Vladan Radosavljevic

Adaptive Content-Based Image Retrieval with Relevance Feedback

Retrieval of images, based on similarities between feature vectors of querying image and those from database, is considered. The searching procedure was performed through the two basic steps: an objective one, based on the Euclidean distances and a subjective one based on the users relevance feedback. Images recognized from user as the best matched to a query are labeled and used for updating the query feature vector through a RBF (radial basis function) neural network. The searching process is repeated from such subjectively refined feature vectors. In practice, several iterative steps are sufficient, as confirmed by intensive simulations

Extraction of Interpretable Multivariate Patterns for Early Diagnostics

Leveraging temporal observations to predict a patients health state at a future period is a very challenging task. Providing such a prediction early and accurately allows for designing a more successful treatment that starts before a disease completely develops. Information for this kind of early diagnosis could be extracted by use of temporal data mining methods for handling complex multivariate time series. However, physicians usually prefer to use interpretable models that can be easily explained, rather than relying on more complex black-box approaches. In this study, a temporal data mining method is proposed for extracting interpretable patterns from multivariate time series data, which can be used to assist in providing interpretable early diagnosis. The problem is formulated as an optimization based binary classification task addressed in three steps. First, the time series data is transformed into a binary matrix representation suitable for application of classification methods. Second, a novel convex-concave optimization problem is defined to extract multivariate patterns from the constructed binary matrix. Then, a mixed integer discrete optimization formulation is provided to reduce the dimensionality and extract interpretable multivariate patterns. Finally, those interpretable multivariate patterns are used for early classification in challenging clinical applications. In the conducted experiments on two human viral infection datasets and a larger myocardial infarction dataset, the proposed method was more accurate and provided classifications earlier than three alternative state-of-the-art methods.

A Data-Mining Technique for Aerosol Retrieval Across Multiple Accuracy Measures

A typical approach in supervised learning is to select an accuracy measure and train a predictor that maximizes it. This can be insufficient in remote-sensing applications where predictor performance is often evaluated over multiple domain-specific accuracy measures. Here, we test the hypothesis that predictors can be trained to maximize performance over multiple accuracy measures. To do this, we evaluate several metalearning algorithms on the problem of aerosol optical depth (AOD) retrieval. The multiple accuracy measures included mean squared error, correlation, relative squared error, and fraction of satisfactory predictions. The proposed metalearning algorithms have a two-layer architecture, where the first layer consists of multiple neural networks, each trained using a different accuracy measure, and the second layer aggregates decisions of the first layer predictors. To evaluate AOD predictors, we used nearly 70 000 collocated data points whose attributes were radiances, solar and view angles, and terrain elevation from MODerate resolution Imaging Spectrometer (MODIS) instrument satellite observations and whose target AOD variable was obtained from the ground-based AEROsol robotic NETwork (AERONET) instruments. The data were collected at 221 AERONET locations over the globe in the period between 2005 and 2007. AOD prediction accuracies of neural networks were compared to the recently developed operational MODIS C005 retrieval algorithm and to several other data-mining methods. Results showed that neural networks are better at reproducing the test data than the operational retrieval algorithm and that predictors obtained by metalearning are robust over multiple accuracy measures.

Travel Speed Forecasting by Means of Continuous Conditional Random Fields

This paper explores the application of the recently proposed continuous conditional random fields (CCRF) to travel forecasting. CCRF is a flexible, probabilistic framework that can seamlessly incorporate multiple traffic predictors and exploit spatial and temporal correlations inherently present in traffic data. In addition to improving prediction accuracy, the probabilistic approach provides information about prediction uncertainty. Moreover, information about the relative importance of particular predictor and spatial–temporal correlations can be easily extracted from the model. CCRF is fault-tolerant and can provide predictions even when some observations are missing. Several CCRF models were applied to the problem of travel speed prediction in a range from 10 to 60 min ahead and evaluated on loop detector data from a 5.71-mi section of I-35W in Minneapolis, Minnesota. Several CCRF models, with increasing levels of complexity, are proposed to assess performance of the method better. When these CCRF models were compared with the linear regression model, they reduced the mean absolute error by around 4%. The results imply that modeling spatial and temporal neighborhoods in traffic data and combining various baseline predictors under the CCRF framework can be beneficial.

