Holistics 3.0 for Health

Abstract

Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.

Estimating the global abundance of ground level presence of particulate matter (PM2.5)

“…Numerous studies show that among air pollutants, particulate matter (PM), especially with a diameter of 2.5 microns or less (PM 2.5 ), has the strongest link with human health effects (Brook et al, 2010a(Brook et al, ,b, 2013aPope et al, 2011;Lary et al, 2014). Increased morbidity and mortality has been associated with exposure to PM 2.5 suggesting that improved life expectancy is possible by reducing the exposure level .…”

Advanced Metrics for Assessing Holistic Care: The “Epidaurus 2” Project

“… 69 Socioeconomic determinants of health, to include income, education, and childhood trauma, should probably be included as well. 68 , 70 – 72 Such approaches go well beyond the Green Road Index; they demonstrate the overall load and complexity of stressors on the body as well as the prognosis for future health problems. Developing such indexes is feasible, either by multiple regression against global outcomes such as mortality, days lost to illness, or disability, or (more simply) by ML paradigms.…”

“…Investigators wished to determine possible causal factors in the well-known relationship of low socioeconomic status with mortality. 68 , 70 , 72 , 74 This relationship operates through a myriad of variables, many of which have nonlinear or unknown effects on health. In 2012, Lary, Woolf and colleagues applied ML techniques to socioeconomic data from the US Census and mortality data from the state of California: they related more than 13,000 socioeconomic variables to mortality rates.…”

Remote Sensing and Geospatial Technologies in Public Health

“…With the availability of large amounts of multidimensional data, new techniques are emerging which apply these data to a wide range of applications, including those of health. In their article, "Holistics 3.0 for Health", Lary et al present a holistic system suitable for complex health studies; this system combines multiple big datasets and multivariate computational techniques, also incorporating new geospatial techniques [12].…”