Difference between revisions of "'The logic of the probabilistic language'"

== Abstract ==

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Finally, the theme of interdisciplinarity in scientific research is addressed, highlighting the importance of an interdisciplinary approach to tackling complex problems. Fuzzy logic is also mentioned as a possible tool for managing uncertainty in medical contexts.

This section examines the internal uncertainties that individuals may experience when faced with a diagnosis, using Mary Poppins as a fictional reference. The concepts of subjective and objective uncertainties are explored:

* '''Subjective Uncertainty''': This type of uncertainty is dependent on an individual's knowledge and beliefs. It is highlighted that objectivity in science is often a shared consensus of subjective views, termed as intersubjectivity.

* '''Causality''': The relationship between causality and uncertainty is discussed in the medical context. Using mathematical expressions, it is shown how medical phenomena do not always follow a deterministic pattern but are rather influenced by probabilistic factors.

This chapter revisits the discussions from Kazem Sadegh-Zadeh's work on the logic of medical language and applies it to the clinical case of Mary Poppins. It elaborates on how events are categorized as probable based on their randomness and subjective uncertainty:

* '''Subjective Probability''': Involves individual belief and varies according to the information available to the person making the assessment.

* '''Objective Probability''': In contrast, objective probability is based on empirical evidence and statistical data, reflecting a more quantifiable aspect of probability.

Probabilistic-causal analysis is used to describe how clinical diagnoses are formulated. This involves assessing the likelihood of various conditions based on observed probabilities:

* The text details the process of causal relevance and how it can be used to infer potential diagnoses from given data sets.

* A specific focus is placed on the interpretation of conditional probabilities and how they can aid in the differential diagnosis.

== Introdduction==

Every scientific idea—whether in medicine, architecture, engineering, chemistry, or any other field—when implemented, is prone to small errors and uncertainties. Mathematics, through the lens of probability theory and statistical inference, aids in precisely managing and thereby mitigating these uncertainties. It must always be considered that in all practical scenarios, "the outcomes also depend on many other external factors to the theory," be they initial and environmental conditions, experimental errors, or others.

Causality indicates the lack of a certain connection between cause and effect. The uncertainty of a close union between the source and the phenomenon is among the most challenging problems in determining a diagnosis.