Artificial Intelligence in Medicine
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Cancer survival classification using integrated data sets and intermediate information.
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...
Automatic classification of epilepsy types using ontology-based and genetics-based machine learning.
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...
Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis.
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...
Subpopulation-specific confidence designation for more informative biomedical classification.
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...
Elicitation of neurological knowledge with argument-based machine learning.
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...
Selective voting in convex-hull ensembles improves classification accuracy.
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...
Intelligent dental training simulator with objective skill assessment and feedback.
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...
Artificial Intelligence in Medicine AIME 2009.
Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions.
We have used a Bayesian network as the primary tool for building a decision-support system. The effects of usage of antibiotics on the colonisation of the respiratory tract by various pathogens and the subsequent antibiotic choices in case of VAP were modelled using the notion of causal independence...