Zeeshan Syed

AAAI Conference 2014 Conference Paper

Evaluating Trauma Patients: Addressing Missing Covariates with Joint Optimization

• Alex Van Esbroeck
• Satinder Singh
• Ilan Rubinfeld
• Zeeshan Syed

Missing values are a common problem when applying classification algorithms to real-world medical data. This is especially true for trauma patients, where the emergent nature of the cases makes it difficult to collect all of the relevant data for each patient. Standard methods for handling missingness first learn a model to estimate missing data values, and subsequently train and evaluate a classifier using data imputed with this model. Recently, several proposed methods have demonstrated the benefits of jointly estimating the imputation model and classifier parameters. However, these methods make assumptions that limit their utility with many real-world medical datasets. For example, the assumption that data elements are missing at random is often invalid. We address this situation by exploring a novel approach for jointly learning the imputation model and classifier. Unlike previous algorithms, our approach makes no assumptions about the missingness of the data, can be used with arbitrary probabilistic data models and classification loss functions, and can be used when both the training and testing data have missing values. We investigate the utility of this approach on the prediction of several patient outcomes in a large national registry of trauma patients, and find that it significantly outperforms standard sequential methods.

AAAI Conference 2014 Conference Paper

Predicting Postoperative Atrial Fibrillation from Independent ECG Components

• Chih-Chun Chia
• James Blum
• Zahi Karam
• Satinder Singh
• Zeeshan Syed

Postoperative atrial fibrillation (PAF) occurs in 10% to 65% of the patients undergoing cardiothoracic surgery. It is associated with increased post-surgical mortality and morbidity, and results in longer and more expensive hospital stays. Accurately stratifying patients for PAF allows for selective use of prophylactic therapies (e. g. , amiodarone). Unfortunately, existing tools to stratify patients for PAF fail to provide clinically adequate discrimination. Our research addresses this situation through the development of novel electrocardiographic (ECG) markers to identify patients at risk of PAF. As a first step, we explore an eigen-decomposition approach that partitions ECG signals into atrial and ventricular components by exploiting knowledge of the underlying cardiac cycle. We then quantify electrical instability in the myocardium manifesting as probabilistic variations in atrial ECG morphology to assess the risk of PAF. When evaluated on 385 patients undergoing cardiac surgery, this approach of stratifying patients for PAF through an analysis of morphologic variability within decoupled atrial ECG demonstrated substantial promise and improved net reclassification by over 53% relative to the use of baseline clinical characteristics.

AAAI Conference 2012 Conference Paper

Heart Rate Topic Models

• Alexander Van Esbroeck
• Chih-Chun Chia
• Zeeshan Syed

A key challenge in reducing the burden of cardiovascular disease is matching patients to treatments that are most appropriate for them. Different cardiac assessment tools have been developed to address this goal. Recent research has focused on heart rate motifs, i. e. , short-term heart rate sequences that are overor under-represented in long-term electrocardiogram (ECG) recordings of patients experiencing cardiovascular outcomes, which provide novel and valuable information for risk stratification. However, this approach can leverage only a small number of motifs for prediction and results in difficult to interpret models. We address these limitations by identifying latent structure in the large numbers of motifs found in long-term ECG recordings. In particular, we explore the application of topic models to heart rate time series to identify functional sets of heart rate sequences and to concisely describe patients using task-independent features for various cardiovascular outcomes. We evaluate the approach on a large collection of real-world ECG data, and investigate the performance of topic mixture features for the prediction of cardiovascular mortality. The topics provided an interpretable representation of the recordings and maintained valuable information for clinical assessment when compared with motif frequencies, even after accounting for commonly used clinical risk scores.

