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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://compass.onlinelibrary.wiley.com/doi/10.1111/lnc3.12529"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1408.6520"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1401.3848"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1409.1053"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1401.3892"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.5847"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1311.1496"/>
	<rdf:li rdf:resource="http://dana.org/news/cerebrum/detail.aspx?id=32066"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.5086"/>
	<rdf:li rdf:resource="http://www.lawyersgunsmoneyblog.com/2010/03/ask-your-doctor-if-moral-hazard-is-right-for-you"/>
	<rdf:li rdf:resource="http://www.headandneckoncology.org/content/1/1/34"/>
	<rdf:li rdf:resource="http://nursingassistantguides.com/2009/50-successful-open-source-projects-that-are-changing-medicine/"/>
	<rdf:li rdf:resource="http://www.codinghorror.com/blog/archives/001227.html"/>
	<rdf:li rdf:resource="http://www.worldchanging.com/archives/007597.html"/>
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  </channel><item rdf:about="https://compass.onlinelibrary.wiley.com/doi/10.1111/lnc3.12529">
    <title>Phonetic cues to depression: A sociolinguistic perspective - Hall‐Lew - 2024 - Language and Linguistics Compass - Wiley Online Library</title>
    <dc:date>2024-07-21T15:10:08+00:00</dc:date>
    <link>https://compass.onlinelibrary.wiley.com/doi/10.1111/lnc3.12529</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Phonetic data are used in several ways outside of the core field of phonetics. This paper offers the perspective of one such field, sociophonetics, towards another, the study of acoustic cues to clinical depression. While sociophonetics is interested in how, when, and why phonetic variables cue information about the world, the study of acoustic cues to depression is focused on how phonetic variables can be used by medical professionals as tools to diagnosis. The latter is only interested in identifying phonetic cues to depression, while the former is interested in how phonetic variation cues anything at all. While the two fields fundamentally differ with respect to ontology, epistemology, and methodology, I argue that there are, nonetheless, possible avenues for future engagement, collaboration, and investigation. Ultimately, both fields need to engage with Crip Linguistics for any successful intervention on the relationship between speech and depression.

]]></description>
<dc:subject>linguistics mental-health depression diagnosis pattern-discovery to-understand consider:filter-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a99351332978/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mental-health"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:depression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pattern-discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:filter-discovery"/>
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<item rdf:about="http://arxiv.org/abs/1408.6520">
    <title>[1408.6520] Knowledge Engineering for Planning-Based Hypothesis Generation</title>
    <dc:date>2014-12-27T13:45:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1408.6520</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we address the knowledge engineering problems for hypothesis generation motivated by applications that require timely exploration of hypotheses under unreliable observations. We looked at two applications: malware detection and intensive care delivery. In intensive care, the goal is to generate plausible hypotheses about the condition of the patient from clinical observations and further refine these hypotheses to create a recovery plan for the patient. Similarly, preventing malware spread within a corporate network involves generating hypotheses from network traffic data and selecting preventive actions. To this end, building on the already established characterization and use of AI planning for similar problems, we propose use of planning for the hypothesis generation problem. However, to deal with uncertainty, incomplete model description and unreliable observations, we need to use a planner capable of generating multiple high-quality plans. To capture the model description we propose a language called LTS++ and a web-based tool that enables the specification of the LTS++ model and a set of observations. We also proposed a 9-step process that helps provide guidance to the domain expert in specifying the LTS++ model. The hypotheses are then generated by running a planner on the translated LTS++ model and the provided trace. The hypotheses can be visualized and shown to the analyst or can be further investigated automatically.
]]></description>
<dc:subject>planning artificial-intelligence diagnosis rather-interesting representation the-other-paradigm modeling metamodeling nudge-targets consider:bridge-building</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:267a47255048/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-other-paradigm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metamodeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:bridge-building"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.3848">
    <title>[1401.3848] Approximate Model-Based Diagnosis Using Greedy Stochastic Search</title>
    <dc:date>2014-10-16T12:19:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.3848</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose a StochAstic Fault diagnosis AlgoRIthm, called SAFARI, which trades off guarantees of computing minimal diagnoses for computational efficiency. We empirically demonstrate, using the 74XXX and ISCAS-85 suites of benchmark combinatorial circuits, that SAFARI achieves several orders-of-magnitude speedup over two well-known deterministic algorithms, CDA* and HA*, for multiple-fault diagnoses; further, SAFARI can compute a range of multiple-fault diagnoses that CDA* and HA* cannot. We also prove that SAFARI is optimal for a range of propositional fault models, such as the widely-used weak-fault models (models with ignorance of abnormal behavior). We discuss the optimality of SAFARI in a class of strong-fault circuit models with stuck-at failure modes. By modeling the algorithm itself as a Markov chain, we provide exact bounds on the minimality of the diagnosis computed. SAFARI also displays strong anytime behavior, and will return a diagnosis after any non-trivial inference time.
