Difference between revisions of "Dissertation Ideas"

From BrothersBrothers
(8/17/2011)
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#Kuhn's definition of Disciplinary Matrix provides a way to frame what medicine is as a conceptual community as opposed to a practice (exemplars, values, etc), also Polanyi's "tacit knowledge"
 
#Kuhn's definition of Disciplinary Matrix provides a way to frame what medicine is as a conceptual community as opposed to a practice (exemplars, values, etc), also Polanyi's "tacit knowledge"
 
#Clearly, practical reasoning (Aquinas and MacIntyre) is very important here, as well.
 
#Clearly, practical reasoning (Aquinas and MacIntyre) is very important here, as well.
 +
==8/28/2011==
 +
#Misunderstandings about risk derive from more fundamentals blindspots about role of mathematical models and values in science
 +
#Scientific theories, framed as mathematical models, are what Berkeley call "mathematical hypotheses." The world does not exist as numbers of mathematical equations; mathematical models are derived from observation and gain support as they successfully represent, and sometimes predict, the way a particular natural event occurs.
 +
#*Possible example here would be an article about something in the natural world, such as modeling [[http://io9.com/5834811/astrophysicists-produce-first-realistic-simulation-of-milky-ways-formation the formation of the Milky Way]].
 +
#As Kuhn observes, paradigm shifts in science occur when paradigms, often based on specific mathematical models, are viewed as failing to respond to problems. This has traditionally assumed to mean that the model fails to predict or represent certain features of the field of study (such as the Ptolemaic models' increasing complexity as it required modifications to fit observed patterns in the solar system).
 +
#There are a number of reasons that such a model may fail, however.  A model might fail because it predicts a outcomes based on a certain set of variables. When a new variable comes to be recognized as having an effect on such systems, mathematical models that don't account for that variable could be viewed as failing.  Possible examples include something like cervical cancer, when HPV came to be appreciated as the significant cause or stomach cancer + H. Pylori.  Obesity would be a great example of the way that various models repeatedly fail as factors thought to be important fail to carry explanatory power: first endocrine, then psychological, then much later genetics.
 +
#Ice cube melting is an example of a process that can be modeled quite successfully. This is because the important variables are assumedly all now known, and ice cubes can be represented quite effectively in models, since their characteristics are quite homogenous. (this is from Gorowitz and MacIntyre)
 +
#However, more complex phenomenon like hurricanes cannot be modeled with complete accuracy.  This is not, however, because the important variables are not known. On the contrary, the appreciation of physics related to air flow over warm water is quite well understood.  In this way, the general features of hurricanes can be modeled effectively. But the reason that specific hurricanes cannot be modeled successfully is that inputs into such models in the real world are large in number, complex, and difficult to collect through available technologies.  So the paths of hurricanes are represented as probability paths.
 +
#Human risk for disease is similar. Many of the causes of many disease are well appreciated. The cause of CF has been known since ____. However, the course of health and wellness are extraordinarily complex such that in many cases the variables that are important in the development of a range of disease are not all known, even though some important inputs may be known.  In addition, the histories of humans are even more complex than that of earthquakes, and infinitely more so than ice cubes (again, these examples are those of Gorowitz and MacIntyre).  The ability to predict the onset of disease in an individual, then, varies on the basis of the comprehensive of available models in terms of the number of variables it can take account of, and also the inputs that are present in the history and ongoing life story of patients.
 +
#Risk estimates for individuals emerge within this context of incomplete knowledge of biological systems and incomplete knowledge of particulars. This fact is demonstrated by the availability of co-existing models to predict disease. For example, MI risk estimate tools.  The online Mayo Tool says one thing, 23inMe says another, etc. Each of these models is incomplete in the number of variables it is able to account for, and even the inputs it takes into account are snapshots, they can't account for the history of individuals, such as changes in cholesterol over time.
 +
#A significant problem with risk estimates, then, is that they represent predictions for the future in mathematical terms in a way that explicitly communicates a probability (Your risk for having an MI is 20%), but lacks the ability to also communicate the success of the model, or, in other words, the probability of the probability.  Disease risk predictions, like weather predictions, become confused with other scientific mathematical models. Even these models are "mathematical hypotheses," albeit ones that have succeeded in representing the pertinent systems accurately through a long history of experiments. But because that historical success comes to support a conflation between the natural reality and the mathematical modeling of that reality, a type of misunderstanding about the nature of scientific models becomes imported into disease risk models.

