Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Communicating climate change: conduits, content, and consensus

Climate change has been the subject of increasing efforts by scientists to understand its causes and implications; it has been of growing interest to policymakers, international bodies, and a variety of nongovernment organizations; and it has attracted varied amounts of attention from traditional and, increasingly, online media. These developments have been aligned with shifts in the nature of climate change communication, with changes in how researchers study it and how a variety of actors try to influence it. This article situates the theory and practice of climate change communication within developments that have taken place since we first reviewed the field in 2009. These include the rise of new social media conduits for communication, research, and practice aimed at fine tuning communication content, and the rise to prominence of scientific consensus as part of that content. We focus in particular on continuing tensions between a focus on the part of communicators to inform the public and more dialogic strategies of public engagement. We also consider the tension between efforts to promote consensus and certainty in climate science and approaches that attempt to engage with uncertainty more fully. We explore the lessons to be learnt from climate communication since 2009, highlighting how the field remains haunted by the deficit model of science communication. Finally, we point to more fruitful future directions for climate change communication, including more participatory models that acknowledge, rather than ignore, residual uncertainties in climate science in order to stimulate debate and deliberation

Explicating ways of consensus-making in science and society: distinguishing the academic, the interface and the meta-consensus

In this chapter, we shed new light on the epistemic struggle between establishing consensus and acknowledging plurality, by explicating different ways of consensus-making in science and society and examining the impact hereof on their field of intersection, i.e. consensus conferences (in particular those organized by the National Institute of Health). We draw a distinction between, what we call, academic and interface consensus, to capture the wide appeal to consensus in existing literature. We investigate such accounts - i.e. from Miriam Solomon, John Beatty and Alfred Moore, and Boaz Miller - as to put forth a new understanding of consensus-making, focusing on the meta-consensus. We further defend how (NIH) consensus conferences enable epistemic work, through demands of epistemic adequacy and contestability, contrary to the claim that consensus conferences miss a window for epistemic opportunity (Solomon M, The social epistemology of NIH consensus conferences. In: Kincaid H, McKitrick J (ed) Establishing medical reality: methodological and metaphysical issues in philosophy of medicine. Springer, Dordrecht, 2007). Paying attention to the dynamics surrounding consensus, moreover, allows us to illustrate how the public understanding of science and the public use of the ideal of consensus could be well modified

Neither fair nor unchangeable but part of the natural order: orientations towards inequality in the face of criticism of the economic system

The magnitude of climate change threats to life on the planet is not matched by the level of current mitigation strategies. To contribute to our understanding of inaction in the face of climate change, the reported study draws upon the pro status quo motivations encapsulated within System Justification Theory. In an online questionnaire study, participants (N = 136) initially completed a measure of General System Justification. Participants in a “System-critical” condition were then exposed to information linking environmental problems to the current economic system; participants in a Control condition were exposed to information unrelated to either environmental problems or the economic system. A measure of Economic System Justification was subsequently administered. Regressions of Economic System Justification revealed interactions between General System Justification and Information Type: higher general system justifiers in the System-critical condition rated the economic system as less fair than did their counterparts in the Control condition. However, they also indicated inequality as more natural than did their counterparts in the Control condition. The groups did not differ in terms of beliefs about the economic system being open to change. The results are discussed in terms of how reassurance about the maintenance of the status quo may be bolstered by recourse to beliefs in a natural order