]]></description>
<dc:subject>artificial-intelligence hillclimbing? models-and-modes diagnosis nudge-targets rule-extraction machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:056c3153b89e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hillclimbing?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rule-extraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1409.1053">
    <title>[1409.1053] Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms</title>
    <dc:date>2014-09-07T11:01:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1409.1053</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of applying a multiple classifier system. In this paper we investigate tuning a side effect multiple classifying system using genetic algorithms. The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier. Furthermore, the framework is able to detect side effects efficiently and obtains a low false positive rate.
]]></description>
<dc:subject>pharmaceutical diagnosis classification discovery algorithms classifier-systems nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:43d797c2d23c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pharmaceutical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classifier-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1401.3892">
    <title>[1401.3892] Sequential Diagnosis by Abstraction</title>
    <dc:date>2014-02-26T11:20:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.3892</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[When a system behaves abnormally, sequential diagnosis takes a sequence of measurements of the system until the faults causing the abnormality are identified, and the goal is to reduce the diagnostic cost, defined here as the number of measurements. To propose measurement points, previous work employs a heuristic based on reducing the entropy over a computed set of diagnoses. This approach generally has good performance in terms of diagnostic cost, but can fail to diagnose large systems when the set of diagnoses is too large. Focusing on a smaller set of probable diagnoses scales the approach but generally leads to increased average diagnostic costs. In this paper, we propose a new diagnostic framework employing four new techniques, which scales to much larger systems with good performance in terms of diagnostic cost. First, we propose a new heuristic for measurement point selection that can be computed efficiently, without requiring the set of diagnoses, once the system is modeled as a Bayesian network and compiled into a logical form known as d-DNNF. Second, we extend hierarchical diagnosis, a technique based on system abstraction from our previous work, to handle probabilities so that it can be applied to sequential diagnosis to allow larger systems to be diagnosed. Third, for the largest systems where even hierarchical diagnosis fails, we propose a novel method that converts the system into one that has a smaller abstraction and whose diagnoses form a superset of those of the original system; the new system can then be diagnosed and the result mapped back to the original system. Finally, we propose a novel cost estimation function which can be used to choose an abstraction of the system that is more likely to provide optimal average cost. Experiments with ISCAS-85 benchmark circuits indicate that our approach scales to all circuits in the suite except one that has a flat structure not susceptible to useful abstraction.
]]></description>
<dc:subject>diagnosis machine-learning algorithms nudge-targets the-mangle-in-practice philosophy-of-engineering fault-analysis abstraction interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:62c55cf66124/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fault-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:abstraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1312.5847">
    <title>[1312.5847] Deep learning for neuroimaging: a validation study</title>
    <dc:date>2014-01-25T11:42:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.5847</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Deep learning methods have recently enjoyed a number of successes in the tasks of classification and representation learning. These tasks are very important for brain imaging and neuroscience discovery, making the methods attractive candidates for porting to a neuroimager's toolbox. Successes are, in part, explained by a great flexibility of deep learning models. This flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
]]></description>
<dc:subject>deep-learning neural-networks tomography classification algorithms nudge-targets representation diagnosis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d26789341d9d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tomography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.1496">
    <title>[1311.1496] Understanding Health and Disease with Multidimensional Single-Cell Methods</title>
    <dc:date>2013-12-19T13:15:30+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.1496</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.
]]></description>
<dc:subject>medical-technology cell-sorting diagnosis algorithms machine-learning nudge-targets interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:29c12cc9b676/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-sorting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dana.org/news/cerebrum/detail.aspx?id=32066">
    <title>Diagnosing the DSM - Dana Foundation</title>
    <dc:date>2011-05-15T13:08:36+00:00</dc:date>
    <link>http://dana.org/news/cerebrum/detail.aspx?id=32066</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[With respect to the DSM-5, I am agnostic about the diagnostic criteria for individual conditions, such as panic disorder or generalized anxiety disorder; in the end, I am not certain that either of these categories capture nature or will even appear in the DSM-6. When it comes to individual diagnostic categories, I would recommend that the DSM-5 take a conservative approach, leaving criteria unchanged unless compelling new evidence suggests that a change would be beneficial. Whatever the ultimate approach to the DSM-5, it is critical that the scientific community escape the artificial diagnostic silos that control so much research, ultimately to our detriment.

]]></description>
<dc:subject>medical-culture diagnosis specification over-specification standard-setting-play pragmatism-it-ain't</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a55ba73ff8bf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:specification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:over-specification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:standard-setting-play"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism-it-ain't"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1005.5086">
    <title>[1005.5086] Classification of interstitial lung disease patterns with topological texture features</title>
    <dc:date>2010-05-31T11:32:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.5086</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["… The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods."
