Showing papers in "Artificial Intelligence in Medicine in 2010"

TL;DR: The results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets.

TL;DR: The method based on machine learning techniques were the most suited for the imputation of missing values and led to a significant enhancement of prognosis accuracy compared to imputation methods based on statistical procedures.

TL;DR: The CDW platform would be a promising infrastructure to make full use of the TCM clinical data for scientific hypothesis generation, and promote the development of TCM from individualized empirical knowledge to large-scale evidence-based medicine.

TL;DR: It is shown that by combining classifier ensemble algorithms in this two-step manner, it is possible to predict the malignancy for solitary pulmonary nodules with a performance exceeding that of either of the individual steps.

TL;DR: A hybridized tabu search algorithm is developed that intelligently assists the admission scheduler in taking decisions fast and presents feasible solutions even when disrupted by the user at an early stage in the calculation.

TL;DR: It is concluded that intelligent visualization and exploration of longitudinal data of multiple patients with the VISITORS system is feasible, functional, and usable.

TL;DR: It is shown that eClass can effectively be applied to the classification of diabetes and dermatological diseases from discrete numerical samples and the results of using a novel optimization strategy indicate that the accuracy of eClass models can be further improved.

TL;DR: The late fusion scheme showed high robustness to the number of clinical parameters used, which suggests that it is appropriate for mining clinical attributes with missing values in clinical routine.

TL;DR: The four stage approach illustrated in this paper is useful for designing and implementing an ontology as the basis for a HIS and extends existing ontology development methodologies by providing an empirical basis for theory incorporated into ontology design.

TL;DR: This study demonstrated that the integrated machine learning method to select the predictor variables is more effective in developing the Cox survival models than the traditional methods commonly found in the literature.

TL;DR: The automatic verification of properties in the model checking approach is able to discover inconsistencies in the GL that cannot be detected in advance by hand, and represents a further step in the direction of general and flexible automated GL verification, which also meets usability requirements.

TL;DR: This work describes semantic relation (SR) classification on medical discharge summaries and presents an SR classifier that studies a corpus of patient records one sentence at a time, promising for semantic indexing of medical records.

TL;DR: A fully automatic, gray-level based segmentation framework based on a multiplanar fast marching method that can be adapted to segment different abdominal organs, achieving promising segmentation results.

TL;DR: The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system.

TL;DR: The obtained results indicate that an objective analysis of dysfunctional vocal fold vibration can be achieved with considerably high accuracy, and the PVG feature extraction and classification approach can be assessed as being promising with regard to the diagnosis of functional voice disorders.

TL;DR: A novel system for computer-aided detection of clusters of microcalcifications on digital mammograms that exhibits some remarkable advantages both in segmentation and classification phases is described.

TL;DR: Fuzzy Arden Syntax offers the possibility to formulate conveniently Medical Logic Modules (MLMs) based on the principle of a continuously graded applicability of statements, and ad hoc decisions about sharp value boundaries can be avoided.

TL;DR: Experimental results on the small round blue-cell tumor data set, compared with other widely used clustering algorithms, show that the proposed method can effectively identify different cancer types and generate high-quality cancer sample clusters.

TL;DR: A new feature subset selection method, FS-MLP, that selects relevant features using multi-layered perceptron (MLP) learning that is effective in analyzing multi-variate, non-linear and high dimensional datasets such as HIV-1 protease cleavage dataset.

TL;DR: A new multiple regression model based on the scale-free property of real biological network for genetic regulatory network inference is proposed that can be widely used for genetic network inference using high-throughput gene expression data from various species for systems biology discovery.

TL;DR: A novel mixture classification model to make full use of the invaluable information in clinical data, which is similar to the traditional ensemble classification models except for putting strict constraints on the construction of mapping functions to avoid voting process is presented.

TL;DR: A semi-supervised graph-based method for tumor classification that can effectively improve the performance of tumor classification based on gene expression profiles is introduced.

TL;DR: The late fusion scheme showed high robustness to the number of clinical parameters used, which suggests that it is appropriate for mining clinical attributes with missing values in clinical routine.

TL;DR: A new approach is demonstrated that dynamically estimates and determines the optimal sequence of tests that provides the most information (or disease probability) based on a patient's available information.

TL;DR: The proposed approach can serve as a valuable decision support tool for the medical practitioner facing the complex problem of designing efficient combined chemotherapies and confirm in silico that the combination of a cytostatic with a cytotoxic agent improves the efficacy and efficiency of the chemotherapy.

TL;DR: It is shown that machine learning techniques attain a predictive performance that is as good as, or better than, that of linear and logistic regression, for target attributes that include tumor N and T stage, survival time, and ECOG quality of life scores.

TL;DR: There is recently a growing interest in the application of AI techniques in biomedical engineering and informatics, ranging from knowledge-based reasoning for disease classification to learning and discovering novel biomedical knowledge for disease treatment.

TL;DR: An improved method for pre-microRNA prediction using a combination of various features and a web server called PMirP is developed, which improves the prediction efficiency and accuracy over existing methods.

TL;DR: The exploration of developing an automated system to identify MEDLINE abstracts referring to host-pathogen protein-protein interactions and the effects of feature selection using the information gain (IG) measure are reported on.

TL;DR: The experiments show that the classification integration method is more accurate than current data integration methods when there are many missing values in the data.