkevich, Low BoneMineral Density
and Its Predictors in Type 1 Diabetic Patients Evaluated by the Classic
Statistics and Artificial Neural Network Analysis, DIABETES CARE , pp 1-6,
2011.
9.T Gomiero, L Croce, E Grossi, L De Vreese, M Buscema, U Mantesso, E De
Bastiani, A Short Version of SIS (Support Intensity Scale): The Utility of the
Application of Artificial Adaptive Systems, US-China Education Review A 2
(2011) 196-207.
10.Enzo Grossi, Angelo Compare, Massimo Buscema (2012) The concept of
individual semantic maps in clinical psychology: a feasibility study on a new
paradigm Quality & Quantity published on line: august 4th.
11.Licastro F, Porcellini E, Forti P, Buscema M, Carbone I, Ravaglia G, Grossi
E.
Multi factorial interactions in the pathogenesis pathway of Alzheimer's
disease: a new risk charts for prevention of dementia. Immun Ageing. 2010
Dec 16;7 Suppl 1:S4. doi: 10.1186/1742-4933-7-S1-S4.
12. Licastro F, Chiappelli M, Porcellini E, Campo G, Buscema M, Grossi E,
Garoia F, Ferrari R. Gene-gene and gene - clinical factors interaction in acute
myocardial infarction: a new detailed risk chart. Curr Pharm Des.
2010;16(7):783-8.
13.Buscema M, Penco S, Grossi E. A Novel Mathematical Approach to Define
the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic
Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Graph
Theory.
Neurol Res Int. 2012;2012:478560.
14.Angelo Compare, Enzo Grossi, Massimo Buscema, Cristina Zarbo, Xia
Mao, Francesco Faletra, Elena Pasotti, Tiziano Moccetti, Paula M C
Mommersteeg,Angelo Auricchio (2013) Combining personality traits with
traditional risk factors for coronary stenosis: an artificial neural networks
solution in patients with computed tomography detected coronary artery
disease. Cardiovascular psychiatry and neurology 2013:
15.Fabio Coppedè, Enzo Grossi, Massimo Buscema, Lucia Migliore (2013)
Application of artificial neural networks to investigate one-carbon metabolism in
Alzheimer's disease and healthy matched individuals. PloS one 8: 8. 08.
16.Maira Gironi, Marina Saresella, Marco Rovaris, Matilde Vaghi, Raffaello Nemni,
Mario Clerici, Enzo Grossi (2013) A novel data mining system points out hidden
relationships between immunological markers in multiple sclerosis. Immun Ageing
10: 1. January.
17.Maurizio Gallucci, Andrea Zanardo, Matteo Bendini, Francesco Di Paola, Paolo
Boldrini, Enzo Grossi (2014) Serum Folate, Homocysteine, Brain Atrophy, and
Auto-CM System: The Treviso Dementia (TREDEM) Study. Journal of Alzheimer's
disease 38: 581-587.
18.Enzo Grossi, Gianmarco Podda, Mariateresa Pugliano, gabba Silvia, Verri
Annalisa, Giovanni Carpani, Massimo Buscema, Giovanni Casazza, Marco
Cattaneo (2014) Prediction of optimal warfarin maintenance dose using advanced
artificial neural networks Pharmacogenomics 15: 1. 29 - 37.
Fabio Coppedè, Enzo Grossi, Angela Lopomo, Roberto Spisni, Massimo Buscema, Lucia Migliore
Soggetti di età > 65 anni
Conclusions and future steps
• Refining the research in terms of specific populations (elderly, children, etc.), of specific classes of pathologies, and of specific forms of cultural participation (protocols), and developing a more articulate and differentiated perception of the benefits and pitfalls of cultural participation
• Starting to develop studies on active participation by designing and refining protocols through field studies
• Evaluating the impact of subjective well being on hospitalization and medicalization rates and estimating the corresponding impact on welfare costs
• Exploring alternative psychological wellbeing measures and estimating sensitivity of results to the specific measure adopted
Conclusions and future steps
Thanks for your attention!