Iyad Batal

A temporal pattern mining approach for classifying electronic health record data

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and nonspurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin-induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.

A Pattern Mining Approach for Classifying Multivariate Temporal Data

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the minimal predictive temporal patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.

Outlier detection for patient monitoring and alerting

We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25% to 66% for a variety of patient-management actions, with 66% corresponding to the strongest outliers.

A Supervised Time Series Feature Extraction Technique Using DCT and DWT

The increased availability of time series datasets prompts the development of new tools and methods that allow machine learning classifiers to better cope with time series data. Time series data are usually characterized by a high space dimensionality and a very strong correlation among features. This special nature makes the development of effective time series classifiers a challenging task. This work proposes and analyzes methods combining spectral decomposition and feature selection for time series classification problems and compares them against methods that work with original time series and time-dependent features. Briefly, our approach first applies discrete cosine transform (DCT) or discrete wavelet transform (DWT) on time series data. Then, it performs supervised feature selection/reduction by selecting only the most discriminative set of coefficients to represent the data. Experimental evaluations, carried out on multiple datasets, demonstrate the benefits of our approach in learning efficient and accurate time series classifiers.

Boosting KNN text classification accuracy by using supervised term weighting schemes

The increasing availability of digital documents in the last decade has prompted the development of machine learning techniques to automatically classify and organize text documents. The majority of text classification systems rely on the vector space model, which represents the documents as vectors in the term space. Each vector component is assigned a weight that reflects the importance of the term in the document. Typically, these weights are assigned using an information retrieval (IR) approach, such as the famous tf-idf function. In this work, we study two weighting schemes based on information gain and chi-square statistics. These schemes take advantage of the category label information to weight the terms according to their distributions across the different categories. We show that using these supervised weights instead of conventional unsupervised weights can greatly improve the performance of the k-nearest neighbor (KNN) classifier. Experimental evaluations, carried out on multiple text classification tasks, demonstrate the benefits of this approach in creating accurate text classifiers.

Constructing classification features using minimal predictive patterns

Choosing good features to represent objects can be crucial to the success of supervised machine learning methods. Recently, there has been a great interest in applying data mining techniques to construct new classification features. The rationale behind this approach is that patterns (feature-value combinations) could capture more underlying semantics than single features. Hence the inclusion of some patterns can improve the classification performance. Currently, most methods adopt a two-phases approach by generating all frequent patterns in the first phase and selecting the discriminative patterns in the second phase. However, this approach has limited success because it is usually very difficult to correctly identify important predictive patterns in a large set of highly correlated frequent patterns. In this paper, we introduce the minimal predictive patterns framework to directly mine a compact set of highly predictive patterns. The idea is to integrate pattern mining and feature selection in order to filter out non-informative and redundant patterns while being generated. We propose some pruning techniques to speed up the mining process. Our extensive experimental evaluation on many datasets demonstrates the advantage of our method by outperforming many well known classifiers.

An efficient pattern mining approach for event detection in multivariate temporal data

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present recent temporal pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the minimal predictive recent temporal patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems.

A Mixtures-of-Trees Framework for Multi-Label Classification

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods.

A Generalized Mixture Framework for Multi-label Classification

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd |X) using a product of posterior distributions over components of the output space. Our approach captures different input-output and output-output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.

An efficient probabilistic framework for multi-dimensional classification

The objective of multi-dimensional classification is to learn a function that accurately maps each data instance to a vector of class labels. Multi-dimensional classification appears in a wide range of applications including text categorization, gene functionality classification, semantic image labeling, etc. Usually, in such problems, the class variables are not independent, but rather exhibit conditional dependence relations among them. Hence, the key to the success of multi-dimensional classification is to effectively model such dependencies and use them to facilitate the learning. In this paper, we propose a new probabilistic approach that represents class conditional dependencies in an effective yet computationally efficient way. Our approach uses a special tree-structured Bayesian network model to represent the conditional joint distribution of the class variables given the feature variables. We develop and present efficient algorithms for learning the model from data and for performing exact probabilistic inferences on the model. Extensive experiments on multiple datasets demonstrate that our approach achieves highly competitive results when it is compared to existing state-of-the-art methods.