Neural Gaussian conditional random fields

We propose a Conditional Random Field (CRF) model for structured regression. By constraining the feature functions as quadratic functions of outputs, the model can be conveniently represented in a Gaussian canonical form. We improved the representational power of the resulting Gaussian CRF (GCRF) model by (1) introducing an adaptive feature function that can learn nonlinear relationships between inputs and outputs and (2) allowing the weights of feature functions to be dependent on inputs. Since both the adaptive feature functions and weights can be constructed using feedforward neural networks, we call the resulting model Neural GCRF. The appeal of Neural GCRF is in conceptual simplicity and computational efficiency of learning and inference through use of sparse matrix computations. Experimental evaluation on the remote sensing problem of aerosol estimation from satellite measurements and on the problem of document retrieval showed that Neural GCRF is more accurate than the benchmark predictors.

Gaussian Conditional Random Fields for Aggregation of Operational Aerosol Retrievals

We present a Gaussian conditional random field model for the aggregation of aerosol optical depth (AOD) retrievals from multiple satellite instruments into a joint retrieval. The model provides aggregated retrievals with higher accuracy and coverage than any of the individual instruments while also providing an estimation of retrieval uncertainty. The proposed model finds an optimal temporally smoothed combination of individual retrievals that minimizes the root-mean-squared error of AOD retrieval. We evaluated the model on five years (2006-2010) of satellite data over North America from five instruments (Aqua and Terra MODIS, MISR, SeaWiFS, and the Ozone Monitoring Instrument), collocated with ground-based Aerosol Robotic Network ground-truth AOD readings, clearly showing that the aggregation of different sources leads to improvements in the accuracy and coverage of AOD retrievals.

Large-Scale Discovery of Disease-Disease and Disease-Gene Associations

Data-driven phenotype analyses on Electronic Health Record (EHR) data have recently drawn benefits across many areas of clinical practice, uncovering new links in the medical sciences that can potentially affect the well-being of millions of patients. In this paper, EHR data is used to discover novel relationships between diseases by studying their comorbidities (co-occurrences in patients). A novel embedding model is designed to extract knowledge from disease comorbidities by learning from a large-scale EHR database comprising more than 35 million inpatient cases spanning nearly a decade, revealing significant improvements on disease phenotyping over current computational approaches. In addition, the use of the proposed methodology is extended to discover novel disease-gene associations by including valuable domain knowledge from genome-wide association studies. To evaluate our approach, its effectiveness is compared against a held-out set where, again, it revealed very compelling results. For selected diseases, we further identify candidate gene lists for which disease-gene associations were not studied previously. Thus, our approach provides biomedical researchers with new tools to filter genes of interest, thus, reducing costly lab studies.

Modeling Healthcare Quality via Compact Representations of Electronic Health Records

Increased availability of Electronic Health Record (EHR) data provides unique opportunities for improving the quality of health services. In this study, we couple EHRs with the advanced machine learning tools to predict three important parameters of healthcare quality. More specifically, we describe how to learn low-dimensional vector representations of patient conditions and clinical procedures in an unsupervised manner, and generate feature vectors of hospitalized patients useful for predicting their length of stay, total incurred charges, and mortality rates. In order to learn vector representations, we propose to employ state-of-the-art language models specifically designed for modeling co-occurrence of diseases and applied clinical procedures. The proposed model is trained on a large-scale EHR database comprising more than 35 million hospitalizations in California over a period of nine years. We compared the proposed approach to several alternatives and evaluated their effectiveness by measuring accuracy of regression and classification models used for three predictive tasks considered in this study. Our model outperformed the baseline models on all tasks, indicating a strong potential of the proposed approach for advancing quality of the healthcare system.

Which links should I use?: a variogram-based selection of relationship measures for prediction of node attributes in temporal multigraphs

When faced with the task of forming predictions for nodes in a social network, it can be quite difficult to decide which of the available connections among nodes should be used for the best results. This problem is further exacerbated when temporal information is available, prompting the question of whether this information should be aggregated or not, and if not, which portions of it should be used. With this challenge in mind, we propose a novel utilization of variograms for selecting potentially useful relationship types, whose merits are then evaluated using a Gaussian Conditional Random Field model for node attribute prediction of temporal social networks with a multigraph structure. Our flexible model allows for measuring many kinds of relationships between nodes in the network that evolve over time, as well as using those relationships to augment the outputs of various unstructured predictors to further improve performance. The experimental results exhibit the effectiveness of using particular relationships to boost performance of unstructured predictors, show that using other relationships could actually impede performance, and also indicate that while variograms alone are not necessarily sufficient to identify a useful relationship, they greatly help in removing obviously useless measures, and can be combined with intuition to identify the optimal relationships.

The influence of the feature vector content on the CBIR system efficiency

The influence of the feature vector (FV) content on the CBIR (content-based image retrieval) system efficiency was considered. By using two different FVs and applying three different learning methods, it was shown that the efficiency of retrieving depends on both the FV content and the learning method, independently.