JMLR Journal 2011 Journal Article

Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data

• Zeeshan Syed
• John Guttag

In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two ways. First, we propose similarity-based algorithms that compare patients in terms of their long-term physiological monitoring data. Symbolic mismatch identifies functional units in long-term signals and measures changes in the morphology and frequency of these units across patients. Second, we describe similarity-based algorithms that are unsupervised and do not require comparisons to patients with known outcomes for risk stratification. This is achieved by using an anomaly detection framework to identify patients who are unlike other patients in a population and may potentially be at an elevated risk. We demonstrate the potential utility of our approach by showing how symbolic mismatch-based algorithms can be used to classify patients as being at high or low risk of major adverse cardiac events by comparing their long-term electrocardiograms to that of a large population. We describe how symbolic mismatch can be used in three different existing methods: one-class support vector machines, nearest neighbor analysis, and hierarchical clustering. When evaluated on a population of 686 patients with available long-term electrocardiographic data, symbolic mismatch-based comparative approaches were able to identify patients at roughly a two-fold increased risk of major adverse cardiac events in the 90 days following acute coronary syndrome. These results were consistent even after adjusting for other clinical risk variables. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

NeurIPS Conference 2010 Conference Paper

Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch

• Zeeshan Syed
• John Guttag

Cardiovascular disease is the leading cause of death globally, resulting in 17 million deaths each year. Despite the availability of various treatment options, existing techniques based upon conventional medical knowledge often fail to identify patients who might have benefited from more aggressive therapy. In this paper, we describe and evaluate a novel unsupervised machine learning approach for cardiac risk stratification. The key idea of our approach is to avoid specialized medical knowledge, and assess patient risk using symbolic mismatch, a new metric to assess similarity in long-term time-series activity. We hypothesize that high risk patients can be identified using symbolic mismatch, as individuals in a population with unusual long-term physiological activity. We describe related approaches that build on these ideas to provide improved medical decision making for patients who have recently suffered coronary attacks. We first describe how to compute the symbolic mismatch between pairs of long term electrocardiographic (ECG) signals. This algorithm maps the original signals into a symbolic domain, and provides a quantitative assessment of the difference between these symbolic representations of the original signals. We then show how this measure can be used with each of a one-class SVM, a nearest neighbor classifier, and hierarchical clustering to improve risk stratification. We evaluated our methods on a population of 686 cardiac patients with available long-term electrocardiographic data. In a univariate analysis, all of the methods provided a statistically significant association with the occurrence of a major adverse cardiac event in the next 90 days. In a multivariate analysis that incorporated the most widely used clinical risk variables, the nearest neighbor and hierarchical clustering approaches were able to statistically significantly distinguish patients with a roughly two-fold risk of suffering a major adverse cardiac event in the next 90 days.

ICML Conference 2010 Conference Paper

Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes

• Zeeshan Syed
• Ilan Rubinfeld

JMLR Journal 2009 Journal Article

Learning Approximate Sequential Patterns for Classification

• Zeeshan Syed
• Piotr Indyk
• John Guttag

In this paper, we present an automated approach to discover patterns that can distinguish between sequences belonging to different labeled groups. Our method searches for approximately conserved motifs that occur with varying statistical properties in positive and negative training examples. We propose a two-step process to discover such patterns. Using locality sensitive hashing (LSH), we first estimate the frequency of all subsequences and their approximate matches within a given Hamming radius in labeled examples. The discriminative ability of each pattern is then assessed from the estimated frequencies by concordance and rank sum testing. The use of LSH to identify approximate matches for each candidate pattern helps reduce the runtime of our method. Space requirements are reduced by decomposing the search problem into an iterative method that uses a single LSH table in memory. We propose two further optimizations to the search for discriminative patterns. Clustering with redundancy based on a 2-approximate solution of the k -center problem decreases the number of overlapping approximate groups while providing exhaustive coverage of the search space. Sequential statistical methods allow the search process to use data from only as many training examples as are needed to assess significance. We evaluated our algorithm on data sets from different applications to discover sequential patterns for classification. On nucleotide sequences from the Drosophila genome compared with random background sequences, our method was able to discover approximate binding sites that were preserved upstream of genes. We observed a similar result in experiments on ChIP-on-chip data. For cardiovascular data from patients admitted with acute coronary syndromes, our pattern discovery approach identified approximately conserved sequences of morphology variations that were predictive of future death in a test population. Our data showed that the use of LSH, clustering, and sequential statistics improved the running time of the search algorithm by an order of magnitude without any noticeable effect on accuracy. These results suggest that our methods may allow for an unsupervised approach to efficiently learn interesting dissimilarities between positive and negative examples that may have a functional role. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )