Vitamin Information Center

Biostatistical Design
Definition
Biostatistical design is a unified approach to a common
core of problems of statistical design that are central to
many related fields in the biomedical sciences, in the
health sciences, in the social sciences and in health services
research led by three fundamental principles: 1)
all problems occur in a system of interconnected processes,
2) variation exists in all processes, and 3) understanding
and reducing variation are the keys of success.
It covers at least the following elements: identification
of the data to be collected (this includes the variables to
be measured, their role in a study, ways of measurement,
the number of experimental units, namely, the
size of the study, and the way they were chosen and
followed-up); the design of a comparison/relationship
strategy; an appropriate analytic model for describing
and processing data; and a list of questions to be
answered throughout the study (What inferences does
one hope to make from the study? What conclusions
might one draw from the study? To what population(s)
is/are the conclusion(s) applicable)?

Autochthonous Population
Synonyms
Natives; Original inhabitants
Definition
Autochthonous population is a general and more neutral
term for natives or original inhabitants of a country
or region. The term ‘autochthonous’ is used to avoid
static ideas implied in terms like ‘native’ or ‘original’.
The terms ‘allochthonous’ population and ‘immigrants’
can be seen as synonyms. The difference between
autochthonous and allochthonous populations is not
absolute – it is a relative one. The difference depends
on both context and time, combined with a range of
variables (e. g., ideas of origin, legal status, social inclusion
and status, ethnic or racial background, religion) in
a specific national or regional context.

Association
Synonyms
Correlation; (Statistical) dependence; Relationship
Definition
An association is a statistical dependence between two
or more events, characteristics, or other variables. An
association is present if the probability of occurrence
of an event or characteristic, or the quantity of a variable,
depends upon the occurrence of one or more other
events, the presence of one or more other characteristics,
or the quantity of one or more other variables. The
association between two variables is described as positive
when the occurrence of higher values of a variable
is associated with the occurrence of higher values
of another variable. In a negative association, the occurrence
of higher values of one variable is associated with
lower values of the other variable.

Artificial Neural Network
Synonyms
Neural network
Definition
An analytic modeling technique modeled after the
(hypothesized) processes of learning in the cognitive
system and the neurological functions of the brain.
It is capable of predicting new observations (on specific
variables) from other observations (on the same
or other variables) after executing a process of socalled
learning from existing data. Artificial neural networks
(ANN) are nonlinear and capable of modeling
extremely complex functions by creating connections
between processing elements – the computer equivalent
of neurons. For example, the onset of a particular
medical condition could be associated with a very
complex (e. g., nonlinear and interactive) combination
of changes on a subset of the variables being monitored
(e. g., a combination of heart rate, levels of various
substances in the blood, respiration rate). Neural
networks have been used to recognize this predictive
pattern so that the appropriate treatment can be prescribed.
A distinction can be made between two different
types of ANN– networks designed for supervised
learning tasks (e. g., Multilayer Perceptron, Bayesian
networks, Genetic algorithms) and networks primarily
designed for unsupervised learning (Self Organizing
Feature Map (SOFM, or Kohonen) networks).

Artificial Neural Network
Synonyms
Neural network
Definition
An analytic modeling technique modeled after the
(hypothesized) processes of learning in the cognitive
system and the neurological functions of the brain.
It is capable of predicting new observations (on specific
variables) from other observations (on the same
or other variables) after executing a process of socalled
learning from existing data. Artificial neural networks
(ANN) are nonlinear and capable of modeling
extremely complex functions by creating connections
between processing elements – the computer equivalent
of neurons. For example, the onset of a particular
medical condition could be associated with a very
complex (e. g., nonlinear and interactive) combination
of changes on a subset of the variables being monitored
(e. g., a combination of heart rate, levels of various
substances in the blood, respiration rate). Neural
networks have been used to recognize this predictive
pattern so that the appropriate treatment can be prescribed.
A distinction can be made between two different
types of ANN– networks designed for supervised
learning tasks (e. g., Multilayer Perceptron, Bayesian
networks, Genetic algorithms) and networks primarily
designed for unsupervised learning (Self Organizing
Feature Map (SOFM, or Kohonen) networks).