IBIS meets medical research

My thesis research calls for collecting IBIS documents to study and, perhaps, to merge. I’ve been collecting IBIS conversations about climate change, one from Debategraph, two provided by MIT, and one I created by harvesting from a webpage, described here. I could, of course, use more such documents. But, I have an opportunity to begin exploring personal medical situations using the same hypermedia discourse platforms. That’s what I did.

I created an IBIS document using Compendium that essentially asks this question: can Coley’s Toxins be used to combat thyroid nodules? I put the constantly evolving document online here. Let me explain it.

As an IBIS conversation, it is a tree rooted in some form of a context. Sometimes, the research question is the context. Sometimes, a background statement is that context, as is this case.  As a topic mapper, I chose to create one branch of the tree called Topics, in which I am recording all the nouns that come up in my research.  It’s an experiment. Things will change. For now, the nouns are organized in a “cheap taxonomy”, one that will certainly change over time.  Other branches sketch the research methodology, the question, and then two domains of interest: the visitation and therapeutics.

It’s pretty easy to use Wikipedia to find out what Coley’s Toxins (adjuvants) are; in brief, they were discovered back in the late 1800s as a way to deal with cancerous tumors.  They are, essentially, bacteria that, when injected directly into the lesion, provoke a massive immune response that takes out the tumor. Unfortunately, until they learned how to inject killed bacteria, the patient did lose the tumor, but died from the bacterial infection as well.  Over time, even as recently as 1990, Coley’s Toxins were still being investigated.

The point of this work, aside from a personal investigation into matters that matter, is to continue the evolution of ways in which patients can conduct research into matters that matter to them. In the long run, if that research is conducted in online social settings, more people are engaged, more people contribute–think, crowd sourcing personal medical research–and the opportunities for synergies abound. When the setting is part of a knowledge garden where stakeholders of other kinds are also engaged, no telling how far we can push the envelope of reducing health care costs while improving outcomes.

The single largest improvement to outcomes, I strongly believe, occur when patients take control of their situation, which, end-to-end, means being part of the research team that finds answers to complex issues that result from the visitation with which they deal.

IBIS meets MediaWiki

Some slides are now online at slideshare which are drawn from training materials for the Bloomer project which is a component in the collective intelligence platform being installed in some Millennium Project nodes. The IBIS MediaWiki extension can be added to any MediaWiki installation (though it’s not tested on the latest MediaWiki build); it should be possible for a good PHP developer to adapt its code to other platforms such as Drupal.

The extension presently is configured to maintain an index of conversations. Each conversation starts as a Wiki topic, and each question, answer, or argument (see below) is also an individual Wiki topic.

IBIS stands for Issue-based Information Systems, and it’s a target in my thesis research. IBIS conversations are structured, meaning each question, answer, or argument occupies its own node which is linked through a coherence-relation to another node. Some references are found at the Compendium website.

A lone question or idea can start a conversation; answers or questions respond to questions. Answers respond to other answers to expand on them. Pro or con arguments follow answers. As a conversational tool, online structured conversation platforms are part of the argument web. They are also highly appropriate to #CCK11 connectivist thought.

Examples of structured conversation platforms include Compendium, Cohere, DebategraphTruthMapping, Climate Collaboratorium, and Argument Mapping and an emerging list of others. It should be noted that Jane McGonigal has introduced IBIS as playing cards in her online games, including the MRF Game I mentioned here, and these.

MRF Game Results Posted

The Myelin Repair Foundation game on which I reported here and here is now discussed at the Robert Wood Johnson website. The 30 page pdf is found here. The report opens with this:

On October 7–8, and November 9–10, 2010, Institute for the Future (IFTF), in cooperation with the Myelin Repair Foundation and the Robert Wood Johnson  Foundation, hosted a Foresight Engine thought experiment called Breakthroughs to Cures.  Designed as an open, non-partisan environment where models for innovation in medical research can be freely explored and developed, the purpose was to generate  “outlier” ideas and strategies that could lead to more effective and efficient ways to fund  and conduct medical research with the goal of speeding up the development of patient  treatments and cures.

