Complexity Sciences Dramatically Improve Biomarker Research and Use

Abstract

This presentation argues that Complexity Sciences already dramatically improve medical biomarkers as part of a sea-change regarding: actionable clinical biomarker uses, search strategies, and future research. This medical sector-wide impact is unevenly known and appreciated. Complexity Sciences are contributing significantly to understanding biomarkers across the dimensions of the human phenotype because Complexity Sciences advantageously explain the changes in humans which characterize the continuous dynamic variability of their existence. Complexity sciences contribute significantly to biomarkers search and applications because humans in the context of existence and evolution are continuously dynamically variable—and it happens that complexity sciences and related maths successfully provide advanced concepts and enhanced measures of the emergent, self-organizing, nonlinear, always changing human interactions with self, others, and environments—built and natural. Scientific methods of research and application call for measurement and methods to be in accordance with the subject or our study. The Complexity Sciences multiple fields of research are theories which, like all theories, are about change. Complexity Sciences methods and measures are a now decades old set of validated concepts, methods, and measures highly suitable to explain complex signals bioinformatics, as studied here—and to contribute to other medical research and clinical topics across the breadth of healthcare. Examples to illustrate clinical practice and clinical studies of nonlinear dynamic biomarkers will describe Vital Sign Variability—HRVD (Heart Rate Variability Dynamics), Respiratory Rate, Temperature Curve Complexity, and Blood Pressure Variability. Other examples from the broader literature on dynamic biomarkers will be cited. The contributions of complexity sciences and nonlinear analytics are part of a broader Nonlinear Dynamic Turn across multiple aspects of human endeavor—medicine, biology, psychology, physiology, and economics, inter alia. A brief section will mention how this Turn moves away from “average” or summary statistics, which can be inadequate for ontological issues (how change characterizes humans) and epistemological (how do we measure change in humans). The last section Extensions and Futures points to the “other half of the sky” owned by psychological aspects of humans and the need to extend variability analysis and integrate psychological, as a broad term, interaction dynamics. Other extensions and futures involve: moving biomarkers into practice, developing variability indices, and expanding collaborative applied research.