Abrupt global events in the Earth's history: a physics perspective

The timeline of the Earth's history reveals quasi-periodicity of the geological record over the last 542 Myr, on timescales close, in the order of magnitude, to 1 Myr. What is the origin of this quasi-periodicity? What is the nature of the global events that define the boundaries of the geological time scale? I propose that a single mechanism is responsible for all three types of such events: mass extinctions, geomagnetic polarity reversals, and sea-level fluctuations. The mechanism is fast, and involves a significant energy release. The mechanism is unlikely to have astronomical causes, both because of the energies involved, and because it acts quasi-periodically. It must then be sought within the Earth itself. And it must be capable of reversing the Earth's magnetic field. The last requirement makes it incompatible with the consensus model of the origin of the geomagnetic field - the hydromagnetic dynamo operating in the Earth's fluid core. In the second part of the paper, I show that a vast amount of seemingly unconnected geophysical and geological data can be understood in a unified way if the source of the Earth's main magnetic field is a ~200-km-thick lithosphere, repeatedly magnetized as a result of methane-driven oceanic eruptions, which produce ocean flow capable of dynamo action. The eruptions are driven by the interplay of buoyancy forces and exsolution of dissolved gas, which accumulates in the oceanic water masses prone to stagnation and anoxia. Polarity reversals, mass extinctions, and sequence boundaries are consequences of these eruptions. Unlike the consensus model of geomagnetism, this scenario is consistent with the paleomagnetic data showing that "directional changes during a [geomagnetic polarity] reversal can be astonishingly fast, possibly occurring as a nearly instantaneous jump from one inclined dipolar state to another in the opposite hemisphere".Comment: Final journal version. New title, significant changes. Supersedes v.

Experimentation on Analogue Models

Summary Analogue models are actual physical setups used to model something else. They are especially useful when what we wish to investigate is difficult to observe or experiment upon due to size or distance in space or time: for example, if the thing we wish to investigate is too large, too far away, takes place on a time scale that is too long, does not yet exist or has ceased to exist. The range and variety of analogue models is too extensive to attempt a survey. In this article, I describe and discuss several different analogue model experiments, the results of those model experiments, and the basis for constructing them and interpreting their results. Examples of analogue models for surface waves in lakes, for earthquakes and volcanoes in geophysics, and for black holes in general relativity, are described, with a focus on examining the bases for claims that these analogues are appropriate analogues of what they are used to investigate. A table showing three different kinds of bases for reasoning using analogue models is provided. Finally, it is shown how the examples in this article counter three common misconceptions about the use of analogue models in physics

Climate stories: Why do climate scientists and sceptical voices participate in the climate debate?

Public perceptions of the climate debate predominantly frame the key actors as climate scientists versus sceptical voices; however, it is unclear why climate scientists and sceptical voices choose to participate in this antagonistic and polarised public battle. A narrative interview approach is used to better understand the underlying rationales behind 22 climate scientists’ and sceptical voices’ engagement in the climate debate, potential commonalities, as well as each actor’s ability to be critically self-reflexive. Several overlapping rationales are identified including a sense of duty to publicly engage, agreement that complete certainty about the complex assemblage of climate change is unattainable and that political factors are central to the climate debate. We argue that a focus on potential overlaps in perceptions and rationales as well as the ability to be critically self-reflexive may encourage constructive discussion among actors previously engaged in purposefully antagonistic exchange on climate change

Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling

Background Energy scenarios that are used for policy advice have ecological and social impact on society. Policy measures that are based on modelling exercises may lead to far reaching financial and ecological consequences. The purpose of this study is to raise awareness that energy modelling results are accompanied with uncertainties that should be addressed explicitly. Methods With view to existing approaches of uncertainty assessment in energy economics and climate science, relevant requirements for an uncertainty assessment are defined. An uncertainty assessment should be explicit, independent of the assessor’s expertise, applicable to different models, including subjective quantitative and statistical quantitative aspects, intuitively understandable and be reproducible. Bayesian model averaging for input variables of energy models is discussed as method that satisfies these requirements. A definition of uncertainty based on posterior model probabilities of input variables to energy models is presented. Results The main findings are that (1) expert elicitation as predominant assessment method does not satisfy all requirements, (2) Bayesian model averaging for input variable modelling meets the requirements and allows evaluating a vast amount of potentially relevant influences on input variables and (3) posterior model probabilities of input variable models can be translated in uncertainty associated with the input variable. Conclusions An uncertainty assessment of energy scenarios is relevant if policy measures are (partially) based on modelling exercises. Potential implications of these findings include that energy scenarios could be associated with uncertainty that is presently neither assessed explicitly nor communicated adequately