]]></description>
<dc:subject>image-processing medical-technology diagnosis nudge-targets classification machine-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:dfb7a545042b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.lawyersgunsmoneyblog.com/2010/03/ask-your-doctor-if-moral-hazard-is-right-for-you">
    <title>Ask your doctor if moral hazard is right for you : Lawyers, Guns &amp; Money</title>
    <dc:date>2010-03-12T15:22:12+00:00</dc:date>
    <link>http://www.lawyersgunsmoneyblog.com/2010/03/ask-your-doctor-if-moral-hazard-is-right-for-you</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["About ten years ago I attended an event hosted by a couple of medical academics. It was a concert at a pretty big auditorium in Denver, and the invitees were almost all participants in the academics’ prostate cancer research trials (I was there for other reasons). This was before I had begun to study the pharmaceutical industry’s role in the obesity panic, and I remember thinking at the time, who is paying for all this? (The event was on a scale that must have cost well into six figures). That’s not a question I would ask today."
]]></description>
<dc:subject>medicine public-policy clinical-trials diagnosis monopoly-and-trust-sittin'-in-a-tree publish-doctors'-morbidity-stats</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c4501c9856eb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clinical-trials"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:monopoly-and-trust-sittin'-in-a-tree"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publish-doctors'-morbidity-stats"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.headandneckoncology.org/content/1/1/34">
    <title>Head &amp; Neck Oncology | Full text | Potential for Raman spectroscopy to provide cancer screening using a peripheral blood sample</title>
    <dc:date>2009-12-16T21:40:22+00:00</dc:date>
    <link>http://www.headandneckoncology.org/content/1/1/34</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The mean spectra were provided as input sequences to the Implicit Context Representation Cartesian Genetic Programming algorithm (IRCGP)[14,15]. IRCGP uses evolutionary computing methodology to learn classifiers that are capable of distinguishing between data classes. Induced classifiers take the form of programmatic expressions applied to particular offsets within the input data sequences. These expressions are composed from a set of simple mathematical functions. Both the choice and connectivity of the functions, and the choice of offsets used within the input sequences, are determined by the algorithm's evolutionary process. The input sequences were divided equally into training and test sets. To prevent over-learning, training of the classifiers was stopped once classification accuracy of the test sequences started to fall."
]]></description>
<dc:subject>genetic-programming clinical diagnosis nudge spectroscopy applied-mathematics machine-learning classification</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3bcce8d9b5db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clinical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:spectroscopy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:applied-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nursingassistantguides.com/2009/50-successful-open-source-projects-that-are-changing-medicine/">
    <title>50 Successful Open Source Projects That Are Changing Medicine</title>
    <dc:date>2009-02-24T23:55:16+00:00</dc:date>
    <link>http://nursingassistantguides.com/2009/50-successful-open-source-projects-that-are-changing-medicine/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Open source healthcare is forging forward quickly on the Internet. But, fast developments often produce many failures. But, many medicinal open source projects that have gained success development. This success shows that open source alone is not the solitary factor in development. Instead, look to great management, public relations, marketing and a sound program that stands up under the scrutiny of a growing number of peer users and, often, patients."
]]></description>
<dc:subject>collaboration medicine diagnosis healthcare software open-source</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cfc92ec06650/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:healthcare"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-source"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.codinghorror.com/blog/archives/001227.html">
    <title>Coding Horror: The Bad Apple: Group Poison</title>
    <dc:date>2009-02-21T12:05:43+00:00</dc:date>
    <link>http://www.codinghorror.com/blog/archives/001227.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The Depressive Pessimist will complain that the task that they're doing isn't enjoyable, and make statements doubting the group's ability to succeed.

The Jerk will say that other people's ideas are not adequate, but will offer no alternatives himself. He'll say "you guys need to listen to the expert: me."

The Slacker will say "whatever", and "I really don't care."
]]></description>
<dc:subject>via:nielsen group-dynamics management TEAM: inagility project-management diagnosis</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:babc6fed2edd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:nielsen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:group-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:TEAM:"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inagility"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:project-management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.worldchanging.com/archives/007597.html">
    <title>WorldChanging: Tools, Models and Ideas for Building a Bright Green Future: Empowering Patients With Information Technology</title>
    <dc:date>2007-11-24T21:38:13+00:00</dc:date>
    <link>http://www.worldchanging.com/archives/007597.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>medicine collaboration disintermediation p2p community communication diagnosis healthcare</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:48df856c7405/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:disintermediation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:p2p"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:community"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:communication"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnosis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:healthcare"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>