Revision as of 22:02, 28 August 2011

8/16/2011

Conversation with Ellen:

  1. Take as the overall theme: Care of patients in particular provider-patient relationship (i.e. what we do and how we decide what to do). What does decision-making look like in this setting, with integration of personal knowledge and experience with patients and generalized data? How do introduction of technologies, including WGS and the informatics tools that are used to deliver those results, influence the way we navigate those influences?
  2. This theme is then the basis for addressing the more specific problem of dealing with genetic risk estimates, especially with respect to (1) determining when to tell them to patients and (2) determining which interventions to perform in response to them
  3. Since there is no synthesized model for medical decision-making, and EBM is clearly problematic here (due to no evidence, and due to conceptual problems with applying it in such a setting), a helpful technique will be to analogize from other practices in medicine, perhaps framing these as heuristics for dealing with uncertainty.

8/17/2011

  1. I could do a chapter (or maybe a separate article) on genetic risk, taking my own risk for MI, and explore using different tools (the Mayo risk estimate tool, genetic test, family history alone, cholesterol markers alone, etc) to demonstrate that risk estimates vary, and that the formalization of risk in a specific number over-represents the certainty of such an event happening.
  2. Kuhn's definition of Disciplinary Matrix provides a way to frame what medicine is as a conceptual community as opposed to a practice (exemplars, values, etc), also Polanyi's "tacit knowledge"
  3. Clearly, practical reasoning (Aquinas and MacIntyre) is very important here, as well.

8/28/2011

  1. Misunderstandings about risk derive from more fundamentals blindspots about role of mathematical models and values in science
  2. Scientific theories, framed as mathematical models, are what Berkeley call "mathematical hypotheses." The world does not exist as numbers of mathematical equations; mathematical models are derived from observation and gain support as they successfully represent, and sometimes predict, the way a particular natural event occurs.
  3. As Kuhn observes, paradigm shifts in science occur when paradigms, often based on specific mathematical models, are viewed as failing to respond to problems. This has traditionally assumed to mean that the model fails to predict or represent certain features of the field of study (such as the Ptolemaic models' increasing complexity as it required modifications to fit observed patterns in the solar system).
  4. There are a number of reasons that such a model may fail, however. A model might fail because it predicts a outcomes based on a certain set of variables. When a new variable comes to be recognized as having an effect on such systems, mathematical models that don't account for that variable could be viewed as failing. Possible examples include something like cervical cancer, when HPV came to be appreciated as the significant cause or stomach cancer + H. Pylori. Obesity would be a great example of the way that various models repeatedly fail as factors thought to be important fail to carry explanatory power: first endocrine, then psychological, then much later genetics.
  5. Ice cube melting is an example of a process that can be modeled quite successfully. This is because the important variables are assumedly all now known, and ice cubes can be represented quite effectively in models, since their characteristics are quite homogenous. (this is from Gorowitz and MacIntyre)
  6. However, more complex phenomenon like hurricanes cannot be modeled with complete accuracy. This is not, however, because the important variables are not known. On the contrary, the appreciation of physics related to air flow over warm water is quite well understood. In this way, the general features of hurricanes can be modeled effectively. But the reason that specific hurricanes cannot be modeled successfully is that inputs into such models in the real world are large in number, complex, and difficult to collect through available technologies. So the paths of hurricanes are represented as probability paths.
  7. Human risk for disease is similar. Many of the causes of many disease are well appreciated. The cause of CF has been known since ____. However, the course of health and wellness are extraordinarily complex such that in many cases the variables that are important in the development of a range of disease are not all known, even though some important inputs may be known. In addition, the histories of humans are even more complex than that of earthquakes, and infinitely more so than ice cubes (again, these examples are those of Gorowitz and MacIntyre). The ability to predict the onset of disease in an individual, then, varies on the basis of the comprehensive of available models in terms of the number of variables it can take account of, and also the inputs that are present in the history and ongoing life story of patients.
  8. Risk estimates for individuals emerge within this context of incomplete knowledge of biological systems and incomplete knowledge of particulars. This fact is demonstrated by the availability of co-existing models to predict disease. For example, MI risk estimate tools. The online Mayo Tool says one thing, 23inMe says another, etc. Each of these models is incomplete in the number of variables it is able to account for, and even the inputs it takes into account are snapshots, they can't account for the history of individuals, such as changes in cholesterol over time.
  9. A significant problem with risk estimates, then, is that they represent predictions for the future in mathematical terms in a way that explicitly communicates a probability (Your risk for having an MI is 20%), but lacks the ability to also communicate the success of the model, or, in other words, the probability of the probability. Disease risk predictions, like weather predictions, become confused with other scientific mathematical models. Even these models are "mathematical hypotheses," albeit ones that have succeeded in representing the pertinent systems accurately through a long history of experiments. But because that historical success comes to support a conflation between the natural reality and the mathematical modeling of that reality, a type of misunderstanding about the nature of scientific models becomes imported into disease risk models.