Played as a “card game” where each card resembles a node in an Issue-based information systems (IBIS) conversation as seen in, for example, Compendium which I illustrated from my own MRF game moves here, or at Debategraph, the game provided wide opportunity for journalistic discovery and reporting. The report says this:

In sum, what game play pointed to was a variety of opportunities—particularly in terms of technological infrastructure and in terms of the types of relationships that could be built to bring new ideas to basic science research and to make better use of current resources. Many of these ideas point toward long-term opportunities to facilitate connection and accelerate, and in this sense, provide the outlines for actions to take over time to accelerate medical research.

I believe that an important contribution provided by the MRF game report as produced by IFTF members is its illustration of how a crowd-sourced research project could produce results that journalists could then synthesize into a report worthy of any sensemaking project which leads to decision making.

Where could the MRF games go from here?  I believe the answer to that question lies in the hands of those who created, conducted, and funded that project. What value can those of us who research and practice the art and science of sensemaking through hypermedia discourse gain from the MRF game? The answer to that lies precisely in what we do with not only the report linked above, but also what we do as we study the game boards ourselves seeking to better understand the craft exhibited.

CCK11 Thinking about connectivism

I am sitting in on a MOOC (massive online open course–seriously cool!) about connectivism.  The term itself caught my eye, though I had to spend some time disambiguating it with “connectionist” (computational neural net) stuff. I now see it as extending learning theories that spanned behaviorist to constructivism, now connectivism. See  this document for the chart, and this page for the first week’s activities.

Actually, I’m fighting a battle with cognitive dissonance here. I’m wrestling with the notion that the chart linked above appears to be trying to answer the wrong question.  In my view, the issue is not about how connectivism is different from the other learning theories; rather, the question should be closer to how connectivism builds on earlier theories, since I believe that’s what it is trying to do.

My view is built upon notions of relational biology due to Rashevsky and Rosen.  In “Topology and life”*, Rashevsky, one of the founders of mathematical biology, realized that we can tease apart a living organism and count the components (which has the nasty side effect of killing it), but we cannot put it back together. Something in our knowledge of living (complex) things is missing. He set out to find out what that something is. Rosen came along and later and offered a mathematical model called the Metabolism-Repair System which serves as a candidate answer.

A central tenet in the Rashevsky-Rosen concept is that the action lies in the connections.

Rosen authored the book Anticipatory Systems (soon to be reprinted) which essentially (my view) offers the underlying basis for all learning theories: living entities are anticipatory by nature. From the lowest single cell creature to the largest living creatures on Earth, anticipatory behavior is in play, whether it’s simple chemical reactions to sunlight bending a plant to face the sun, or to complex neurological processes from neural firings to brains at work.  That model fits all the learning processes, be they conditioned-response or people in networks.

Ok, I just offered a highly reductionist explanation to a massively complex set of processes. I don’t see that as any different from someone saying that process X is better than process Y. At some point, as Rosen was fond of saying in his books, you have to keep asking “why”.  I believe that anticipation serves as a foundation on which “why” questions can best be understood and, perhaps, debated. My own answer to cognitive dissonance is to ignore the “my process is better than” arguments and spend time seeing how they each work together.

* Topology and life: In search of general mathematical principles in biology and sociology. Bulletin of Mathematical Biophysics 16 (1954): 317–348

A first look at an MRF game move

In my previous post on online games that matter, I described the Myelin Repair Foundation’s research game. That game was mounted in concert with Justine Lam and the Institute for the Future, and was funded by the Pioneer Fund of the Robert Wood Johnson Foundation.  I look forward to continued explorations of online games that matter. Meanwhile,  as part of my thesis research, I began to analyze game moves, starting with one of my own.

In that game, players create game moves by filling in cards, each of a specific type: a question, or several types of answers. The result is a tree structure, not unlike those created with Compendium.  I therefore lifted one of my game moves together with the entire subtree it anchors and copied that into Compendium. The tree’s image is online here (click on it to expand its size), and the report is here.  It’s worth noting that the tree I crafted represents my interpretation of the game moves; it is entirely reasonable to expect there to be other interpretations, as well as errors in my own.

One goal of this analysis is to begin the process of discovering and evolving a set of best practices associated with structured conversation, be it in games or otherwise.  From the nature of each contributed card (node), I look for evidence of issues related the contribution, and seek ways to improve the process.

