Matchings and Decision Trees for Determining Optimal Therapy
This volume contains proceedings of the first Workshop on Data Analysis in Medicine held in May 2017 at the National Research University Higher School of Economics, Moscow. The volume contains one invited paper by Dr. Svetla Boytcheva, 6 regular contributions and 2 project proposals, carefully selected and reviewed by at least two reviewers from the international program commit- tee. The papers accepted for publication report on different aspects of analysis of medical data, among them treatment of data on particular diseases (Consoli- dated mathematical growth model of Breast Cancer CoMBreC, Artificial neural networks for prediction of final height in children with growth hormone deficiency), methods of data analysis (analysis of rare diseases, methods of machine learning and Big Data, subgroup discovery for treatment optimization), and instrumental tools (explanation-oriented methods of data analysis in medicine, information support features of the medical research process, modeling frame- work for medical data semantic transformations, radiology quality management and peer-review system). Organizers of the workshop would like to thank the reviewers for their careful work and all contributors and participants of the workshop.
MICCAI 2016, the 19th International Conference on Medical Image Computing and Computer Assisted Intervention, will be held from October 17th to 21st, 2016 in Athens, Greece. MICCAI 2016 is organized in collaboration with Bogazici, Sabanci, and Istanbul Technical Universities.
The annual MICCAI conference attracts world leading biomedical scientists, engineers, and clinicians from a wide range of disciplines associated with medical imaging and computer assisted intervention.
The conference series includes three days of oral presentations and poster sessions. MICCAI 2016 will also include workshops, tutorials, and challenges on the days preceding and succeeding the conference. These satellite events will offer a comprehensive forum to further explore topics relevant to MICCAI.
In this paper, we tackle a problem of predicting phenotypes from structural connectomes. We propose that normalized Laplacian spectra can capture structural properties of brain networks, and hence graph spectral distributions are useful for a task of connectome-based classication. We introduce a kernel that is based on earth mover's distance (EMD) between spectral distributions of brain networks. We access performance of an SVM classier with the proposed kernel for a task of classication of autism spectrum disorder versus typical development based on a publicly available dataset. Classication quality (area under the ROC-curve) obtained with the EMD-based kernel on spectral distributions is 0.71, which is higher than that based on simpler graph embedding methods.
An outline of a few methods in an emerging field of data analysis, “data interpretation”, is given as pertaining to medical informatics and being parts of a general interpretation issue. Specifically, the following subjects are covered: measuring correlation between categories, conceptual clustering, and generalization and interpretation of empirically derived concepts in taxonomies. It will be shown that all of these can be put as parts of the same inquiry.
In this paper, we summarize the results of recent studies on the application of pattern mining and machine learning to the analysis of demographic sequences. The main goal is the demonstration of demographers’ needs, including next-event prediction and the extraction of interesting patterns from substantial datasets of demographic data, which cannot be handled by conventional demographic techniques. We use decision trees as a technique for demographic event prediction, and emerging sequential patterns and pattern structures for discovering relevant interpretable sequences. The emerging problem statements and positive prospects of the usage of pattern mining in the demography domain are worth dissemination in the data mining community.
An automated real-time classification of human functional states is an important problem for stress resistance evaluation, supervision over operators of critical infrastructure, automated teaching and phobia therapy. In this paper we propose a novel method for binary classification of functional states based on the integrated analysis of (peripheral) physiological parameters: galvanic skin response, respiratory rate, electrocardiographic data, body temperature, electromyographic data, photoplethysmographic data, muscle contraction. The method is based on Gradient Boosted Trees algorithm. A testing of the method showed that in case of stress vs. calm wakefulness differentiation a reliability of the method exceeds 80%.
We study dierences in structural connectomes between typically developing and autism spectrum disorders individuals with machine learning techniques using connection weights and network metrics as features. We build linear SVM classier with accuracy score 0:64 and report 16 features (seven connection weights and nine network node centralities) best distinguishing these two groups.
Methods of classification by nature of decision-making divide on methods using global optimization (all training samples are used), and local optimization (only samples in the neighbourhood of the studied object are used). The perspective direction of research is combination of advantages of each approach in one integrated classifier. In article the method of combination of these approaches by embedding of local metric features into the approach using global optimization is proposed. This approach is shown for a case when the classifier using global optimization is random forest and extra random trees. Various variants of metric features are evaluated. Performance of the proposed approach is illustrated on the forest cover type prediction task, where it leads to significant improvement in classification accuracy.