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Evaluating the clinical similarities between pairwisepatients is a fundamental problem in healthcare informatics. Aproper patient similarity measure enables various downstreamapplications, such as cohort study and treatment comparative effectiveness research. One major carrier for conductingpatient similarity research is the Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal,...
Accurate assessment of patients' risk against a certain disease is pivotal to healthcare management and personalized medicine. Although a variety of risk prediction models have been proposed in the literature, these models are mostly single-task, i.e. they only predict the risk of one disease at a time. However, in practice, the risks of multiple related diseases are often studied together. By separately...
Patient risk stratification, which aims to stratify a patient cohort into a set of homogeneous groups according to some risk evaluation criteria, is an important task in modern medical informatics. Good risk stratification is the key to good personalized care plan design and delivery. The typical procedure for risk stratification is to first identify a set of risk-relevant medical features (also called...
Prognosis refers to the prediction of the future health status of a patient. Providing prognostic insight to clinicians is critical for physician decision support. In this paper we present a collaborative disease prognosis strategy leveraging the information of the clinically similar patient cohort, using a Local Spline Regression (LSR) based similarity measure. To improve the reliability of the approach,...
Knowledge discovery in electronic health records (EHRs) is a central aspect for improved clinical decision making, prognosis, and patient management. While EHRs show great promise towards better data integration, automated access, and clinical workflow improvement, the vast information they capture over time pose challenges not only for medical practitioners, but also for the information analysis...
Echocardiography provides important morphological and functional details of the heart which can be used for the diagnosis of various cardiac diseases. Most of the existing automatic cardiac disease recognition systems that use echocardiograms are either based on unreliable anatomical region detection (e.g. left ventricle) or require extensive manual labeling of training data which renders such systems...
With the rise of tools for clinical decision support, there is an increased need for automatic processing of electrocardiograms (ECG) documents. In fact, many systems have already been developed to perform signal processing tasks such as 12-lead off-line ECG analysis and real-time patient monitoring. All these applications require an accurate detection of the heart rate of the ECG. In this paper,...
In this paper, we present an approach to matching heart sounds based on modeling the morphological variations of audio envelopes through a constrained nonrigid translation transform. Similar heart sounds are then retrieved by recovering the corresponding alignment transform using a variant of shape-based dynamic time warping. Results of comparison with other audio retrieval methods are reported on...
The recent development of microarray gene expression techniques have made it possible to offer phenotype classification of many diseases. However, in gene expression data analysis, each sample is represented by quite a large number of genes, and many of them are redundant or insignificant to clarify the disease problem. Therefore, how to efficiently select the most useful genes has been becoming one...
An electrocardiogram (ECG) is an important and commonly used diagnostic aid in cardiovascular disease diagnosis. Physicians routinely perform diagnosis by a simple visual examination of ECG waveform shapes. In this paper, we address the problem of shape-based retrieval of ECG recordings, both digital and scanned from paper, to infer similarity in diagnosed diseases. Specifically, we use the knowledge...
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