Publications

• Michal Valko, Gregory Cooper, Amy Seybert, Shyam Visweswaran, Melissa Saul, Milos Hauskrecht: Conditional anomaly detection methods for patient-management alert systems, Workshop on Machine Learning in Health Care Applications in The 25th International Conference on Machine Learning (ICML-2008 - MLHealth) bibtex abstract

  Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods rely on the distance metric to identify examples in the dataset that are most critical for detecting the anomaly. We investigate various metrics and metric learning methods to optimize the performance of the instance-based anomaly detection methods. We show the benefits of the instance-based methods on two real-world detection problems: detection of unusual admission decisions for patients with the community-acquired pneumonia and detection of unusual orders of an HPF4 test that is used to confirm Heparin induced thrombocytopenia - a life-threatening condition caused by the Heparin therapy.

• Michal Valko, Milos Hauskrecht: Distance metric learning for conditional anomaly detection, Twenty-First International Florida AI Research Society Conference (FLAIRS 2008) bibtex abstract

  Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods depend heavily on the distance metric that lets us identify examples in the dataset that are most critical for detecting the anomaly. To optimize the performance of the anomaly detection methods we explore and study metric learning methods. We evaluate the quality of our methods on the Pneumonia PORT dataset by detecting unusual admission decisions for patients with the community-acquired pneumonia. The results of our metric learning methods show an improved detection performance over standard distance metrics, which is very promising for building automated anomaly detection systems for variety of intelligent monitoring applications.

• Michal Valko, Richard Pelikan, Milos Hauskrecht: Learning predictive models for multiple heterogeneous proteomic datasources, AMIA Summit on Translational Bioinformatics (STB 2008) [paper award] bibtex abstract

  Abstract: Multiple technologies that measure expression levels of protein mixtures in the human body offer a potential for detection and understanding the disease. The recent increase of these technologies prompts researchers to evaluate the individual and combined utility of data generated by the technologies. In this work, we study two data sources to measure the expression of protein mixtures in the human body: whole-sample MS profiling and multiplexed protein arrays. We investigate the individual and combined utility of these technologies by learning and testing a variety of classification models on the data from a pancreatic cancer study. We show that for the combination of these two (heterogeneous) datasets, classification models that work well on one of them individually fail on the combination of the two datasets. We study and propose a class of model fusion methods that acknowledge the differences and try to reap most of the benefits from their combination.

• Wendy W. Chapman, John N. Dowling, Gregory F. Cooper, Milos Hauskrecht, Michal Valko, Will Bridewell: Identifying Acute Lower Respiratory Syndrome from Emergency Department Texts. Journal of American Medical Informatics Association, (JAMIA 2008, submitted)
• Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaram, Gregory Cooper: Evidence-based Anomaly Detection in Clinical Domains in Annual American Medical Informatics Association conference (AMIA 2007). [nominated for the best paper award] bibtex abstract

  Abstract: Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that let us evaluate management decisions for a specific patient and identify those decisions that are highly unusual with respect to patients with the same or similar condition. The statistics used in this detection are derived from probabilistic models such as Bayesian networks that are learned from a database of past patient cases. We evaluate our methods on the problem of detection of unusual hospitalization patterns for patients with community acquired pneumonia. The results show very encouraging detection performance with 0.5 precision at 0.53 recall and give us hope that these techniques may provide the basis of intelligent monitoring systems that alert clinicians to the occurrence of unusual events or decisions.

• Wendy W. Chapman, John N. Dowling, Gregory F. Cooper, Milos Hauskrecht and Michal Valko: A Comparison of Chief Complaints and Emergency Department Reports for Identifying Patients with Acute Lower Respiratory Syndrome in Proceedings of the National Syndromic Surveillance Conference (ISDS 2006) bibtex abstract

  Abstract: Automated syndromic surveillance systems often classify patients into syndromic categories based on free-text chief complaints. Chief complaints (CC) demonstrate low to moderate sensitivity in identify-ing syndromic cases. Emergency Department (ED) reports promise more detailed clinical information that may increase sensitivity of detection.

