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Artificial Intelligence in Medicine

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  1. Fundamentals of clinical methodology: 2. Etiology.

    Artificial Intelligence in Medicine 12(3):227 (1998) PMID 9626958

    The concept of etiology is analyzed and the possibilities and limitations of deterministic, probabilistic, and fuzzy etiology are explored. Different kinds of formal structures for the relation of causation are introduced which enable us to explicate the notion of cause on qualitative, comparative,...
  2. Distributed cognition and knowledge-based controlled medical terminologies.

    Artificial Intelligence in Medicine 12(2):153 (1998) PMID 9520222

    Controlled medical terminologies (CMTs) are playing central roles in clinical information systems and medical knowledge resource applications. As these terminologies grow, they are able to support more complex tasks but require more intensive efforts to create and maintain them. Several terminologie...
  3. Dependency parsing for medical language and concept representation.

    Artificial Intelligence in Medicine 12(1):77 (1998) PMID 9475953

    We present a PROLOG-based formalization of dependency grammar that can accommodate conceptual structures in its dependency rules. First results indicate that this formalization provides an operational basis for the implementation of medical language parsers and for the design of medical concept repr...
  4. Supervised machine learning-based classification of oral malodor based on the microbiota in saliva samples.

    Artificial Intelligence in Medicine 60(2):97 (2014) PMID 24439218

    This study presents an effective method of classifying oral malodor from oral microbiota in saliva by using a support vector machine (SVM), an artificial neural network (ANN), and a decision tree. This approach uses concentrations of methyl mercaptan in mouth air as an indicator of oral malodor, and...
  5. Knowledge discovery in clinical decision support systems for pain management: a systematic review.

    Artificial Intelligence in Medicine 60(1):1 (2014) PMID 24370382

    The occurrence of pain accounts for billions of dollars in annual medical expenditures; loss of quality of life and decreased worker productivity contribute to indirect costs. As pain is highly subjective, clinical decision support systems (CDSSs) can be critical for improving the accuracy of pain a...
  6. Computational intelligence for the Balanced Scorecard: Studying performance trends of hemodialysis clinics.

    Artificial Intelligence in Medicine 58(3):165 (2013) PMID 23768423

    We show the potential of the proposed methods through illustrative results derived from the analysis of BSC data of 109 FME clinics in three countries. We were able to identify the performance drivers for specific groups of clinics and to distinguish between countries whose performances are likely t...
  7. Comparative study of approximate entropy and sample entropy robustness to spikes

    Artificial Intelligence in Medicine 53(2):97 (2011)

    Objective There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the...
  8. Incorporating expert knowledge when learning Bayesian network structure: A medical case study

    Artificial Intelligence in Medicine 53(3):181 (2011) PMID 21958683

    We examine a number of approaches to their combination when learning structure and present new techniques for assessing their results....
  9. Patterns for collaborative work in health care teams

    Artificial Intelligence in Medicine 53(3):139 (2011) PMID 21899990

    We address these problems by specifying goal-based patterns for abstracting the delegation and assignment of services. The proposed patterns should provide generic and reusable solutions and be flexible enough to be customizable at run time to the particular context of execution. Most importantly th...
  10. Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples

    Artificial Intelligence in Medicine 45(1):63 (2009) PMID 19185475

    We propose a new method to build scoring functions able to make reliable predictions, though functions whose induction only requires records from a small set of patients described by a few variables....