The simplest observation is that multiple topics made in any given card make it difficult to establish a coherent subtree of responses to that node. Here is a trivial example, not taken from the game:

  • Q: What are the causes of climate change?
    • A: Upper atmosphere carbon dioxide and refrigerator magnets

One should not dwell on the apparent humour in that answer, since there are skilled people who could turn that into a really thoughtful conversational arc around the energetics of making refrigerator magnets and entailed effects on climate. Our interest lies in a suspicion that the subtree that grows from that answer will use a lot of coherence factors to separate out the two topics, then deal with each, separately.

A preference, cast in the light of conversation federation, is to seek simple answers, and use lots of tree (child) nodes to expand on those answers such that each expansion, itself, is an addressable assertion that can, where appropriate, serve as a root for a new subtree.

I think there is room for a large (global) conversation that orbits a well-posed core question that seeks best practices in hypermedia discourse.

Online games that matter

The Institute for the Future (IFTF) teamed with the Myelin Repair Foundation, funded by the Robert Wood Johnson Foundation to host a game with this title:

How would you advise the President to reinvent the process of medical discovery?

The game was played here, ending today. Following the game, as part of my thesis project, I wrote a quick summary report, found here.  At the same time, I created a new hashtag at Twitter: #ogtm for online games that matter.

My overall impression from playing the game is that online games that matter, with the Foresight Engine being a shiny new example, will play an increasingly important role in social sensemaking and learning.

Towards a research corpus

In my thesis proposal,  I outline an approach to the federation of structured conversations. On the surface, federation means combining representations of topics that are about the same topic. The term, from topic maps, is merging.

The trivial example is seen in these two conversation assertions in answer to the same question:

  • co2 causes climate change
  • climate change is caused by co2

On inspection, humans recognize those two assertions as saying the same thing. Not so for most computer programs; my task is to write a program that notices the sameness of the two assertions. One approach is to transform the assertions into some sort of canonical form and compare those. Many tricks (a term exploited by the climategate crowd) are available. One is to notice that causes and is caused by relate to the same notion of causality, a root relation. A transform based on that results in these two triples:

  • {co2, cause, climate change}
  • {carbon dioxide, cause, climate change}

The next trick is to notice that co2 and carbon dioxide are both names for the same topic. We thus reduce both assertions to one triple; both say the same thing. We can merge the two statements into one.

To do that on a large scale, we need a corpus of conversations for training and testing.  Our mission was thus one of harvesting numerous such conversations from the web. We could use search engines and find various blog entries, Wikipedia entries, op eds, and so forth; we will eventually do lots of that. But, good fortune bestowed the gift of 126 climate change arguments into our laptop and the corpus described in the last post appeared. To get that corpus into shape requires further processing.

Further processing happens in the form of an online web service, AlchemyAPI, one among several we are testing. One signs up for an account, downloads some software utilities, writes a program to use those utilities and begins to harvest each of the pages linked in our 126-argument issue map from our last post. Those utilities harvest the page and return several XML files. One returns clean text ready for further processing. One returns named entities discovered in the text, and others return key terms and concepts. We are well on our way to a corpus sufficient to conduct this research.

Skeptic Arguments and What the Science Says

Today, I uploaded an issue map to the Compendium maps section of the Open University’s moodle website. The issue map was created with the kind permission of John Cook, owner of skepticalscience.com, which provided the conversation material necessary.

My thesis research mission includes the collection of a text corpus representing arguments on both sides of some issue. When I mapped the site, there were 126 arguments; there are now 127. Along the way, I decided that two of the arguments were essentially saying the same thing, so I merged them in my map, which is what my thesis research is about: federating conversations.

In a subsequent post, I shall describe what is expected of that text corpus. For purposes of my initial research, the text collected from this one website and sites linked to it should provide a sufficient starting text corpus.

Data-intensive Scientific Discovery

It seems worth mentioning a book, The Fourth Paradigm: Data-intensive Scientific Discovery, found here.

The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies.

Most notably, the book can be purchased, and it can be downloaded in two PDF formats for free.

I suspect that events of history such as climategate and others lend force to ideas such as citizen science, multiple opinions, and transparency. My sense is that, given massive improvements in compute power, parallel processing, seti@home-like computing, we will see more opportunities for eScience to “take to the streets”.