Understanding mechanisms in the health sciences

Manipulative evidence and medical interventions: some qualifications

The notion of causal evidence in medicine has been the subject of wide philosophical debate in recent years. The notion of evidence has been discussed mostly in connection with Evidence Based Medicine and, more in general, with the assessment of causal nexus in medical, and especially research contexts. "Manipulative evidence" is one of the notions of causal evidence that has stimulated much debate. It has been defined in slightly different ways, attributed different relevance, and recently placed at the core of Gillies' "action-related theory of causality", a view specifically meant to address causation in medicine. While in general sympathetic to Gillies' account, and totally convinced of the relevance of manipulative evidence and different sorts of interventions in the biomedical sciences, we believe that some further qualifications are needed to allow the notion of manipulative evidence to better express features of medical practice. In particular, we provide some qualification of the role of "interventional evidence" proposed by Gillies, suggesting a distinction between "interventional evidence" and "evidence for interventions". A case study from research on rare diseases is analyzed in depth and a multifaceted notion of manipulative evidence put forward that allows better understanding of what manipulations in medical contexts amount to and what their targets are.

BiOnt: Deep Learning Using Multiple Biomedical Ontologies for Relation Extraction

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93% points for drug-drug interactions (DDI corpus), 4.99% points for phenotype-gene relations (PGR corpus), and 2.21% points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiONT.

What is mechanistic evidence, and why do we need it for evidence-based policy?

It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so. Although agreeing with the spirit of this proposal, we argue that the underlying conception of mechanistic evidence as evidence that is different in kind from correlational, difference-making or statistical evidence, does not correctly capture the role that information about mechanisms should play in evidence-based policy. We offer an alternative account of mechanistic evidence as information concerning the causal pathway connecting the policy intervention to its outcome. Not only can this be analyzed as evidence of difference-making, it is also to be found at any level and is obtainable by a broad range of methods, both experimental and observational. Using behavioral policy as an illustration, we draw the implications of this revised understanding of mechanistic evidence for debates concerning policy extrapolation, evidence hierarchies, and evidence integration.

Medicalization and epistemic injustice

Many critics of medicalization (the process by which phenomena become candidates for medical definition, explanation and treatment) express concern that the process privileges individualised, biologically grounded interpretations of medicalized phenomena, inhibiting understanding and communication of aspects of those phenomena that are less relevant to their biomedical modelling. I suggest that this line of critique views medicalization as a hermeneutical injustice--a form of epistemic injustice that prevents people having the hermeneutical resources available to interpret and communicate significant areas of their experience. Interpreting the critiques in this fashion shows they frequently fail because they: neglect the ways in which medicalization may not obscure, but rather illuminate, individuals' experiences; and neglect the testimony of those experiencing first-hand medicalized problems, thus may be guilty of perpetrating testimonial injustice. However, I suggest that such arguments are valuable insofar as they highlight the unwarranted epistemic privilege frequently afforded to medical institutions and medicalized models of phenomena, and a consequent need for greater epistemic humility on the part of health workers and researchers.

Mechanisms in biomedical ontology

The concept of a mechanism has become a standard proposal for explanations in biology. It has been claimed that mechanistic explanations are appropriate for systems biology, because they occupy a middle ground between strict reductionism and holism. Because of their importance in the field a formal ontological description of mechanisms is desirable. The standard philosophical accounts of mechanisms are often ambiguous and lack the clarity that can be provided by a formal-ontological framework. The goal of this paper is to clarify some of these ambiguities and suggest such a framework for mechanisms. Taking some hints from an "ontology of devices" I suggest as a general approach for this task the introduction of functional kinds and functional parts by which the particular relations between a mechanism and its components can be captured.

Causation and melanoma classification

In this article, I begin by giving a brief history of melanoma causation. I then discuss the current manner in which malignant melanoma is classified. In general, these systems of classification do not take account of the manner of tumour causation. Instead, they are based on phenomenological features of the tumour, such as size, spread, and morphology. I go on to suggest that misclassification of melanoma is a major problem in clinical practice. I therefore outline an alternative means of classifying these tumours based on causal factors. By analogy with similar systems that have recently emerged for other cancers, I suggest that this causal classification is likely to be both workable and helpful, even in the absence of a full causal-mechanistic understanding of the aetiology of the tumour.

Research traditions and evolutionary explanations in medicine

In this article, I argue that distinguishing 'evolutionary' from 'Darwinian' medicine will help us assess the variety of roles that evolutionary explanations can play in a number of medical contexts. Because the boundaries of evolutionary and Darwinian medicine overlap to some extent, however, they are best described as distinct 'research traditions' rather than as competing paradigms. But while evolutionary medicine does not stand out as a new scientific field of its own, Darwinian medicine is united by a number of distinctive theoretical and methodological claims. For example, evolutionary medicine and Darwinian medicine can be distinguished with respect to the styles of evolutionary explanations they employ. While the former primarily involves 'forward looking' explanations, the latter depends mostly on 'backward looking' explanations. A forward looking explanation tries to predict the effects of ongoing evolutionary processes on human health and disease in contemporary environments (e.g., hospitals). In contrast, a backward looking explanation typically applies evolutionary principles from the vantage point of humans' distant biological past in order to assess present states of health and disease. Both approaches, however, are concerned with the prevention and control of human diseases. In conclusion, I raise some concerns about the claim that 'nothing in medicine makes sense except in the light of evolution'.