• Miloš Hauskrecht, Richard Pelikan, Michal Valko, James Lyons-Weiler: Feature Selection and Dimensionality Reduction in Genomics and Proteomics. Fundamentals of Data Mining in Genomics and Proteomics, eds. Berrar, Dubitzky, Granzow. Springer (2006) bibtex abstract

  Abstract: Finding reliable, meaningful patterns in data with high numbers of attributes can be extremely difficult. Feature selection helps us to decide what attributes or combination of attributes are most important for finding these patterns. In this chapter, we study feature selection methods for building classification models from high-throughput genomic (microarray) and proteomic (mass spectrometry) data sets. Thousands of feature candidates must be analyzed, compared and combined in such data sets. We describe the basics of four different approaches used for feature selection and illustrate their effects on an MS cancer proteomic data set. The closing discussion provides assistance in performing an analysis in high-dimensional genomic and proteomic data.

• Michal Valko, Nuno C. Marques, Marco Castelani: Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers in Proceedings of Portuguese Conference on Artificial Intelligence (EPIA 2005), eds. Bento et al., IEEE, pages 24-32. bibtex abstract

  Abstract: This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard multi-layer perceptrons is extended with JASTAP model. Preliminary results on IRIS standard data set give evidence that this extension allows the use of smaller neural networks that can handle noisier data without any degradation in classification accuracy.

• Michal Valko Evolving Neural Networks for Statistical Decision Theory, Comenius University, Bratislava, 2005 defense presentation (master thesis) (2005) Advisor: Radoslav Harman thesis@sk bibtex abstract

  Abstract: Real biological networks are able to make decisions. We will show that this behavior can be observed even in some simple architectures of biologically plausible neural models. The great interest of this thesis is also to contribute to methods of statistical decision theory by giving a lead how to evolve the neural networks to solve miscellaneous decision tasks.

Presentations

• Michal Valko: Conditional anomaly detection with adaptive similarity metric: Presented at CS Department Research Competition (2008) [#1st place]
• Michal Valko, Milos Hauskrecht, G. Cooper, S. Visweswaran, M. Saul, A. Seybert, J. Harrison, A. Post: Conditional Anomaly Detection, Presented at (CS Day 2008), Poster [#1st by people, #2nd by faculty]
• Michal Valko, Milos Hauskrecht, G. Cooper, S. Visweswaran, M. Saul, A. Seybert, J. Harrison, A. Post: Conditional Anomaly Detection in Medical Domains, Presented at University of Pittsburgh, Arts & Sciences (Grad Expo 2008), Poster

Research Projects

• Branislav Kveton, Michal Valko
  Online semi-supervised learning 2009 - present
  
  Extended graph-based semi-supervised learning to the structured case and demonstrated on handwriting recognition and object detection from video streams. Regularized harmonic function solution: The algorithm outputs a confidence of inference and uses it for learning.
• Miloš Hauskrecht, Michal Valko
  Anomaly detection in clinical databasess 2007 - present
  
  Statistical anomaly detection methods for identification of unusual outcomes and patient management decisions. I combined max-margin learning with distance learned to create and anomaly detector, which outperforms the hospital rule for Heparin Induced Thrombocytopenia detection.
• Miloš Hauskrecht, Richard Pelikan, Michal Valko, Shuguang Wang
  High--throughput proteomic and genomic data and biomarker discovery 2006 - 2007
  
  Tools for analysis of high-throughput proteomic and genomic data sources. I found a way to merge heterogeneous data sources for mass spectrometry (Luminex and SELDI) and build a framework for the cancer prediction.
• Nuno Miguel Cavalheiro Marques, Marco Castelani, Michal Valko
  Evolutionary feature selection algorithms for pattern recognition 2005
  
  I enhanced FeaSANNT selection with spiking neuron model and show that it can handle noised inputs.
  
• Juraj Pavlásek, Radoslav Harman, Ján Jenča, Michal Valko: Plastic Synapses (regularity counting) 2003 - 2005
  
  Modelling basic learning function at the level of synapses. Design of a model that is able to adapt to the regular frequencies with different a rate as the time flows. I used genetic programming to find biologically plausible networks that distinguish different gamma distribution and provided explanation of the strategies evolved.