Artificial Intelligence in Medicine
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A knowledge-based clinical toxicology consultant for diagnosing multiple exposures.
We seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances. The system is automatically trained using data mining techniques to extract prior probabilities and likelihood ratios from a...
Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging.
This paper explores the importance of the latent symmetry of the brain in computer-aided systems for diagnosing Alzheimer's disease (AD). Symmetry and asymmetry are studied from two points of view: (i) the development of an effective classifier within the scope of machine learning techniques, and (i...
Case-based reasoning emulation of persons for wheelchair navigation.
We established the generality of our approach taking a new path not present in the training traces; (iii) the emulated user is more realistic - path and efficiency are less homogeneous and smooth - than potential field approaches; and (iv) the system adequately emulates in-patients - maximum and mea...
Identifying malignant transformations in recurrent low grade gliomas using high resolution magic angle spinning spectroscopy.
The objective of this study was to determine whether metabolic parameters derived from ex vivo analysis of tissue samples are predictive of biologic characteristics of recurrent low grade gliomas (LGGs). This was achieved by exploring the use of multivariate pattern recognition methods to generate s...
Resolution of redundant semantic type assignments for organic chemicals in the UMLS.
The Unified Medical Language System (UMLS) integrates terms from different sources into concepts and supplements these with the assignment of one or more high-level semantic types (STs) from its Semantic Network (SN). For a composite organic chemical concept, multiple assignments of organic chemical...
Prediction of intraoperative complexity from preoperative patient data for laparoscopic cholecystectomy.
Different reasons may cause difficult intraoperative surgical situations. This study aims to predict intraoperative complexity by classifying and evaluating preoperative patient data. The basic prediction problem addressed in this paper involves the classification of preoperative data into two class...
Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques.
As described in this paper we tested this method on 410 individuals without performing any personalized calibration. The results were computed after cross validation. The machine learning techniques tested were: ridge linear regression, a multilayer perceptron neural network, support vector machines...
Visual pattern mining in histology image collections using bag of features.
The experimental evidence suggests that the bag-of-features representation is a good alternative to represent visual content in histology images. The proposed method exploits this representation to perform visual pattern mining from a wider perspective where the focus is the image collection as a wh...
A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets
This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance...
An evaluation of heuristics for rule ranking
On a lung cancer dataset, the area under the ROC curve (AUC) of, on average, 14267.1 rules was 0.862. Multi-rule ranking found 13.3 rules with an AUC of 0.852. Four single-rule ranking algorithms, using the same number of rules, achieved average AUC values of 0.830, 0.823, 0.823, and 0.822, respecti...