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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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%

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Hearth rate 98

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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!