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Artificial Intelligence in Medicine

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  1. Self-focusing therapeutic gene delivery with intelligent gene vector swarms: Intra-swarm signalling through receptor transgene expression in...

    Artificial Intelligence in Medicine 63(1):1 (2015) PMID 25547266

    I hypothesize that intelligent self-focusing behaviour of swarms of cell-targeted therapeutic gene vectors can be accomplished without the employment of difficult-to-use diffusible chemo-attractants, instead relying on the intra-swarm signalling through cells expressing a non-diffusible extra-cellul...
  2. Cancer survival classification using integrated data sets and intermediate information.

    Artificial Intelligence in Medicine 62(1):23 (2014) PMID 24997860

    We suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. FSCOX provides us with intermediate survival information, w...
  3. Automatic classification of epilepsy types using ontology-based and genetics-based machine learning.

    Artificial Intelligence in Medicine 61(2):79 (2014) PMID 24743020

    We present the results achieved by using two machine learning methods. The first is an ontology-based classification that can directly incorporate human knowledge, while the second is a genetics-based data mining algorithm that learns or extracts the domain knowledge from medical data in implicit fo...
  4. Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis.

    Artificial Intelligence in Medicine 60(1):41 (2014) PMID 24388398

    We present a methodological approach that combines multiresolution analysis and fuzzy systems to represent cases of dengue and severe dengue in Colombia. The performance of this proposal was compared with that obtained by applying traditional fuzzy modeling techniques on the same data set. This comp...
  5. Subpopulation-specific confidence designation for more informative biomedical classification.

    Artificial Intelligence in Medicine 58(3):155 (2013) PMID 23731649 PMCID PMC3727244

    Our approach demonstrates a positive correspondence between the predictivity designations derived from training samples and the classification accuracy of test samples. The average difference between highest- and lowest-confidence accuracies for the six datasets is 17.8%, with a minimum of 11.3% and...
  6. Elicitation of neurological knowledge with argument-based machine learning.

    Artificial Intelligence in Medicine 57(2):133 (2013) PMID 23063772

    We are developing a neurological decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (comorbidity). The system is intended to act as a second opinion for the neurologists, and most importantly to help them reduce th...
  7. Selective voting in convex-hull ensembles improves classification accuracy.

    Artificial Intelligence in Medicine 54(3):171 (2012) PMID 22064044 PMCID PMC3666100

    Classification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routine clinical...
  8. Intelligent dental training simulator with objective skill assessment and feedback.

    Artificial Intelligence in Medicine 52(2):115 (2011) PMID 21641781

    We present a dental training simulator that provides a virtual reality (VR) environment with haptic feedback for dental students to practice dental surgical skills in the context of a crown preparation procedure. The simulator addresses challenges in traditional training such as the subjective natur...
  9. Artificial Intelligence in Medicine AIME 2009.

    Artificial Intelligence in Medicine 52(2):57 (2011) PMID 21645999

  10. Editorial Board
    Author(s) unavailable

    Artificial Intelligence in Medicine 49(3):CO2 (2010)