Today, the term “translation” is used in a variety of ways to reflect the rich
connotations of English while at the same time making the implications of each
discipline more effective. In philosophy, “translation” means “understanding, interpretation, and hermeneutics.” “Translation” is used in linguistics as a term that
implies “meaning, conceptualization, interpretation, and metaphor.” “Translation”
in anthropology means “encounter between others and yourself” (Blumczynski,
2016). When used as “translational research” in the medical field, the adjective
“translational” refers to the “translation” of basic scientific findings in a laboratory
setting into potential treatments for disease (Woolf, 2008; Reis et al., 2010; Agency
for Healthcare Research and Quality, 2017). The concept of translational research is
originated from medical science for enhancing human health and well-being. Translational medical research is often labeled as the “bench-to-bedside” approach. It
places emphasis on translating the findings in basic research (at bench) more quickly
and efficiently into medical practice (at bedside) (Kijima, 2015).
Thus, the word “translationality” has become a word representing rich metaphor
for various disciplines. Hence, the position of this book is to respect the definition of
“translationality” in each individual discipline and at the same time to redefine and
utilize “translationality” from the viewpoint of systems science. Systems science is
an interdisciplinary field that studies the nature of systems, from simple to complex,
in nature, society, cognition, engineering, technology, and science itself. To systems
scientists, the world can be understood as a system of systems (Mobus & Kalton,
2015).
3 Lens of Translational Systems Science
Translational systems science is a new trend within systems sciences motivated by
the need for practical applications that help people by a holistic, comprehensive, and
systems thinking on problematic complexity (Kijima, 2015 p40). This book focuses
on the aforementioned innovation-oriented health information. Innovations include
big data, AI, IoT, neural networks, and brain–computer interface (BCI). The term
innovation was conceptualized by Schumpeter. Schumpeter’s vision, which
regarded the new combination as a source of innovation, has gained a good eye.
However, during Schumpeter’s time, attention to information was extremely low
compared to the present. Therefore, Schumpeter’s insights into the importance of the
information in the new combination were unfortunately limited.
Let us now complement Schumpeter’s discourse. As he stated, the source of
innovation is a new combination of existing things (Schumpeter & Opie, 1934).
Information and context connect different things. They need to be connected for
things to adopt new combinations. A context is a story that incorporates human
intentions and goals. Therefore, in many cases, human beings spin the context.
However, in recent years, AI has been trying to carry out this human-specific
behavior. In a sense, the future where the context is automatically generated even
without human intervention is coming up soon.
4
H. Matsushita
connotations of English while at the same time making the implications of each
discipline more effective. In philosophy, “translation” means “understanding, interpretation, and hermeneutics.” “Translation” is used in linguistics as a term that
implies “meaning, conceptualization, interpretation, and metaphor.” “Translation”
in anthropology means “encounter between others and yourself” (Blumczynski,
2016). When used as “translational research” in the medical field, the adjective
“translational” refers to the “translation” of basic scientific findings in a laboratory
setting into potential treatments for disease (Woolf, 2008; Reis et al., 2010; Agency
for Healthcare Research and Quality, 2017). The concept of translational research is
originated from medical science for enhancing human health and well-being. Translational medical research is often labeled as the “bench-to-bedside” approach. It
places emphasis on translating the findings in basic research (at bench) more quickly
and efficiently into medical practice (at bedside) (Kijima, 2015).
Thus, the word “translationality” has become a word representing rich metaphor
for various disciplines. Hence, the position of this book is to respect the definition of
“translationality” in each individual discipline and at the same time to redefine and
utilize “translationality” from the viewpoint of systems science. Systems science is
an interdisciplinary field that studies the nature of systems, from simple to complex,
in nature, society, cognition, engineering, technology, and science itself. To systems
scientists, the world can be understood as a system of systems (Mobus & Kalton,
2015).
3 Lens of Translational Systems Science
Translational systems science is a new trend within systems sciences motivated by
the need for practical applications that help people by a holistic, comprehensive, and
systems thinking on problematic complexity (Kijima, 2015 p40). This book focuses
on the aforementioned innovation-oriented health information. Innovations include
big data, AI, IoT, neural networks, and brain–computer interface (BCI). The term
innovation was conceptualized by Schumpeter. Schumpeter’s vision, which
regarded the new combination as a source of innovation, has gained a good eye.
However, during Schumpeter’s time, attention to information was extremely low
compared to the present. Therefore, Schumpeter’s insights into the importance of the
information in the new combination were unfortunately limited.
Let us now complement Schumpeter’s discourse. As he stated, the source of
innovation is a new combination of existing things (Schumpeter & Opie, 1934).
Information and context connect different things. They need to be connected for
things to adopt new combinations. A context is a story that incorporates human
intentions and goals. Therefore, in many cases, human beings spin the context.
However, in recent years, AI has been trying to carry out this human-specific
behavior. In a sense, the future where the context is automatically generated even
without human intervention is coming up soon.
4
H. Matsushita